{"id":3926,"date":"2024-06-19T11:15:49","date_gmt":"2024-06-19T03:15:49","guid":{"rendered":"https:\/\/nullthought.net\/?p=3926"},"modified":"2025-09-11T13:05:18","modified_gmt":"2025-09-11T05:05:18","slug":"%e6%98%be%e7%a4%ba%e6%8c%af%e5%8a%a8%e4%bf%a1%e5%8f%b7%e4%b8%8d%e5%90%8c%e7%bb%9f%e8%ae%a1%e7%89%b9%e5%be%81%e7%9a%84%e4%b8%80%e5%bc%a0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/nullthought.net\/?p=3926","title":{"rendered":"\u663e\u793a\u632f\u52a8\u4fe1\u53f7\u4e0d\u540c\u7edf\u8ba1\u7279\u5f81\u7684\u4e00\u5f20\u56fe"},"content":{"rendered":"\n<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"573\" src=\"https:\/\/nullthought.net\/wp-content\/uploads\/2024\/06\/The-histograms-provide-a-clear-visual-representation-of-how-different-statistical-features-of-the-vibration-signal-differ-1024x573.jpg\" alt=\"\" class=\"wp-image-3927\" srcset=\"https:\/\/nullthought.net\/wp-content\/uploads\/2024\/06\/The-histograms-provide-a-clear-visual-representation-of-how-different-statistical-features-of-the-vibration-signal-differ-1024x573.jpg 1024w, https:\/\/nullthought.net\/wp-content\/uploads\/2024\/06\/The-histograms-provide-a-clear-visual-representation-of-how-different-statistical-features-of-the-vibration-signal-differ-300x168.jpg 300w, https:\/\/nullthought.net\/wp-content\/uploads\/2024\/06\/The-histograms-provide-a-clear-visual-representation-of-how-different-statistical-features-of-the-vibration-signal-differ-768x430.jpg 768w, https:\/\/nullthought.net\/wp-content\/uploads\/2024\/06\/The-histograms-provide-a-clear-visual-representation-of-how-different-statistical-features-of-the-vibration-signal-differ.jpg 1180w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">The histograms provide a clear visual representation of how different statistical features of the vibration signal differ based on the presence (faultCode 1) or absence (faultCode 0) of a fault.<br>Source: <a href=\"https:\/\/www.mathworks.com\/company\/mathworks-stories\/model-based-design-and-code-generation-embed-ai-powered-predictive-maintenance-algorithms-in-electrical-systems.html?s_eid=psm_brj&amp;source=15308\" target=\"_blank\" rel=\"noreferrer noopener\">Designing Smarter Electrical Equipment Embedded with AI<\/a>, Mathworks<\/figcaption><\/figure>\n\n\n\n<p>\u8fd9\u5f20\u56fe\u7247\u5c55\u793a\u4e86\u4e00\u4e2a\u7528\u4e8e\u5206\u6790\u632f\u52a8\u6570\u636e\u7684\u7279\u5f81\u8bbe\u8ba1\u5de5\u5177\u754c\u9762\uff0c\u5e26\u6709\u663e\u793a\u4ece\u632f\u52a8\u4fe1\u53f7\u4e2d\u63d0\u53d6\u7684\u5404\u79cd\u7edf\u8ba1\u7279\u5f81\u7684\u76f4\u65b9\u56fe\u3002\u4ee5\u4e0b\u662f\u8be5\u56fe\u50cf\u7684\u8be6\u7ec6\u6280\u672f\u5206\u6790\uff1a<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u754c\u9762\u6982\u89c8<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u7279\u5f81\u8bbe\u8ba1\u5de5\u5177\uff1a<\/strong> \u8fd9\u53ef\u80fd\u662f\u4e00\u4e2a\u7528\u4e8e\u4fe1\u53f7\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u7684\u8f6f\u4ef6\u5957\u4ef6\u7684\u4e00\u90e8\u5206\uff0c\u65e8\u5728\u4ece\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4e2d\u63d0\u53d6\u7279\u5f81\u3002\uff08<strong>\u6b63\u786e\uff0c\u662fMatlab<\/strong>\uff09<\/li>\n\n\n\n<li><strong>\u5206\u7ec4\u548c\u5206\u7bb1\u63a7\u5236\uff1a<\/strong> \u5728\u9876\u90e8\uff0c\u6709\u6309 <code>faultCode<\/code> \u5206\u7ec4\u3001\u8bbe\u7f6e\u7bb1\u5bbd\u3001\u7bb1\u9650\u5236\u548c\u5f52\u4e00\u5316\u65b9\u6cd5\u7684\u9009\u9879\u3002\u8fd9\u8868\u660e\u53ef\u4ee5\u7075\u6d3b\u5730\u81ea\u5b9a\u4e49\u6570\u636e\u7684\u5206\u7ec4\u548c\u53ef\u89c6\u5316\u65b9\u5f0f\u3002<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">\u53d8\u91cf\u7a97\u683c<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u5f53\u524d\u5e27\u7b56\u7565\uff1a<\/strong> \u5168\u4fe1\u53f7<\/li>\n\n\n\n<li><strong>\u5f53\u524d\u81ea\u53d8\u91cf\uff1a<\/strong> \u65f6\u95f4\uff08\u79d2\uff09<\/li>\n\n\n\n<li><strong>\u7279\u5f81\u9009\u62e9\uff1a<\/strong> \u5de6\u4fa7\u7a97\u683c\u663e\u793a\u4e86\u4ece\u632f\u52a8\u6570\u636e\u4e2d\u63d0\u53d6\u7684\u7279\u5f81\u7684\u5c42\u6b21\u5217\u8868\u3002\u8fd9\u4e9b\u7279\u5f81\u5217\u5728 <code>FeatureTable1 > Vibration_tsa_sigstats<\/code> \u4e0b\u3002<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">\u7279\u5f81<\/h3>\n\n\n\n<p>\u5217\u51fa\u7684\u7279\u5f81\u5305\u62ec\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u6e05\u9664\u56e0\u5b50\uff08Clearance Factor\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u5cf0\u5ea6\u56e0\u5b50\uff08Crest Factor\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u51b2\u51fb\u56e0\u5b50\uff08Impulse Factor\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u5cf0\u5ea6\uff08Kurtosis\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u5747\u503c\uff08Mean\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u5cf0\u503c\uff08Peak Value\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u5747\u65b9\u6839\uff08RMS\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u504f\u5ea6\uff08Skewness\uff09<\/strong><\/li>\n\n\n\n<li><strong>\u6807\u51c6\u5dee\uff08Std\uff09<\/strong><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">\u8be6\u60c5\u7a97\u683c<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u6d3e\u751f\u81ea\uff1a<\/strong> Tacho\/Data, Vibration\/Data<\/li>\n\n\n\n<li><strong>\u81ea\u53d8\u91cf\uff1a<\/strong> \u65f6\u95f4\uff08\u79d2\uff09<\/li>\n\n\n\n<li><strong>\u5e27\u7b56\u7565\uff1a<\/strong> \u5168\u4fe1\u53f7<\/li>\n\n\n\n<li><strong>\u6570\u636e\u96c6\uff1a<\/strong> Ensemble1\uff0816\u4e2a\u6210\u5458\uff09<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">\u76f4\u65b9\u56fe\u5206\u6790<\/h3>\n\n\n\n<p>\u6bcf\u4e2a\u76f4\u65b9\u56fe\u4ee3\u8868\u7279\u5b9a\u7279\u5f81\u7684\u5206\u5e03\uff0c\u6309 <code>faultCode<\/code>\uff080 \u6216 1\uff09\u5206\u5f00\uff0c\u8868\u793a\u662f\u5426\u5b58\u5728\u6545\u969c\u3002<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u6e05\u9664\u56e0\u5b50\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6982\u7387\u5206\u5e03\u663e\u793a\uff0c\u6545\u969c\u4ee3\u78011\uff08\u6a59\u8272\uff09\u7684\u503c\u96c6\u4e2d\u5728\u7ea63.0\uff0c\u800c\u6545\u969c\u4ee3\u78010\uff08\u84dd\u8272\uff09\u96c6\u4e2d\u5728\u7ea62.8\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5cf0\u5ea6\u56e0\u5b50\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6545\u969c\u4ee3\u78011\uff08\u6a59\u8272\uff09\u5206\u5e03\u5728\u7ea62.0\u5904\uff0c\u800c\u6545\u969c\u4ee3\u78010\uff08\u84dd\u8272\uff09\u7565\u4f4e\u4e00\u4e9b\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u51b2\u51fb\u56e0\u5b50\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u7c7b\u4f3c\u4e8e\u5cf0\u5ea6\u56e0\u5b50\uff0c\u6545\u969c\u4ee3\u78011\u5728\u7ea62.6\u5904\u8fbe\u5230\u5cf0\u503c\uff0c\u800c\u6545\u969c\u4ee3\u78010\u5728\u7ea62.4\u5904\u8fbe\u5230\u5cf0\u503c\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5cf0\u5ea6\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u660e\u663e\u7684\u533a\u522b\u5728\u4e8e\uff0c\u6545\u969c\u4ee3\u78011\uff08\u6a59\u8272\uff09\u5728\u8f83\u9ad8\u503c\u5904\u663e\u8457\u8fbe\u5230\u5cf0\u503c\uff0c\u800c\u6545\u969c\u4ee3\u78010\u5219\u6ca1\u6709\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5747\u503c\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6545\u969c\u4ee3\u78011\u7684\u5747\u503c\u66f4\u5206\u6563\uff0c\u4e14\u7565\u9ad8\u4e8e\u6545\u969c\u4ee3\u78010\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5cf0\u503c\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6545\u969c\u4ee3\u78011\u663e\u793a\u51fa\u8f83\u9ad8\u7684\u5cf0\u503c\u5206\u5e03\uff0c\u76f8\u5bf9\u4e8e\u6545\u969c\u4ee3\u78010\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5747\u65b9\u6839\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6545\u969c\u4ee3\u78011\u7684\u5747\u65b9\u6839\u503c\u9ad8\u4e8e\u6545\u969c\u4ee3\u78010\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u504f\u5ea6\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u504f\u5ea6\u7684\u5206\u5e03\u5bf9\u4e8e\u6545\u969c\u4ee3\u78011\u66f4\u5bbd\uff0c\u8868\u660e\u5728\u5b58\u5728\u6545\u969c\u65f6\u504f\u5ea6\u53d8\u5316\u66f4\u5927\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u6807\u51c6\u5dee\uff1a<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u6545\u969c\u4ee3\u78011\u7684\u6807\u51c6\u5dee\u7565\u9ad8\u4e8e\u6545\u969c\u4ee3\u78010\uff0c\u8868\u660e\u5728\u51fa\u73b0\u6545\u969c\u65f6\u6570\u636e\u53d8\u5f02\u6027\u66f4\u5927\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">\u7ed3\u8bba<\/h3>\n\n\n\n<p>\u8fd9\u4e9b\u76f4\u65b9\u56fe\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6e05\u6670\u7684\u53ef\u89c6\u5316\u8868\u793a\uff0c\u663e\u793a\u4e86\u632f\u52a8\u4fe1\u53f7\u7684\u4e0d\u540c\u7edf\u8ba1\u7279\u5f81\u5982\u4f55\u57fa\u4e8e\u6545\u969c\uff08faultCode 1\uff09\u6216\u65e0\u6545\u969c\uff08faultCode 0\uff09\u7684\u5b58\u5728\u800c\u53d8\u5316\u3002\u8fd9\u4e9b\u7279\u5f81\u5728\u9884\u6d4b\u6027\u7ef4\u62a4\u548c\u673a\u68b0\u7cfb\u7edf\u6545\u969c\u8bca\u65ad\u4e2d\u81f3\u5173\u91cd\u8981\uff0c\u5176\u4e2d\u67d0\u4e9b\u7279\u5f81\uff08\u5982\u5cf0\u5ea6\u3001\u5747\u65b9\u6839\u548c\u5cf0\u503c\uff09\u7684\u8f83\u9ad8\u503c\u53ef\u80fd\u8868\u660e\u6545\u969c\u7684\u5b58\u5728\u3002\u8be5\u754c\u9762\u5141\u8bb8\u8be6\u7ec6\u5206\u6790\u548c\u81ea\u5b9a\u4e49\uff0c\u4f7f\u5176\u6210\u4e3a\u6545\u969c\u68c0\u6d4b\u548c\u72b6\u6001\u76d1\u6d4b\u4e2d\u4fe1\u53f7\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>The image depicts a feature designer tool interface used for analyzing vibration data, with histograms showing various statistical features extracted from the vibration signal. Here&#8217;s a detailed technical analysis of the image:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Interface Overview<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Feature Designer Tool:<\/strong> This is likely a part of a software suite for signal processing and machine learning, aimed at feature extraction from time-series data. &#8212;&#8212;><strong>Yes, it&#8217;s Matlab<\/strong><\/li>\n\n\n\n<li><strong>Grouping and Binning Controls:<\/strong> At the top, there are options for grouping by <code>faultCode<\/code>, setting bin width, bin limits, and normalization method. This indicates the flexibility to customize how data is grouped and visualized.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Variables Pane<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Current Frame Policy:<\/strong> Full Signal<\/li>\n\n\n\n<li><strong>Current Independent Variable:<\/strong> Time (seconds)<\/li>\n\n\n\n<li><strong>Feature Selection:<\/strong> The left pane shows a hierarchical list of features extracted from the vibration data. The features are listed under <code>FeatureTable1 > Vibration_tsa_sigstats<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Features<\/h3>\n\n\n\n<p>The listed features are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Clearance Factor<\/strong><\/li>\n\n\n\n<li><strong>Crest Factor<\/strong><\/li>\n\n\n\n<li><strong>Impulse Factor<\/strong><\/li>\n\n\n\n<li><strong>Kurtosis<\/strong><\/li>\n\n\n\n<li><strong>Mean<\/strong><\/li>\n\n\n\n<li><strong>Peak Value<\/strong><\/li>\n\n\n\n<li><strong>RMS (Root Mean Square)<\/strong><\/li>\n\n\n\n<li><strong>Skewness<\/strong><\/li>\n\n\n\n<li><strong>Std (Standard Deviation)<\/strong><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Details Pane<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Derived From:<\/strong> Tacho\/Data, Vibration\/Data<\/li>\n\n\n\n<li><strong>Independent Variable:<\/strong> Time (seconds)<\/li>\n\n\n\n<li><strong>Frame Policy:<\/strong> Full Signal<\/li>\n\n\n\n<li><strong>Dataset:<\/strong> Ensemble1 (16 Members)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Histograms Analysis<\/h3>\n\n\n\n<p>Each histogram represents the distribution of a specific feature, separated by <code>faultCode<\/code> (0 or 1), indicating whether a fault is present or not.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Clearance Factor:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Probability distribution shows that the faultCode 1 (orange) has higher values concentrated around 3.0, whereas faultCode 0 (blue) is around 2.8.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Crest Factor:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The distribution for faultCode 1 (orange) peaks around 2.0, whereas faultCode 0 (blue) peaks slightly lower.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Impulse Factor:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Similar to the Crest Factor, with faultCode 1 peaking around 2.6 and faultCode 0 peaking around 2.4.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Kurtosis:<\/strong>\n<ul class=\"wp-block-list\">\n<li>A notable difference where faultCode 1 (orange) peaks significantly at a higher value compared to faultCode 0.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Mean:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Mean values for faultCode 1 are more spread out and slightly higher than for faultCode 0.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Peak Value:<\/strong>\n<ul class=\"wp-block-list\">\n<li>FaultCode 1 shows a higher peak value distribution compared to faultCode 0.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>RMS:<\/strong>\n<ul class=\"wp-block-list\">\n<li>RMS values for faultCode 1 are higher than those for faultCode 0.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Skewness:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Distribution is wider for faultCode 1, indicating more variation in skewness when a fault is present.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Std (Standard Deviation):<\/strong>\n<ul class=\"wp-block-list\">\n<li>FaultCode 1 has a slightly higher standard deviation compared to faultCode 0, suggesting more variability in the data when a fault occurs.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Conclusion<\/h3>\n\n\n\n<p>The histograms provide a clear visual representation of how different statistical features of the vibration signal differ based on the presence (faultCode 1) or absence (faultCode 0) of a fault. These features can be critical in predictive maintenance and fault diagnosis of mechanical systems, where higher values of certain features (like kurtosis, RMS, and peak value) can indicate the presence of faults. The interface allows for detailed analysis and customization, making it a powerful tool for signal processing and machine learning applications in fault detection and condition monitoring.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>TSA: <strong><a href=\"https:\/\/www.crystalinstruments.com\/time-synchronous-average\" target=\"_blank\" rel=\"noreferrer noopener\">Time Synchronous Averaging<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u8fd9\u5f20\u56fe\u7247\u5c55\u793a\u4e86\u4e00\u4e2a\u7528\u4e8e\u5206\u6790\u632f\u52a8\u6570\u636e\u7684\u7279\u5f81\u8bbe\u8ba1\u5de5\u5177\u754c\u9762\uff0c\u5e26\u6709\u663e\u793a\u4ece\u632f\u52a8\u4fe1\u53f7\u4e2d\u63d0\u53d6\u7684\u5404\u79cd\u7edf\u8ba1\u7279\u5f81\u7684\u76f4\u65b9\u56fe\u3002\u4ee5\u4e0b\u662f\u8be5 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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