{"id":10145332,"date":"2024-08-27T12:53:14","date_gmt":"2024-08-27T17:53:14","guid":{"rendered":"https:\/\/www.erbessd-instruments.com\/?p=10145332"},"modified":"2025-06-16T15:34:24","modified_gmt":"2025-06-16T20:34:24","slug":"is-your-vibration-diagnostic-system-a-black-box-or-a-white-box","status":"publish","type":"post","link":"https:\/\/www.erbessd-instruments.com\/de\/articles\/is-your-vibration-diagnostic-system-a-black-box-or-a-white-box\/","title":{"rendered":"Is Your Vibration Diagnostic System a Black Box or a White Box?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"10145332\" class=\"elementor elementor-10145332\" data-elementor-post-type=\"post\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-d89b7ba e-flex e-con-boxed e-con e-parent\" data-id=\"d89b7ba\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f996569 elementor-widget elementor-widget-heading\" data-id=\"f996569\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Is Your Vibration Diagnostic System a Black Box or a White Box? - The Case for Hybrid Rule-Based and Machine Learning Approaches<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-7301023 e-flex e-con-boxed e-con e-parent\" data-id=\"7301023\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8621e4f elementor-widget elementor-widget-heading\" data-id=\"8621e4f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Green Doctor Technique<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-964a586 elementor-widget elementor-widget-text-editor\" data-id=\"964a586\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>When it comes to vibration diagnostics, do you know how your system makes its decisions? Is it a black box\u2014opaque and mysterious, where the reasoning behind its diagnoses is hidden? Or is it a white box\u2014transparent, with every decision backed by clear, understandable logic? As industries increasingly rely on automated systems for predictive maintenance, understanding how these systems work is crucial not just for trust but also for ensuring the accuracy and reliability of fault detection.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-eb1deba e-flex e-con-boxed e-con e-parent\" data-id=\"eb1deba\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4c7c420 elementor-widget elementor-widget-heading\" data-id=\"4c7c420\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Machine Learning or Rules Based Diagnostics?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-6507c79 e-flex e-con-boxed e-con e-parent\" data-id=\"6507c79\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-02824b6 elementor-widget elementor-widget-text-editor\" data-id=\"02824b6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Another essential aspect of reliable vibration diagnostics is the integration of rule-based analysis with machine learning. While machine learning offers the ability to detect patterns across vast datasets, rule-based systems bring decades of expert knowledge into the equation. These rules, refined over years of study and practice, provide a robust and interpretable foundation for diagnostics.<\/p><p>In this article, we\u2019ll explore how combining these two approaches\u2014leveraging the precision of rule-based analysis with the adaptability of machine learning\u2014can create a powerful, transparent, and trustworthy vibration diagnostic tool.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-bd10ecd e-flex e-con-boxed e-con e-parent\" data-id=\"bd10ecd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-03634f0 elementor-widget elementor-widget-heading\" data-id=\"03634f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Understanding Rule-Based Analysis in Vibration Diagnostics and the Green Doctor Technique<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-0f856ab e-flex e-con-boxed e-con e-parent\" data-id=\"0f856ab\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-33b0491 elementor-widget elementor-widget-heading\" data-id=\"33b0491\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is Rule-Based Analysis?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-3fa117a e-flex e-con-boxed e-con e-parent\" data-id=\"3fa117a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-832ae31 elementor-widget elementor-widget-text-editor\" data-id=\"832ae31\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Rule-based analysis is a diagnostic approach that uses predefined rules, derived from expert knowledge, to interpret vibration data and diagnose faults. Experienced vibration analysts craft these rules, which correspond to specific fault conditions like misalignment, imbalance, bearing defects, or looseness. The rules use input data from vibration sensors to assess the likelihood of these faults.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-ca31c37 e-flex e-con-boxed e-con e-parent\" data-id=\"ca31c37\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-64836c7 elementor-widget elementor-widget-heading\" data-id=\"64836c7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How Does Rule-Based Analysis Work?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-739297d e-flex e-con-boxed e-con e-parent\" data-id=\"739297d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b883fdd elementor-widget elementor-widget-text-editor\" data-id=\"b883fdd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The process begins by collecting data from vibration sensors, typically in the form of time waveform (TWF) and Fast Fourier Transform (FFT) spectra. Key features such as overall root mean square (RMS) values, 1X amplitude, harmonics, crest factor, and phase information are extracted. The rules are then applied to these features to calculate the probability of specific faults.<\/p><p>A significant advantage of rule-based analysis is its transparency. Unlike machine learning models, which can be difficult to interpret, rule-based systems offer clear, understandable reasoning behind each diagnosis. This is especially important in industrial settings where understanding the rationale for a decision is as crucial as the decision itself.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-98fd845 e-flex e-con-boxed e-con e-parent\" data-id=\"98fd845\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-580b8bf elementor-widget elementor-widget-heading\" data-id=\"580b8bf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The Importance of Recording Phase Between Sensors<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-87db2c9 e-flex e-con-boxed e-con e-parent\" data-id=\"87db2c9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5b21c65 elementor-widget elementor-widget-text-editor\" data-id=\"5b21c65\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A standout feature of advanced rule-based systems is the ability to record and analyze phase information between sensors. Phase analysis is crucial in vibration diagnostics because it provides insights into the relationship between different parts of the machinery. For example, in cases of misalignment, the phase difference between vibration signals from different sensors can indicate the relative movement of components, helping to pinpoint the exact nature and location of the fault.<\/p><p>Moreover, phase analysis between two different accelerometers can reveal complex issues that might be missed by simpler amplitude-based measurements. Certain types of imbalance or misalignment produce specific phase relationships that are crucial for accurate diagnosis. Incorporating phase data into the diagnostic rules elevates the precision and reliability of fault detection.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-16600dc e-flex e-con-boxed e-con e-parent\" data-id=\"16600dc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-96b94ca elementor-widget elementor-widget-heading\" data-id=\"96b94ca\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Positive Predictive Value (PPV) and Negative Predictive Value (NPV)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-2939679 e-flex e-con-boxed e-con e-parent\" data-id=\"2939679\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d74bc40 elementor-widget elementor-widget-text-editor\" data-id=\"d74bc40\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In rule-based systems, not all diagnostic rules are equal. Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are used to quantify the impact of each rule. These values represent the likelihood that a rule correctly identifies a fault when it is present (PPV) or correctly identifies the absence of a fault when it is not present (NPV).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-bdadefc e-flex e-con-boxed e-con e-parent\" data-id=\"bdadefc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ad1f7ec elementor-widget elementor-widget-heading\" data-id=\"ad1f7ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">A Medical Example: Diagnosing a Fracture<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-65e52be e-flex e-con-boxed e-con e-parent\" data-id=\"65e52be\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f8b34e5 elementor-widget elementor-widget-text-editor\" data-id=\"f8b34e5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To illustrate PPV and NPV, imagine training a computer to diagnose a bone fracture. The rules might include:<\/p><ul><li>Pain in the region of the injury<\/li><li>Loss of mobility<\/li><li>Deformity of the arm or leg<\/li><li>X-ray for confirmation<\/li><\/ul><p>These rules are straightforward. Focusing on the first rule,\u00a0<strong>pain in the region<\/strong>:<\/p><ul><li><strong>PPV:<\/strong>\u00a0Pain could indicate a fracture but might also result from a sprain or bruise. Thus, the PPV for this rule would be relatively low, as pain alone does not strongly predict a fracture.<\/li><li><strong>NPV:<\/strong>\u00a0The absence of pain, however, strongly suggests the absence of a fracture, making the NPV for this rule quite high.<\/li><\/ul><p>This example highlights how PPV and NPV assess the reliability of different diagnostic indicators in rule-based systems, including those used in vibration diagnostics.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-97143d3 e-flex e-con-boxed e-con e-parent\" data-id=\"97143d3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-edc4882 elementor-widget elementor-widget-heading\" data-id=\"edc4882\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">From Medical Diagnostics to Vibration Analysis: Real-World Examples<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-01883b0 e-flex e-con-boxed e-con e-parent\" data-id=\"01883b0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-210fe86 elementor-widget elementor-widget-text-editor\" data-id=\"210fe86\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>While concepts like Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are crucial in fields like medicine, they are just as important in vibration diagnostics. Let\u2019s move from the medical analogy to explore two real-world examples of diagnosing mechanical faults: static imbalance and parallel misalignment. These examples demonstrate how rule-based analysis, coupled with phase measurement, can lead to accurate diagnoses, even when vibration patterns appear similar.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-a698665 e-flex e-con-boxed e-con e-parent\" data-id=\"a698665\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-af7fbdb elementor-widget elementor-widget-heading\" data-id=\"af7fbdb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Example 1: Static Imbalance Diagnosis<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-9a4dd3f e-flex e-con-boxed e-con e-parent\" data-id=\"9a4dd3f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-ce00d53 e-con-full e-flex e-con e-child\" data-id=\"ce00d53\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-57647bc elementor-widget elementor-widget-text-editor\" data-id=\"57647bc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In one case, we identified a static imbalance in a large industrial fan. The rules used were:<\/p><ul><li><strong>RMS &gt; reference value<\/strong><\/li><li><strong>1X &gt; reference value<\/strong><\/li><li><strong>2X &lt; 1X<\/strong><\/li><li><strong>Vibration higher on fan side<\/strong><\/li><li><strong>Phase between points close to 0\u00b0<\/strong><\/li><\/ul><p>Using ODS (Operating Deflection Shape) analysis, we visualized the vibration motion, which, along with the FFT showing a high 1X amplitude, pointed to an imbalance. The phase measurement confirmed the diagnosis by showing a 0\u00b0 phase difference, typical of static imbalance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-1f01bb3 e-con-full e-flex e-con e-child\" data-id=\"1f01bb3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b752ac9 elementor-widget elementor-widget-image\" data-id=\"b752ac9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"898\" height=\"585\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/imbalance_on_a_diagnostic_system.gif\" class=\"attachment-large size-large wp-image-10145342\" alt=\"imbalance on a diagnosys system\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-013070f elementor-widget elementor-widget-image\" data-id=\"013070f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"330\" height=\"186\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/imbalance_diagnosys_fft.png\" class=\"attachment-large size-large wp-image-10145369\" alt=\"imbalance diagnosys fft\" srcset=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/imbalance_diagnosys_fft.png 330w, https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/imbalance_diagnosys_fft-300x169.png 300w\" sizes=\"(max-width: 330px) 100vw, 330px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-e45f4d0 e-flex e-con-boxed e-con e-parent\" data-id=\"e45f4d0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-71cd3e1 elementor-widget elementor-widget-heading\" data-id=\"71cd3e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Example 2: Parallel Misalignment Diagnosis<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-5db2c84 e-flex e-con-boxed e-con e-parent\" data-id=\"5db2c84\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-31c34dc e-con-full e-flex e-con e-child\" data-id=\"31c34dc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4ca0d44 elementor-widget elementor-widget-image\" data-id=\"4ca0d44\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"898\" height=\"585\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/parallel_misalignment_on_a_diagnosys_system.gif\" class=\"attachment-large size-large wp-image-10145355\" alt=\"parallel misalignment on a diagnosys system\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9a342f7 elementor-widget elementor-widget-image\" data-id=\"9a342f7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"328\" height=\"192\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/misalignment_diagnosys_fft.png\" class=\"attachment-large size-large wp-image-10145373\" alt=\"parallel misalignment fft\" srcset=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/misalignment_diagnosys_fft.png 328w, https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/misalignment_diagnosys_fft-300x176.png 300w\" sizes=\"(max-width: 328px) 100vw, 328px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-60a6229 e-con-full e-flex e-con e-child\" data-id=\"60a6229\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1c81186 elementor-widget elementor-widget-text-editor\" data-id=\"1c81186\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In another case, we diagnosed parallel misalignment. The rules were:<\/p><ul><li><strong>RMS &gt; reference value<\/strong><\/li><li><strong>1X &gt; reference value<\/strong><\/li><li><strong>2X &lt; 75% of 1X<\/strong>\u00a0(This rule did not meet in this case)<\/li><li><strong>Max vibration on coupling side<\/strong><\/li><li><strong>Phase between points 180\u00b0<\/strong><\/li><\/ul><p>Although the FFT data resembled that of the imbalance case in point 2 of the motor, the key difference was the phase measurement. The 180\u00b0 phase difference between points confirmed the misalignment.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-d3707dd e-flex e-con-boxed e-con e-parent\" data-id=\"d3707dd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cb6311a elementor-widget elementor-widget-heading\" data-id=\"cb6311a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Importance of Phase in Accurate Diagnostics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-be44a40 e-flex e-con-boxed e-con e-parent\" data-id=\"be44a40\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e8a279 elementor-widget elementor-widget-text-editor\" data-id=\"1e8a279\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>These examples show that while FFT data provides valuable insights, phase measurement is often the critical factor in distinguishing between similar faults. By combining rule-based analysis with phase data and ODS visualization, we can achieve precise and reliable diagnoses, preventing costly misinterpretations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-ca510c7 e-flex e-con-boxed e-con e-parent\" data-id=\"ca510c7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c27ba7e elementor-widget elementor-widget-heading\" data-id=\"c27ba7e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Understanding the Rule Editor for Diagnosing Static Imbalance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-fffc1be e-flex e-con-boxed e-con e-parent\" data-id=\"fffc1be\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6a09681 elementor-widget elementor-widget-text-editor\" data-id=\"6a09681\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The rule editor interface you use allows you to set up diagnostic rules, such as for detecting static imbalance. Here\u2019s a brief breakdown:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-a535119 e-flex e-con-boxed e-con e-parent\" data-id=\"a535119\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-0ecf108 e-con-full e-flex e-con e-child\" data-id=\"0ecf108\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-42829e6 elementor-widget elementor-widget-image\" data-id=\"42829e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"589\" height=\"374\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/rule_editor_for_diagnosing_failure.png\" class=\"attachment-large size-large wp-image-10145359\" alt=\"Rule editor for diagnostic tool\" srcset=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/rule_editor_for_diagnosing_failure.png 589w, https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/rule_editor_for_diagnosing_failure-300x190.png 300w\" sizes=\"(max-width: 589px) 100vw, 589px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-f760694 e-con-full e-flex e-con e-child\" data-id=\"f760694\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-92f6d9e elementor-widget elementor-widget-text-editor\" data-id=\"92f6d9e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><strong>Rule Name and ID:<\/strong>\u00a0The rule is titled &#8220;1X amplitude,&#8221; focusing on the amplitude at the 1X frequency, corresponding to the machine\u2019s rotational speed.<\/li><li><strong>Predictive Values:<\/strong><ul><li><strong>PPV (2.0):<\/strong>\u00a0Indicates that if the rule is met (1X amplitude is high), it moderately supports the likelihood of a static imbalance.<\/li><li><strong>NPV (10.0):<\/strong>\u00a0A high value suggesting that if the rule isn\u2019t met (1X amplitude is low), static imbalance is very unlikely.<\/li><\/ul><\/li><li><strong>Value A and Value B:<\/strong><ul><li><strong>Location &amp; Axis:<\/strong>\u00a0Both are set to &#8220;main&#8221; and &#8220;H&#8221; (horizontal axis), referring to where the vibration is measured.<\/li><li><strong>Units:<\/strong>\u00a0Velocity in mm\/s.<\/li><li><strong>Value Type:<\/strong><ul><li><strong>Value A:<\/strong>\u00a0RMS amplitude within the 1X order (rotational frequency).<\/li><li><strong>Value B:<\/strong>\u00a0A threshold, likely a cautionary level set to &#8220;yellow.&#8221;<\/li><\/ul><\/li><\/ul><\/li><li><strong>Operator and Factor %:<\/strong>\u00a0The rule triggers if Value A is \u2265 75% of Value B.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-9589fa1 e-flex e-con-boxed e-con e-parent\" data-id=\"9589fa1\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8c13be6 elementor-widget elementor-widget-heading\" data-id=\"8c13be6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How It Works<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-1e69c39 e-flex e-con-boxed e-con e-parent\" data-id=\"1e69c39\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-11fc7c9 elementor-widget elementor-widget-text-editor\" data-id=\"11fc7c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This rule checks if the 1X amplitude (a key indicator of static imbalance) exceeds a certain threshold. If it does, the system considers an imbalance moderately likely (PPV of 2.0). However, if the 1X amplitude is low, the system is confident (NPV of 10.0) that no imbalance exists. This ensures accurate diagnoses by strongly ruling out faults when critical indicators are absent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-71acbfe e-flex e-con-boxed e-con e-parent\" data-id=\"71acbfe\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c29e639 elementor-widget elementor-widget-heading\" data-id=\"c29e639\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Comparison with Machine Learning Models<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-3ae358a e-flex e-con-boxed e-con e-parent\" data-id=\"3ae358a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7d46e58 elementor-widget elementor-widget-heading\" data-id=\"7d46e58\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Generalization vs. Specialization<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-c202167 e-flex e-con-boxed e-con e-parent\" data-id=\"c202167\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dce2570 elementor-widget elementor-widget-text-editor\" data-id=\"dce2570\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Machine learning models excel at generalization, learning patterns across diverse datasets and adapting to new scenarios. However, this can also be a limitation, as ML models may overlook the nuances of specific machines or operating conditions. Rule-based systems, on the other hand, excel in specialization, offering superior accuracy and reliability within well-defined domains.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-86798ba e-flex e-con-boxed e-con e-parent\" data-id=\"86798ba\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e45938f elementor-widget elementor-widget-heading\" data-id=\"e45938f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Transparency and Interpretability<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-385b2bd e-flex e-con-boxed e-con e-parent\" data-id=\"385b2bd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-79ffbdd elementor-widget elementor-widget-text-editor\" data-id=\"79ffbdd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Rule-based systems are inherently transparent, with each decision\u2019s logic clear and understandable. This is critical for safety and reliability in industrial environments. In contrast, ML models, especially deep learning ones, can often act as black boxes\u2014producing accurate results without clear reasoning, which can be a barrier to trust.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-e55ab8b e-flex e-con-boxed e-con e-parent\" data-id=\"e55ab8b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ad290f0 elementor-widget elementor-widget-heading\" data-id=\"ad290f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Adaptability and Scalability<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-7d02054 e-flex e-con-boxed e-con e-parent\" data-id=\"7d02054\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-560c2fa elementor-widget elementor-widget-text-editor\" data-id=\"560c2fa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>ML models are more adaptable and scalable, applicable across various machinery and conditions without extensive reconfiguration. Rule-based systems require careful tuning and updating but provide unmatched precision in specific contexts.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-a95e7c5 e-flex e-con-boxed e-con e-parent\" data-id=\"a95e7c5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-da134c2 elementor-widget elementor-widget-heading\" data-id=\"da134c2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Hybrid Approach: Combining the Best of Both Worlds<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-edbfbc4 e-flex e-con-boxed e-con e-parent\" data-id=\"edbfbc4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ab2da3f elementor-widget elementor-widget-text-editor\" data-id=\"ab2da3f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In a hybrid model, rule-based analysis provides the foundational framework, built on expert knowledge and specialized rules proven effective in identifying faults. The system uses these rules to analyze vibration data, incorporating advanced features like phase analysis and probabilistic weighting with PPV and NPV.<\/p><p>Simultaneously, machine learning models enhance the system by recognizing patterns that may not be explicitly covered by the rule-based framework. When the system encounters a scenario outside predefined rules, the ML model steps in to provide a generalized diagnosis based on learned patterns.<\/p><p>This combination allows the hybrid system to maintain the precision and transparency of rule-based analysis while also benefiting from the adaptability and scalability of machine learning. The result is a diagnostic tool that is reliable, versatile, and accurate across a wide range of conditions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-b9e6f58 e-flex e-con-boxed e-con e-parent\" data-id=\"b9e6f58\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a828ef7 elementor-widget elementor-widget-heading\" data-id=\"a828ef7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Benefits of the Hybrid Approach<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-0e58261 e-flex e-con-boxed e-con e-parent\" data-id=\"0e58261\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c70aca9 elementor-widget elementor-widget-text-editor\" data-id=\"c70aca9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><strong>Enhanced Accuracy:<\/strong>\u00a0The hybrid system achieves higher diagnostic accuracy by combining rule-based precision with ML\u2019s adaptability.<\/li><li><strong>Improved Trust:<\/strong>\u00a0The transparency of rule-based analysis, combined with ML\u2019s pattern recognition, builds trust in the system\u2019s diagnoses.<\/li><li><strong>Scalability and Flexibility:<\/strong>\u00a0The ML component allows the hybrid model to adapt to new data and conditions without constant rule updates, making it suitable for diverse industrial environments.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-ebb369b e-flex e-con-boxed e-con e-parent\" data-id=\"ebb369b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-296a73c elementor-widget elementor-widget-heading\" data-id=\"296a73c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-ad00425 e-flex e-con-boxed e-con e-parent\" data-id=\"ad00425\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-06711fe elementor-widget elementor-widget-text-editor\" data-id=\"06711fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In the evolving landscape of vibration diagnostics, the debate between rule-based analysis and machine learning is shifting towards how to effectively combine them. Rule-based systems offer transparency, precision, and the benefit of decades of expert knowledge, while machine learning models provide adaptability and the ability to generalize across various conditions.<\/p><p>By integrating these two approaches we create The Green Doctor technique, a hybrid model that delivers a diagnostic framework that is both reliable and interpretable, incorporating advanced capabilities of machine learning to handle a broader range of scenarios. As the industry continues to evolve, such hybrid systems are likely to become the gold standard in vibration diagnostics, offering enhanced accuracy, trust, and flexibility in predictive maintenance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-22c9b48 e-flex e-con-boxed e-con e-parent\" data-id=\"22c9b48\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-2120d48 e-flex e-con-boxed e-con e-child\" data-id=\"2120d48\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-01294c0 elementor-widget-divider--view-line_text elementor-widget-divider--element-align-center elementor-widget elementor-widget-divider\" data-id=\"01294c0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t\t<span class=\"elementor-divider__text elementor-divider__element\">\n\t\t\t\t<b>About the author<\/b>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-bcf26d3 e-flex e-con-boxed e-con e-child\" data-id=\"bcf26d3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-6e5685a e-flex e-con-boxed e-con e-child\" data-id=\"6e5685a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7547bb4 elementor-widget elementor-widget-text-editor\" data-id=\"7547bb4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Dr. Thierry Erbessd<\/strong>, a prominent Mexican entrepreneur, and graduate of the National Polytechnic Institute has revolutionized the field of Vibration Analysis, Dynamic Balancing, and Condition Monitoring. Through his innovative software DigivibeMX\u00ae, DragonVision\u00ae, and WiSER Vibe\u00ae, he has set a before and after in the industry. As president of Erbessd Instruments\u00ae, he has not only led the company to the top of the global industrial maintenance industry but has also expanded its influence with strategically located offices in America, Europe, and Asia, establishing himself as an undisputed reference in industrial maintenance solutions worldwide.<\/p><p>ERBESSD INSTRUMENTS\u00ae is a leading manufacturer of Vibration Analysis Equipment, Dynamic Balancing Machines, and Condition Monitoring with facilities in Mexico, the USA, England, and India<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-d3559b9 e-con-full e-flex e-con e-child\" data-id=\"d3559b9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-808caa4 elementor-widget elementor-widget-image\" data-id=\"808caa4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"923\" height=\"951\" src=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/03\/thierry_erbessd_profile.png\" class=\"attachment-large size-large wp-image-10141478\" alt=\"\" srcset=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/03\/thierry_erbessd_profile.png 923w, https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/03\/thierry_erbessd_profile-291x300.png 291w, https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/03\/thierry_erbessd_profile-768x791.png 768w\" sizes=\"(max-width: 923px) 100vw, 923px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-7dfe785 e-flex e-con-boxed e-con e-child\" data-id=\"7dfe785\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Is Your Vibration Diagnostic System a Black Box or a White Box? &#8211; The Case for Hybrid Rule-Based and Machine Learning Approaches The Green Doctor Technique When it comes to vibration diagnostics, do you know how your system makes its decisions? Is it a black box\u2014opaque and mysterious, where the reasoning behind its diagnoses is &#8230; <a title=\"Is Your Vibration Diagnostic System a Black Box or a White Box?\" class=\"read-more\" href=\"https:\/\/www.erbessd-instruments.com\/de\/articles\/is-your-vibration-diagnostic-system-a-black-box-or-a-white-box\/\" aria-label=\"Mehr Informationen \u00fcber Is Your Vibration Diagnostic System a Black Box or a White Box?\">Weiterlesen<\/a><\/p>\n","protected":false},"author":3,"featured_media":10145357,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-10145332","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.8 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Is Your Vibration Diagnostic System a Black Box or a White Box? &#8211; ERBESSD INSTRUMENTS<\/title>\n<meta name=\"description\" content=\"Is Your Vibration Diagnostic System a Black Box or a White Box? - The Case for Hybrid Rule-Based and Machine Learning Approaches The Green Doctor &#8211; ERBESSD INSTRUMENTS\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.erbessd-instruments.com\/articles\/is-your-vibration-diagnostic-system-a-black-box-or-a-white-box\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Is Your Vibration Diagnostic System a Black Box or a White Box?\" \/>\n<meta property=\"og:description\" content=\"Is Your Vibration Diagnostic System a Black Box or a White Box? - The Case for Hybrid Rule-Based and Machine Learning Approaches The Green Doctor &#8211; ERBESSD INSTRUMENTS\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.erbessd-instruments.com\/articles\/is-your-vibration-diagnostic-system-a-black-box-or-a-white-box\/\" \/>\n<meta property=\"og:site_name\" content=\"ERBESSD INSTRUMENTS\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/erbessdinstrumentsco\" \/>\n<meta property=\"article:published_time\" content=\"2024-08-27T17:53:14+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-16T20:34:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.erbessd-instruments.com\/wp-content\/uploads\/2024\/08\/rule_editor_for_diagnosing_failure.png\" \/>\n\t<meta property=\"og:image:width\" content=\"589\" \/>\n\t<meta property=\"og:image:height\" content=\"374\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Thierry Erbessd\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@ErbessdRel\" \/>\n<meta name=\"twitter:site\" content=\"@ErbessdRel\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Thierry Erbessd\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"10\u00a0Minuten\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Is Your Vibration Diagnostic System a Black Box or a White Box? &#8211; 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