{"id":23873,"date":"2026-09-15T16:22:30","date_gmt":"2026-09-15T14:22:30","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/?post_type=learning&#038;p=23873"},"modified":"2026-09-17T10:07:40","modified_gmt":"2026-09-17T08:07:40","slug":"hydraulic-system-anomaly-detection","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/hydraulic-system-anomaly-detection\/","title":{"rendered":"Hydraulic system anomaly detection"},"content":{"rendered":"<style>\n.ndb{--ndb-code-bg:#f3f7fa;--ndb-code-fg:#12354b;box-sizing:border-box;width:100vw;margin-left:calc(50% - 50vw);padding:22px 24px 14px;background:#eee;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;background:linear-gradient(135deg,#12354b,#245e80);color:#fff}\n.ndb-executive h2{margin:0 0 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Mono\",monospace!important;font-size:14px;line-height:1.55}\n.ndb :not(pre)>code{padding:.08em .32em;border-radius:4px;background:#e6eef3!important;color:#12354b!important}\n.ndb .ndb-expression{max-width:100%;overflow:auto;margin:22px auto;padding:18px 20px;border:1px solid #d5e2e9;border-radius:12px;background:#fff!important;color:#12354b!important;text-align:center}\n.ndb .ndb-expression math,.ndb .ndb-expression mjx-container,.ndb .ndb-expression .MathJax{background:transparent!important;color:#12354b!important}\n@media(max-width:1020px){.ndb-figure-grid,.ndb-comparison-grid{grid-template-columns:1fr}.ndb-figure-grid .ndb-interactive-chart,.ndb-comparison-grid .ndb-interactive-chart{height:500px;min-height:500px}}\n@media(max-width:820px){.ndb-kpis{grid-template-columns:repeat(2,minmax(0,1fr))}.ndb-value-grid{grid-template-columns:1fr}}\n@media(max-width:620px){.ndb{padding:12px 14px}.ndb-kpis{grid-template-columns:1fr}.ndb-executive{padding:24px 20px}.ndb-card table{font-size:13px}.ndb-card th,.ndb-card td{padding:9px 8px}}\n.ndb .ndb-calculator-grid>br,.ndb .ndb-calculator-grid>p:empty{display:none!important}\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Detect internal pump-leakage patterns in hydraulic test-rig cycles<\/h2>\n<p>A 17-input autoencoder learns a normal baseline from no-leak hydraulic test-rig cycles and ranks held-out cycles by reconstruction error. The final saved model reaches ROC AUC 0.902. Its automatic threshold is conservative: it flags 246 of 984 labelled leakage cycles and 6 of 245 no-leak cycles, so the score is useful for prioritization but the default operating point misses most leakage examples.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>2,205<\/strong><span>60-second cycles<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>17<\/strong><span>cycle-mean signals<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>984<\/strong><span>labelled leak cycles<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.902<\/strong><span>test ROC AUC<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a href=\"#7-model-deployment\">Review test results<\/a><a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Get Neural Designer<\/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>Internal leakage reduces a hydraulic pump&#8217;s volumetric efficiency and can change several measurements at once rather than creating one universal sensor limit. A reconstruction model can rank cycles whose combined pressure, flow, temperature, vibration and motor-power pattern differs from the no-leak baseline. The score supports condition-monitoring review; fault confirmation and maintenance decisions still require operating context and engineering diagnostics.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><strong>Focus the use case<\/strong><p>Rank cycles for internal pump-leakage review rather than claiming a general hydraulic diagnosis.<\/p><\/div><div class=\"ndb-value\"><strong>Expose severity<\/strong><p>Report weak and severe leakage separately so aggregate recall does not hide missed early-stage cases.<\/p><\/div><div class=\"ndb-value\"><strong>Control the operating point<\/strong><p>Choose threshold and persistence from the acceptable inspection workload, then monitor drift and false alerts over time.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Reliability engineering<\/span><span>Maintenance<\/span><span>Data science<\/span><\/div><div class=\"ndb-note\">This model detects patterns associated with the packaged internal pump-leakage label. It does not diagnose cooler, valve or accumulator condition, quantify leakage flow, estimate remaining useful life or replace hydraulic inspection.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The packaged <code>hydraulic_anomaly_detection.csv<\/code> contains 2,205 complete rows, 17 numeric inputs and one binary evaluation label. A direct comparison with the official UCI files confirms that every row represents one of the test rig&#8217;s 60-second load cycles and that each input is the arithmetic mean of one complete sensor channel during that cycle. The packaged rows are reordered, but all 2,205 official cycles are represented once, with no missing or exact duplicate rows.<\/p><table><thead><tr><th scope=\"col\">Input family<\/th><th scope=\"col\">Packaged variables<\/th><th scope=\"col\">Physical quantity<\/th><th scope=\"col\">Official sampling rate<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Pressure<\/th><td>pressure_1_bar\u2013pressure_6_bar<\/td><td>Six pressure channels (bar)<\/td><td>100 Hz<\/td><\/tr><tr><th scope=\"row\">Electrical<\/th><td>motor_power_w<\/td><td>Motor power (W)<\/td><td>100 Hz<\/td><\/tr><tr><th scope=\"row\">Flow<\/th><td>flow_1_l_min\u2013flow_2_l_min<\/td><td>Volume flow (L\/min)<\/td><td>10 Hz<\/td><\/tr><tr><th scope=\"row\">Temperature<\/th><td>temperature_1_c\u2013temperature_4_c<\/td><td>Four temperatures (\u00b0C)<\/td><td>1 Hz<\/td><\/tr><tr><th scope=\"row\">Vibration<\/th><td>vibration_mm_s<\/td><td>Vibration (mm\/s)<\/td><td>1 Hz<\/td><\/tr><tr><th scope=\"row\">Derived process signals<\/th><td>cooling_efficiency_pct, cooling_power_kw, efficiency_factor_pct<\/td><td>Cooling efficiency, cooling power and efficiency factor<\/td><td>1 Hz<\/td><\/tr><\/tbody><\/table><p>The binary label also matches the official cycle annotation exactly: pump-leakage class 0 becomes <code>anomaly = 0<\/code>; weak leakage (class 1) and severe leakage (class 2) become <code>anomaly = 1<\/code>. Cooler, valve and accumulator conditions are present in the source experiment but are not the reference target in this derived file.<\/p><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">No leak<\/th><th scope=\"col\">Weak leakage<\/th><th scope=\"col\">Severe leakage<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>732<\/td><td>0<\/td><td>0<\/td><td>732<\/td><\/tr><tr><th scope=\"row\">Validation<\/th><td>244<\/td><td>0<\/td><td>0<\/td><td>244<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>245<\/td><td>492<\/td><td>492<\/td><td>1,229<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>1,221<\/td><td>492<\/td><td>492<\/td><td>2,205<\/td><\/tr><\/tbody><\/table><div class=\"ndb-note\">Training and validation contain only no-leak cycles; all weak- and severe-leak cycles are reserved for testing. This is appropriate for demonstrating novelty detection, but every row comes from the same laboratory rig and the test set is deliberately enriched to 80.1% leakage. The reported precision and accuracy therefore do not estimate field alarm rates. Averaging each 60-second signal also removes waveform, transient and spectral information that may matter in a deployed monitoring system.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The final autoencoder has 17 \u2192 8 \u2192 4 \u2192 8 \u2192 17 units and 373 trainable parameters. Inputs are standardized with the training means and standard deviations; the three hidden stages use ReLU and the reconstruction output is linear before values are returned to their original units. The pump-leakage label is used only for evaluation and never enters the reconstruction path.<\/p><p>The four-unit bottleneck forces the network to represent the normal operating pattern compactly. A high error indicates poor reconstruction of the complete sensor pattern; it does not identify a physical root cause by itself.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/hydraulicanomalydetection-architecture-1.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/hydraulicanomalydetection-architecture-1.png\" alt=\"Saved hydraulic autoencoder layer widths from input to reconstruction\"><\/a><figcaption>Native <em>Report neural network<\/em> output for the 17 \u2192 8 \u2192 4 \u2192 8 \u2192 17 hydraulic autoencoder; select the diagram to inspect it at full resolution.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The final saved model minimizes mean absolute error with Adam (learning rate 0.001, batch size 32) and no regularization. It reaches the configured maximum of 1,000 epochs with standardized training error 0.107 and validation error 0.119 on the normal-only subsets. The close final values do not establish robustness across random initializations; they show only that this run fits the two stored normal blocks similarly.<\/p><div class=\"ndb-interactive-bundle\"><script src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\" data-noptimize=\"1\" data-cfasync=\"false\" charset=\"utf-8\"><\/script><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-training\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive training and validation mean absolute error history for the hydraulic autoencoder\"><\/div><figcaption>Native <em>Perform training<\/em> result from the final saved model. 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.22,\"yanchor\":\"top\"},\"margin\":{\"b\":105,\"l\":72,\"pad\":4,\"r\":36,\"t\":72},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"text\":\"Training and validation MAE\",\"x\":0.5,\"xanchor\":\"center\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,999],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.8002],\"title\":{\"text\":\"Mean absolute error\"},\"zerolinecolor\":\"#aeb6c2\"}},\"renderError\":\"Unable to render the interactive chart.\"}<\/script><script data-noptimize=\"1\">(()=>{const host=document.getElementById(\"ndb-hydraulic-training\");const source=document.getElementById(\"ndb-hydraulic-training-figure\");if(!host||!source)return;const figure=JSON.parse(source.textContent);const render=()=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).catch(error=>{host.textContent='The interactive chart could not be rendered.';console.error(error);});if(window.Plotly)render();else{let attempts=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);render();}else if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No executed architecture-selection comparison is saved. The 17 \u2192 8 \u2192 4 \u2192 8 \u2192 17 network is therefore the evaluated configuration, not a demonstrated optimum. A professional study would compare bottleneck sizes and random seeds on a separate normal validation period, freeze the selected model, and only then evaluate leakage cycles.<\/p><h3>Threshold sensitivity<\/h3><p>The automatic rule uses mean training error plus one standard deviation. The candidate table comes from the final Neural Designer threshold task. Validation-normal and test columns are consequences of each rule; test performance must not be used to select a threshold and then reported as independent evidence.<\/p><div class=\"ndb-table-scroll\"><table><thead><tr><th scope=\"col\">Candidate rule<\/th><th scope=\"col\">Threshold<\/th><th scope=\"col\">Validation normal flagged<\/th><th scope=\"col\">Test cycles flagged<\/th><th scope=\"col\">Leak recall<\/th><th scope=\"col\">Precision<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training P95<\/th><td>1.142<\/td><td>5.33%<\/td><td>48.3%<\/td><td>59.2%<\/td><td>98.1%<\/td><\/tr><tr><th scope=\"row\">Mean + 1 SD (reported)<\/th><td>2.389<\/td><td>3.28%<\/td><td>20.5%<\/td><td>25.0%<\/td><td>97.6%<\/td><\/tr><tr><th scope=\"row\">Training P99<\/th><td>3.048<\/td><td>1.23%<\/td><td>18.8%<\/td><td>22.9%<\/td><td>97.4%<\/td><\/tr><tr><th scope=\"row\">Mean + 2 SD<\/th><td>4.212<\/td><td>0.82%<\/td><td>18.8%<\/td><td>22.9%<\/td><td>97.4%<\/td><\/tr><\/tbody><\/table><\/div><div class=\"ndb-note\">The default threshold favours a small review queue and high test precision, but it is not a useful high-sensitivity alarm in this run. A field threshold needs a normal-only calibration period, realistic fault prevalence and a persistence rule tied to the acceptable inspection workload.<\/div><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The final anomaly score is mean absolute reconstruction error in the variables&#8217; original units. Across 732 normal training cycles its mean is approximately 0.567 and its standard deviation is approximately 1.823, giving the exported automatic mean-plus-one-standard-deviation threshold <strong>2.38958<\/strong>. The model flags 252 of 1,229 test cycles.<\/p><p>The score still ranks leakage-labelled cycles reasonably well (ROC AUC <strong>0.902<\/strong>), but the chosen operating point recovers only 25.0% of them. This distinction matters: ROC AUC describes ranking across all thresholds, while the confusion matrix describes one specific review threshold.<\/p><div class=\"ndb-note ndb-note--warning\">The score averages reconstruction errors measured in bar, watts, L\/min, \u00b0C, mm\/s, percent and kW, so it changes if units or feature definitions change. Use the standardized sample-reconstruction charts to inspect contributing signals and recalibrate the score for any production feature pipeline.<\/div><div class=\"ndb-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><th scope=\"col\">Count or context<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">ROC AUC<\/th><td>0.902<\/td><td>Score ranking across 1,229 packaged test cycles<\/td><\/tr><tr><th scope=\"row\">Leak recall<\/th><td>25.0%<\/td><td>246 of 984 weak or severe leakage cycles<\/td><\/tr><tr><th scope=\"row\">No-leak specificity<\/th><td>97.6%<\/td><td>239 of 245 no-leak cycles<\/td><\/tr><tr><th scope=\"row\">Precision on this enriched subset<\/th><td>97.6%<\/td><td>246 of 252 flagged cycles; prevalence-dependent<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>39.8%<\/td><td>At exported threshold 2.38958<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>39.5%<\/td><td>485 of 1,229 test cycles<\/td><\/tr><\/tbody><\/table><\/div><div class=\"ndb-table-scroll\"><table><thead><tr><th scope=\"col\">Official pump-leakage state<\/th><th scope=\"col\">Flagged<\/th><th scope=\"col\">Not flagged<\/th><th scope=\"col\">Detection rate<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">No leak (class 0)<\/th><td>6<\/td><td>239<\/td><td>2.4% flagged<\/td><\/tr><tr><th scope=\"row\">Weak leakage (class 1)<\/th><td>105<\/td><td>387<\/td><td>21.3% detected<\/td><\/tr><tr><th scope=\"row\">Severe leakage (class 2)<\/th><td>141<\/td><td>351<\/td><td>28.7% detected<\/td><\/tr><\/tbody><\/table><\/div><div class=\"ndb-figure-grid\"><div class=\"ndb-figure-item\"><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-train-histogram\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive histogram of normal training reconstruction errors with threshold 2.38958\"><\/div><figcaption>Native <em>Calculate anomaly threshold<\/em> export for the final training run and its 732 normal training cycles. 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if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><div class=\"ndb-figure-item\"><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-normal-reconstruction\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive standardized input and reconstruction comparison for no-leak sample 1743\"><\/div><figcaption>Native <em>Plot sample reconstruction<\/em> export for no-leak sample 1743. Its reproduced score is approximately 0.414, below the 2.38958 threshold. The reconstruction pair shares the same vertical scale.<\/figcaption><\/figure><script id=\"ndb-hydraulic-normal-reconstruction-figure\" type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"hydraulic-sample-1743-reconstruction\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"Input\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[-0.6030570268630981,-0.4218379855155945,-0.8563500046730042,-0.51426100730896,-0.8213779926300049,-0.810155987739563,-0.7739660143852234,0.03230179846286774,-0.6426159739494324,0.801675021648407,0.7921969890594482,0.7631409764289856,0.8100870251655579,0.661346971988678,-0.9892020225524902,-1.001829981803894,0.33075299859046936],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"},{\"marker\":{\"color\":\"#99ca53\"},\"name\":\"Reconstruction\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[-0.6619799733161926,-0.541333019733429,-0.6058220267295837,-0.562270998954773,-0.7979729771614075,-0.7961360216140747,-0.8272079825401306,0.009066269733011723,-0.7559369802474976,0.8017690181732178,0.8212680220603943,0.8058930039405823,0.7973570227622986,0.7260779738426208,-0.8045939803123474,-0.7452329993247986,0.25308099389076233],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.34,\"yanchor\":\"top\"},\"margin\":{\"b\":180,\"l\":72,\"pad\":4,\"r\":36,\"t\":72},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"text\":\"Sample 1743 \u2014 no leak\",\"x\":0.5,\"xanchor\":\"center\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Cycle-mean signal\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\",\"tickangle\":-52,\"tickfont\":{\"size\":10}},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-3,3],\"title\":{\"text\":\"Standard deviations from training mean\"},\"zerolinecolor\":\"#aeb6c2\"}},\"renderError\":\"Unable to render the interactive chart.\"}<\/script><script data-noptimize=\"1\">(()=>{const host=document.getElementById(\"ndb-hydraulic-normal-reconstruction\");const source=document.getElementById(\"ndb-hydraulic-normal-reconstruction-figure\");if(!host||!source)return;const figure=JSON.parse(source.textContent);const render=()=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).catch(error=>{host.textContent='The interactive chart could not be rendered.';console.error(error);});if(window.Plotly)render();else{let attempts=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);render();}else if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><div class=\"ndb-figure-item\"><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-leak-reconstruction\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive standardized input and reconstruction comparison for weak-leak sample 892\"><\/div><figcaption>Native <em>Plot sample reconstruction<\/em> export for weak-leak sample 892. Its reproduced score is approximately 0.969, below the 2.38958 threshold, so this example exposes a false negative at the reported operating point.<\/figcaption><\/figure><script id=\"ndb-hydraulic-leak-reconstruction-figure\" type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"hydraulic-sample-892-reconstruction\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"Input\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[0.7635130286216736,0.551692008972168,0.8718060255050659,2.2033400535583496,1.2651300430297852,1.2624200582504272,1.3903900384902954,-0.3108130097389221,1.2358800172805786,-1.2338999509811401,-1.235319972038269,-1.2376099824905396,-1.2535699605941772,-1.3356000185012817,1.4077099561691284,1.2654399871826172,-0.8254280090332031],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"},{\"marker\":{\"color\":\"#99ca53\"},\"name\":\"Reconstruction\",\"opacity\":1,\"showlegend\":true,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[0.8667960166931152,0.7790560126304626,1.2257200479507446,1.6979700326919556,1.3479499816894531,1.3443399667739868,1.2309900522232056,0.2341109961271286,1.2101099491119385,-1.2586300373077393,-1.2576700448989868,-1.2579599618911743,-1.2656199932098389,-1.12322998046875,1.3345999717712402,1.2295500040054321,-0.09330350160598755],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.34,\"yanchor\":\"top\"},\"margin\":{\"b\":180,\"l\":72,\"pad\":4,\"r\":36,\"t\":72},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"text\":\"Sample 892 \u2014 weak leakage\",\"x\":0.5,\"xanchor\":\"center\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Cycle-mean signal\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\",\"tickangle\":-52,\"tickfont\":{\"size\":10}},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-3,3],\"title\":{\"text\":\"Standard deviations from training mean\"},\"zerolinecolor\":\"#aeb6c2\"}},\"renderError\":\"Unable to render the interactive chart.\"}<\/script><script data-noptimize=\"1\">(()=>{const host=document.getElementById(\"ndb-hydraulic-leak-reconstruction\");const source=document.getElementById(\"ndb-hydraulic-leak-reconstruction-figure\");if(!host||!source)return;const figure=JSON.parse(source.textContent);const render=()=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).catch(error=>{host.textContent='The interactive chart could not be rendered.';console.error(error);});if(window.Plotly)render();else{let attempts=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);render();}else if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><div class=\"ndb-figure-item\"><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-normal-residuals\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive standardized reconstruction residuals for no-leak sample 1743\"><\/div><figcaption>Native per-signal absolute standardized residuals for no-leak sample 1743. The residual pair uses the same vertical scale.<\/figcaption><\/figure><script id=\"ndb-hydraulic-normal-residuals-figure\" type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"hydraulic-sample-1743-residuals\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[0.05900000035762787,0.11900000274181366,0.25099998712539673,0.04800000041723251,0.023000000044703484,0.014000000432133675,0.05299999937415123,0.023000000044703484,0.11299999803304672,0,0.028999999165534973,0.0430000014603138,0.013000000268220901,0.06499999761581421,0.1850000023841858,0.25699999928474426,0.07800000160932541],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":165,\"l\":72,\"pad\":4,\"r\":36,\"t\":72},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"text\":\"Sample 1743 \u2014 no-leak residuals\",\"x\":0.5,\"xanchor\":\"center\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Cycle-mean signal\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\",\"tickangle\":-52,\"tickfont\":{\"size\":10}},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.8],\"title\":{\"text\":\"Absolute error (standard deviations)\"},\"zerolinecolor\":\"#aeb6c2\"},\"showlegend\":false},\"renderError\":\"Unable to render the interactive chart.\"}<\/script><script data-noptimize=\"1\">(()=>{const host=document.getElementById(\"ndb-hydraulic-normal-residuals\");const source=document.getElementById(\"ndb-hydraulic-normal-residuals-figure\");if(!host||!source)return;const figure=JSON.parse(source.textContent);const render=()=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).catch(error=>{host.textContent='The interactive chart could not be rendered.';console.error(error);});if(window.Plotly)render();else{let attempts=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);render();}else if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><div class=\"ndb-figure-item\"><figure class=\"ndb-interactive-figure\"><div id=\"ndb-hydraulic-leak-residuals\" class=\"ndb-interactive-chart\" role=\"img\" aria-label=\"Interactive standardized reconstruction residuals for weak-leak sample 892\"><\/div><figcaption>Native per-signal absolute standardized residuals for weak-leak sample 892. Its moderate residual pattern remains below the automatic threshold, illustrating why the model misses many weak-leak cycles.<\/figcaption><\/figure><script id=\"ndb-hydraulic-leak-residuals-figure\" type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"hydraulic-sample-892-residuals\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"y\":[0.10300000011920929,0.22699999809265137,0.3540000021457672,0.5049999952316284,0.08299999684095383,0.0820000022649765,0.1589999943971634,0.5450000166893005,0.026000000536441803,0.02500000037252903,0.02199999988079071,0.019999999552965164,0.012000000104308128,0.21199999749660492,0.0729999989271164,0.035999998450279236,0.7319999933242798],\"hovertemplate\":\"%{x}<br>%{y:.3f} standard deviations<extra>%{fullData.name}<\\\/extra>\"}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":165,\"l\":72,\"pad\":4,\"r\":36,\"t\":72},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"text\":\"Sample 892 \u2014 weak-leak residuals\",\"x\":0.5,\"xanchor\":\"center\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Pressure 1\",\"Pressure 2\",\"Pressure 3\",\"Pressure 4\",\"Pressure 5\",\"Pressure 6\",\"Motor power\",\"Flow 1\",\"Flow 2\",\"Temperature 1\",\"Temperature 2\",\"Temperature 3\",\"Temperature 4\",\"Vibration\",\"Cooling efficiency\",\"Cooling power\",\"Efficiency factor\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Cycle-mean signal\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\",\"tickangle\":-52,\"tickfont\":{\"size\":10}},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.8],\"title\":{\"text\":\"Absolute error (standard deviations)\"},\"zerolinecolor\":\"#aeb6c2\"},\"showlegend\":false},\"renderError\":\"Unable to render the interactive chart.\"}<\/script><script data-noptimize=\"1\">(()=>{const host=document.getElementById(\"ndb-hydraulic-leak-residuals\");const source=document.getElementById(\"ndb-hydraulic-leak-residuals-figure\");if(!host||!source)return;const figure=JSON.parse(source.textContent);const render=()=>window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).catch(error=>{host.textContent='The interactive chart could not be rendered.';console.error(error);});if(window.Plotly)render();else{let attempts=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);render();}else if(++attempts>100){clearInterval(timer);host.textContent='The interactive chart could not be loaded.';}},50);}})();<\/script><\/div><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>In Neural Designer, run <em>Perform training<\/em> to reproduce the learning curve, <em>Calculate anomaly threshold<\/em> to inspect the training and testing score distributions, <em>Calculate anomaly ROC curve<\/em> to assess ranking performance and <em>Calculate anomaly detection tests<\/em> to recover the operating-point metrics. For a selected cycle, <em>Plot sample reconstruction<\/em> reports both standardized input-versus-reconstruction bars and per-variable reconstruction errors, which is the most useful diagnostic view for signals with different units.<\/p><ol class=\"ndb-pipeline\"><li><strong>Acquire<\/strong>Preserve asset, cycle, timestamp, duty point and sensor-health metadata.<\/li><li><strong>Transform<\/strong>Reproduce the same 60-second aggregation, variable names, units and ordering.<\/li><li><strong>Score<\/strong>Store the versioned reconstruction error and per-variable residuals.<\/li><li><strong>Gate<\/strong>Apply a threshold and persistence rule calibrated to the acceptable inspection workload.<\/li><li><strong>Confirm<\/strong>Review trends and contributing signals with hydraulic diagnostics before maintenance action.<\/li><\/ol><p><em>Export anomaly detector<\/em> is appropriate only after validating that complete chain on separate equipment or test campaigns.<\/p><div class=\"ndb-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Get Neural Designer<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\">Explore model types<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2><p>This is an internal benchmark on one laboratory test rig. The split does not isolate a different rig, asset, campaign or duty profile, and the test subset contains far more leakage cycles than a typical monitoring stream. Cycle means discard within-cycle dynamics. The binary target also combines weak and severe leakage; at the reported threshold the detector recovers only 21.3% of weak-leak and 28.7% of severe-leak cycles.<\/p><p>The reconstruction score is unit-sensitive and is not a leakage magnitude, remaining-useful-life estimate or universal alarm limit. Before field use, validate independent assets and operating regimes, retain the original severity labels, calibrate threshold and persistence on realistic normal operation, quantify false alerts per asset-hour, evaluate detection lead time, test sensor failures and drift, and define an engineering confirmation procedure. The detector must not command an automatic shutdown or replace hydraulic inspection.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><p>Project evidence: the current saved <code>hydraulic.nd<\/code> project, <code>hydraulic_anomaly_detection.csv<\/code>, native Neural Designer tasks executed on that saved model and a row-level comparison against the official sensor matrices and <code>profile.txt<\/code>. Primary source: Helwig, Pignanelli and Sch\u00fctze, <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/447\/condition%20monitoring%20of%20hydraulic%20systems\">Condition Monitoring of Hydraulic Systems<\/a>, UCI Machine Learning Repository, DOI <a href=\"https:\/\/doi.org\/10.24432\/C5CW21\">10.24432\/C5CW21<\/a>. See <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\">Neural Designer model types<\/a>.<\/p><\/section>\n<\/div>\n<\/div>\n","protected":false},"author":152,"featured_media":0,"template":"","categories":[29],"tags":[],"class_list":["post-23873","learning","type-learning","status-publish","hentry","category-examples"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Hydraulic system anomaly detection - Neural Designer<\/title>\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.neuraldesigner.com\/learning\/examples\/hydraulic-system-anomaly-detection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hydraulic system anomaly detection - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"Detect internal pump-leakage patterns in hydraulic test-rig cycles A 17-input autoencoder learns a normal baseline from no-leak hydraulic test-rig cycles and ranks held-out cycles by reconstruction error. The final saved model reaches ROC AUC 0.902. Its automatic threshold is conservative: it flags 246 of 984 labelled leakage cycles and 6 of 245 no-leak cycles, so [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/hydraulic-system-anomaly-detection\/\" \/>\n<meta property=\"og:site_name\" content=\"Neural Designer\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-17T08:07:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/hydraulicanomalydetection-architecture-1.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@NeuralDesigner\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/hydraulic-system-anomaly-detection\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/hydraulic-system-anomaly-detection\/\",\"name\":\"Hydraulic system anomaly detection - 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