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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 reconstruction errors in bearing vibration<\/h2>\n<p>An autoencoder models vibration-window features from the complete E2 run-to-failure experiment. The adapted dataset contains 9,925 one-second windows; the final tenth of acquisitions is labelled anomalous for this teaching example.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>9,925<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>18<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>2,781<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>18<\/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-bearinganomalydetection-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>Reliability analysts can inspect how reconstruction error changes across one bearing run and evaluate a recorded operating threshold. The anomaly labels are derived by Artelnics from acquisition order, not supplied failure diagnoses.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Derived labels<\/h3><p>Identify the final acquisition decile explicitly.<\/p><\/div><div class=\"ndb-value-card\"><h3>Reconstruction evidence<\/h3><p>Compare saved samples with their reconstructions.<\/p><\/div><div class=\"ndb-value-card\"><h3>False-alarm review<\/h3><p>Inspect both missed labels and false-positive counts.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Reliability engineering<\/span><span>Vibration analysis<\/span><span>Condition monitoring<\/span><\/div><div class=\"ndb-note\">One-experiment condition-pattern benchmark; no remaining-useful-life estimate or automatic protection claim.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The source is Run-to-failure vibration dataset of self-aligning double-row ball bearings \u2013 Part 1. Each five-second acquisition at 25.6 kHz is split into five non-overlapping windows. Eighteen time-domain and relative spectral-power features are retained alongside the derived anomaly label.<\/p><p>Source: <a href=\"https:\/\/data.mendeley.com\/datasets\/htk59pp5wx\/1\">Run-to-failure vibration dataset of self-aligning double-row ball bearings &#8211; Part 1<\/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>one-second vibration window<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>9,925<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>19<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>18<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>18<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>5358<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>1786<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>2781<\/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\">mean<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">standard_deviation<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">rms<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">absolute_peak<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">peak_to_peak<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">mean_absolute<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">skewness<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">kurtosis<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">crest_factor<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">impulse_factor<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">shape_factor<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">zero_crossing_rate<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">spectral_centroid_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">relative_power_0_500_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">relative_power_500_2000_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">relative_power_2000_5000_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">relative_power_5000_10000_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">relative_power_10000_12800_hz<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">anomaly<\/th><td>Evaluation<\/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-bearinganomalydetection-t0-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t0-s1-20261006\" role=\"img\" aria-label=\"mean distribution\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t0-s1-20261006.png\" alt=\"mean distribution\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"0.029\",\"0.031\",\"0.033\",\"0.035\",\"0.037\",\"0.04\",\"0.042\",\"0.044\",\"0.046\",\"0.048\"],\"y\":[0.10000000149011612,1.5,7.699999809265137,20.899999618530273,30.700000762939453,25.100000381469727,10.800000190734863,2.799999952316284,0.30000001192092896,0.10000000149011612]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"mean histogram\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"0.029\",\"0.031\",\"0.033\",\"0.035\",\"0.037\",\"0.04\",\"0.042\",\"0.044\",\"0.046\",\"0.048\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Bin center\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,40.01],\"title\":{\"text\":\"% frequency\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>mean distribution. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-bearinganomalydetection-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t0-s2-20261006\" role=\"img\" aria-label=\"standard_deviation distribution\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t0-s2-20261006.png\" alt=\"standard_deviation distribution\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"width\":0.3799999952316284,\"x\":[1.15,1.5499999999999998,1.95,2.35,2.75,3.15,3.5500000000000003,3.95,4.35,4.75],\"y\":[27.299999237060547,68.9000015258789,0.30000001192092896,0.4000000059604645,0.8999999761581421,0.8999999761581421,0.800000011920929,0.30000001192092896,0.10000000149011612,0.10000000149011612]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"standard_deviation histogram\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,4.95],\"tickmode\":\"array\",\"ticktext\":[\"0\",\"1\",\"2\",\"3\",\"4\"],\"tickvals\":[0,1,2,3,4],\"title\":{\"text\":\"Bin center\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,80.02],\"title\":{\"text\":\"% frequency\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>standard_deviation distribution. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved roles are retained exactly. Training and validation use the selected normal records, while testing includes both label types. These are record-level internal results; source recordings or acquisition windows can remain dependent.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 18 encoded inputs and 18 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>18<\/td><td>18<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>18<\/td><td>8<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>8<\/td><td>4<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>4<\/td><td>8<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>8<\/td><td>18<\/td><td>Identity<\/td><\/tr><tr><th scope=\"row\">Unscaling<\/th><td>18<\/td><td>18<\/td><td><\/td><\/tr><\/tbody><\/table><\/div><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t1-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-bearinganomalydetection-t1-s1-20261006.png\" alt=\"Bearing anomaly detection from vibration features \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 Adam with MeanAbsoluteError.<\/p><h3>Adaptive moment estimation 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>100<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:07<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum epochs number<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.189<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.284<\/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-bearinganomalydetection-t2-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t2-s2-20261006\" role=\"img\" aria-label=\"Adaptive moment estimation error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t2-s2-20261006.png\" alt=\"Adaptive moment estimation error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training 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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><\/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 2781 source records.<\/p><p>ROC AUC describes ranking on this testing subset. Any optimal threshold shown in the saved ROC report was selected descriptively on that same subset; it is not an independently validated operating policy.<\/p><h3>Area under curve<\/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\">Area under curve<\/th><td>0.752<\/td><\/tr><\/tbody><\/table><\/div><h3>Anomaly detection tests<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Accuracy<\/th><td>0.492988<\/td><\/tr><tr><th scope=\"row\">Recall<\/th><td>0.925628<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>0.25196<\/td><\/tr><tr><th scope=\"row\">Precision<\/th><td>0.408064<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.566421<\/td><\/tr><\/tbody><\/table><\/div><h3>Confusion matrix<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted anomalous<\/th><th scope=\"col\">Predicted normal<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Real anomalous<\/th><td>921<\/td><td>74<\/td><\/tr><tr><th scope=\"row\">Real normal<\/th><td>1336<\/td><td>450<\/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-bearinganomalydetection-t7-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t7-s1-20261006\" role=\"img\" aria-label=\"Anomaly ROC chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t7-s1-20261006.png\" alt=\"Anomaly ROC chart\"><script 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Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,1],\"y\":[0,1]},{\"connectgaps\":false,\"marker\":{\"color\":\"#ed982a\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0.3241879940032959],\"y\":[0.6864320039749146]}],\"layout\":{\"annotations\":[{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Optimal Threshold\",\"x\":0.49418799400329594,\"y\":0.5964320039749146},{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Area under curve: 0.752\",\"x\":0.7,\"y\":0.02}],\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Anomaly ROC 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-bearinganomalydetection-t4-s3-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t4-s3-20261006\" role=\"img\" aria-label=\"Reconstruction error\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t4-s3-20261006.png\" alt=\"Reconstruction error\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"mean\",\"standard_deviation\",\"rms\",\"absolute_peak\",\"peak_to_peak\",\"mean_absolute\",\"skewness\",\"kurtosis\",\"crest_factor\",\"impulse_factor\",\"shape_factor\",\"zero_crossing_rate\",\"spectral_centroid_hz\",\"relative_power_0_500_hz\",\"relative_power_500_2000_hz\",\"relative_power_2000_5000_hz\",\"relative_power_5000_10000_hz\",\"relative_power_10000_12800_hz\"],\"y\":[0.9229999780654907,0.4659999907016754,0.4569999873638153,0.10100000351667404,0.0949999988079071,0.3659999966621399,3.2090001106262207,1.409999966621399,0.5479999780654907,0.45399999618530273,1.5759999752044678,1.5019999742507935,0.05400000140070915,0.26600000262260437,1.909000039100647,1.7970000505447388,0.28999999165534973,0.7059999704360962]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Reconstruction error\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"mean\",\"standard_deviation\",\"rms\",\"absolute_peak\",\"peak_to_peak\",\"mean_absolute\",\"skewness\",\"kurtosis\",\"crest_factor\",\"impulse_factor\",\"shape_factor\",\"zero_crossing_rate\",\"spectral_centroid_hz\",\"relative_power_0_500_hz\",\"relative_power_500_2000_hz\",\"relative_power_2000_5000_hz\",\"relative_power_5000_10000_hz\",\"relative_power_10000_12800_hz\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Variable\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,3.5005],\"title\":{\"text\":\"Standard deviations\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Reconstruction error. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-bearinganomalydetection-t6-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-bearinganomalydetection-t6-s2-20261006\" role=\"img\" aria-label=\"Input and reconstruction\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-bearinganomalydetection-t6-s2-20261006.png\" alt=\"Input and reconstruction\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"Input\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"mean\",\"standard_deviation\",\"rms\",\"absolute_peak\",\"peak_to_peak\",\"mean_absolute\",\"skewness\",\"kurtosis\",\"crest_factor\",\"impulse_factor\",\"shape_factor\",\"zero_crossing_rate\",\"spectral_centroid_hz\",\"relative_power_0_500_hz\",\"relative_power_500_2000_hz\",\"relative_power_2000_5000_hz\",\"relative_power_5000_10000_hz\",\"relative_power_10000_12800_hz\"],\"y\":[-0.7951459884643555,-0.26146599650382996,-0.2618870139122009,-0.5169000029563904,-0.3166790008544922,-0.252826988697052,-1.9231300354003906,0.5314329862594604,-0.5004169940948486,-0.4992330074310303,-0.10397399961948395,2.6898000240325928,3.7030699253082275,-3.6292099952697754,-0.8108369708061218,0.7570710182189941,3.4967598915100098,3.7595300674438477]},{\"marker\":{\"color\":\"#99ca53\"},\"name\":\"Reconstruction\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"mean\",\"standard_deviation\",\"rms\",\"absolute_peak\",\"peak_to_peak\",\"mean_absolute\",\"skewness\",\"kurtosis\",\"crest_factor\",\"impulse_factor\",\"shape_factor\",\"zero_crossing_rate\",\"spectral_centroid_hz\",\"relative_power_0_500_hz\",\"relative_power_500_2000_hz\",\"relative_power_2000_5000_hz\",\"relative_power_5000_10000_hz\",\"relative_power_10000_12800_hz\"],\"y\":[-0.5446169972419739,-0.737185001373291,-0.7828570008277893,-0.7022860050201416,-0.7907630205154419,-0.6355860233306885,-1.55771005153656,-0.3055579960346222,-0.5640140175819397,-0.6401690244674683,-0.6908969879150391,2.4872500896453857,2.9897100925445557,-3.1263699531555176,-1.0431400537490845,1.051900029182434,2.786829948425293,1.5482399463653564]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Input and reconstruction\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"mean\",\"standard_deviation\",\"rms\",\"absolute_peak\",\"peak_to_peak\",\"mean_absolute\",\"skewness\",\"kurtosis\",\"crest_factor\",\"impulse_factor\",\"shape_factor\",\"zero_crossing_rate\",\"spectral_centroid_hz\",\"relative_power_0_500_hz\",\"relative_power_500_2000_hz\",\"relative_power_2000_5000_hz\",\"relative_power_5000_10000_hz\",\"relative_power_10000_12800_hz\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Variable\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-6,6.002],\"title\":{\"text\":\"Standard deviations\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Input and reconstruction. 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/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-bearinganomalydetection-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>Windows within one acquisition and one bearing run are correlated. Test results do not establish performance on another bearing or machine. The saved detector produces many false positives at its operating threshold; evaluate threshold selection, acquisition grouping and independent runs before operational use.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/data.mendeley.com\/datasets\/htk59pp5wx\/1\">Run-to-failure vibration dataset of self-aligning double-row ball bearings &#8211; Part 1<\/a>. Alberto Gabrielli; Luca Arpa; Mattia Battarra; Emiliano Mucchi. 10.17632\/htk59pp5wx.1<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. 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