{"id":3371,"date":"2026-04-02T13:53:31","date_gmt":"2026-04-02T11:53:31","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/air-compressor\/"},"modified":"2026-10-06T13:26:41","modified_gmt":"2026-10-06T11:26:41","slug":"air-compressor","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/air-compressor\/","title":{"rendered":"Industrial machine failure prediction"},"content":{"rendered":"<style>\n.ndb{--ndb-code-bg:#f3f7fa;--ndb-code-fg:#12354b;box-sizing:border-box;width:100%;margin-left:0;margin-right:0;padding:22px 0 14px;background:#fff;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(calc(100% - 48px),1160px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;}\n.ndb-executive h2{margin:0 0 12px;font-size:30px}.ndb-executive 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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Inspect machine failure and failure-mode scores<\/h2>\n<p>This example uses the synthetic AI4I 2020 predictive-maintenance dataset to model machine failure and five recorded failure modes from operating conditions. The saved model produces six separate scores; multiple failure modes may coexist.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>10,000<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>9<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>2,000<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>6<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-machinefailureprediction-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Industrial challenge<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Industrial challenge<\/h2><p>Maintenance analysts need to distinguish the general failure label from specific mechanisms before deciding which observations need inspection. This example supports model evaluation and inspection planning, not a remaining-useful-life estimate or automatic machine shutdown.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Separate outcomes<\/h3><p>Inspect the general failure label and each failure-mode score.<\/p><\/div><div class=\"ndb-value-card\"><h3>Measure missed failures<\/h3><p>Compare sensitivity and false positives at the saved threshold.<\/p><\/div><div class=\"ndb-value-card\"><h3>Review operating conditions<\/h3><p>Use results as evidence for engineering inspection.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Reliability engineering<\/span><span>Maintenance analytics<\/span><span>Manufacturing<\/span><\/div><div class=\"ndb-note\">Synthetic operating-condition benchmark. These labels do not establish failure lead time or measured maintenance savings.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>AI4I 2020 contains 10,000 synthetic operating records. The source models tool wear, heat dissipation, power, overstrain and random failures. Identifier fields are excluded from the model; product type is encoded alongside operating measurements.<\/p><p>Source: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/601\/ai4i+2020+predictive+maintenance+dataset\">AI4I 2020 Predictive Maintenance Dataset<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>synthetic machine record<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>10,000<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>13<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>9<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>6<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>2000<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>2000<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">UDI<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Type<\/th><td>Input<\/td><td>Categorical<\/td><td>H, L, M<\/td><\/tr><tr><th scope=\"row\">Air temperature [K]<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Process temperature [K]<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Rotational speed [rpm]<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Torque [Nm]<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Tool wear [min]<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Machine failure<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">TWF<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">HDF<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">PWF<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">OSF<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><tr><th scope=\"row\">RNF<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-machinefailureprediction-t1-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-machinefailureprediction-t1-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-machinefailureprediction-t1-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"Machine failure\",\"TWF\",\"HDF\",\"PWF\",\"OSF\",\"RNF\"],\"marker\":{\"colors\":[\"#b5dff4\",\"#6abfea\",\"#209fdf\",\"#1879aa\",\"#115375\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[47.61000061035156,6.460000038146973,16.149999618530273,13.34000015258789,13.760000228881836,2.6700000762939453]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 9 encoded inputs and 6 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>9<\/td><td>9<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>9<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>6<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. Use the output encodings and decision rule documented with this project; do not assume independent sigmoid scores sum to one.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-machinefailureprediction-t7-s1-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-machinefailureprediction-t7-s1-20261006.png\" alt=\"Industrial machine failure prediction \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with CrossEntropy.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>151<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:01<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.152<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.173<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-machinefailureprediction-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-machinefailureprediction-t0-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>On this test subset a majority-class baseline would correctly classify 96.95% of the records. This baseline does not detect both classes and is not an optimized model.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 2000 source records.<\/p><p>These operating-point metrics are calculated from the saved confusion counts at threshold <strong>0.5<\/strong>. They apply to <strong>Machine failure<\/strong>; the remaining outputs have separate one-versus-rest tables.<\/p><h3>Confusion table: Machine failure<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>17 (0.9%)<\/td><td>44 (2.2%)<\/td><td>61 (3.0%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>4 (0.2%)<\/td><td>1935 (96.8%)<\/td><td>1939 (96.9%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>21 (1.0%)<\/td><td>1979 (98.9%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><details><summary>Inspect every output confusion table<\/summary><h3>Confusion table: TWF<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>0 (0.0%)<\/td><td>11 (0.6%)<\/td><td>11 (0.6%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>1 (0.1%)<\/td><td>1988 (99.4%)<\/td><td>1989 (99.4%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>1 (0.1%)<\/td><td>1999 (99.9%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><h3>Confusion table: HDF<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>8 (0.4%)<\/td><td>7 (0.3%)<\/td><td>15 (0.8%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>1 (0.1%)<\/td><td>1984 (99.2%)<\/td><td>1985 (99.3%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>9 (0.4%)<\/td><td>1991 (99.6%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><h3>Confusion table: PWF<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>10 (0.5%)<\/td><td>8 (0.4%)<\/td><td>18 (0.9%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>2 (0.1%)<\/td><td>1980 (99.0%)<\/td><td>1982 (99.1%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>12 (0.6%)<\/td><td>1988 (99.4%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><h3>Confusion table: OSF<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>8 (0.4%)<\/td><td>14 (0.7%)<\/td><td>22 (1.1%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>0 (0.0%)<\/td><td>1978 (98.9%)<\/td><td>1978 (98.9%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>8 (0.4%)<\/td><td>1992 (99.6%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><h3>Confusion table: RNF<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>0 (0.0%)<\/td><td>3 (0.2%)<\/td><td>3 (0.2%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>0 (0.0%)<\/td><td>1997 (99.8%)<\/td><td>1997 (99.8%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>0 (0.0%)<\/td><td>2000 (100.0%)<\/td><td>2000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><\/details><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>2000<\/td><\/tr><tr><th scope=\"row\">Positive cases<\/th><td>61<\/td><\/tr><tr><th scope=\"row\">Positive prevalence<\/th><td>3.05%<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>97.60%<\/td><\/tr><tr><th scope=\"row\">Sensitivity \/ recall<\/th><td>27.87%<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>99.79%<\/td><\/tr><tr><th scope=\"row\">Precision \/ PPV<\/th><td>80.95%<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.415<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-machinefailureprediction-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2><p>Randomly held-out synthetic rows do not establish transfer to a real compressor or production line. At threshold 0.5, the saved general-failure classifier misses 44 of 61 positive test rows; accuracy must be interpreted alongside this low recall. Validate against machine- and time-separated records before operational use.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/601\/ai4i+2020+predictive+maintenance+dataset\">AI4I 2020 Predictive Maintenance Dataset<\/a>. Stephan Matzka. 10.24432\/C5HS5C<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-machinefailureprediction-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":24,"featured_media":4719,"template":"","categories":[],"tags":[43],"class_list":["post-3371","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-industry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Industrial machine failure prediction<\/title>\n<meta name=\"description\" content=\"This example uses the synthetic AI4I 2020 predictive-maintenance dataset to model machine failure and five recorded failure modes from operating conditions. 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