{"id":81367,"date":"2026-09-18T17:04:17","date_gmt":"2026-09-18T20:04:17","guid":{"rendered":"https:\/\/a3aengenharia.com\/?post_type=articles&#038;p=81367"},"modified":"2026-09-18T17:04:17","modified_gmt":"2026-09-18T20:04:17","slug":"predictive-analytics-engineering-assets-failures-risks-decisions","status":"publish","type":"articles","link":"https:\/\/a3aengenharia.com\/en-us\/content\/technical-articles\/predictive-analytics-engineering-assets-failures-risks-decisions\/","title":{"rendered":"Predictive Analytics in Engineering: Assets, Failures, Risks, and Decision-Making"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>Predictive analytics in engineering<\/strong> is the use of historical and current data, statistical models, and machine-learning techniques to estimate future events relevant to assets, systems, projects, and operations. Instead of only describing what happened or diagnosing why it happened, predictive analytics seeks to answer questions such as: what is the probability of failure? which variable is likely to exceed its limit? which asset shows abnormal degradation? which delay is most likely to occur?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In engineering, this capability is especially valuable when decisions depend on large volumes of operational data, maintenance history, sensors, inspections, or project performance. The value is not in \u201cpredicting the future\u201d with certainty, but in transforming signals and patterns into useful probabilities for prioritization, planning, and intervention.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Predictive analytics is not synonymous with predictive maintenance. Predictive maintenance is a specific application focused on physical assets. Predictive analytics is broader and can support reliability, energy, quality, risk, planning, project performance, logistics, and asset management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It should also not be confused with prescriptive analytics. Predictive analytics estimates what may occur; prescriptive analytics seeks to recommend what to do in response to that scenario.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">What is predictive analytics in engineering<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">IBM defines predictive analytics as an area of advanced analytics that uses historical data combined with statistical modeling, data mining, and machine learning to estimate future outcomes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The logic can be organized into four levels:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>descriptive<\/strong>: what happened;<\/li><li><strong>diagnostic<\/strong>: why it happened;<\/li><li><strong>predictive<\/strong>: what will probably happen;<\/li><li><strong>prescriptive<\/strong>: what to do.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In engineering, these levels can coexist within the same process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A system can detect increasing vibration, identify the combination of variables associated with the behavior, estimate the probability of failure over the coming weeks, and then prioritize an inspection.<\/p>\n\n\n\n<figure class=\"a3a-mermaid\"><svg id=\"a3a-diagram-1\" width=\"100%\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"flowchart\" style=\"max-width:min(1103.15625px, 100%);height:auto;display:block;margin:0 auto\" viewBox=\"0 0 1103.15625 68.5\" role=\"graphics-document document\" aria-roledescription=\"flowchart-v2\" aria-labelledby=\"chart-title-a3a-diagram-1\"><title id=\"chart-title-a3a-diagram-1\">From descriptive analysis to predictive decision-making in engineering<\/title><style>#a3a-diagram-1{font-family:Roboto,sans-serif;font-size:15px;fill:var(--a3a-diag-text, #0a0a0a);}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#a3a-diagram-1 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear 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xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Descriptive<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-C-3\" transform=\"translate(407.9375, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-69.765625\" y=\"-26.25\" width=\"139.53125\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-39.765625, -11.25)\"><rect><\/rect><foreignObject width=\"79.53125\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Diagnostic<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-D-5\" transform=\"translate(588.1328125, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-60.4296875\" y=\"-26.25\" 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class=\"nodeLabel\"><p>Prescriptive<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-F-9\" transform=\"translate(987.7890625, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-107.3671875\" y=\"-26.25\" width=\"214.734375\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-77.3671875, -11.25)\"><rect><\/rect><foreignObject width=\"154.734375\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Engineering decision<\/p><\/span><\/div><\/foreignObject><\/g><\/g><\/g><\/g><\/g><\/svg><figcaption>From descriptive analysis to predictive decision-making in engineering<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"\/conteudo\/artigos-tecnicos\/inteligencia-artificial-na-engenharia\/\">Artificial Intelligence in Engineering pillar<\/a> organizes machine learning as one of the main classes of AI applied to engineering. Predictive analytics is one of the most mature ways to transform operational data into decisions.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Data, models, and predictive architecture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Predictive models depend first on data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Historical data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Failures, interventions, inspections, alarms, production, and operating conditions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Condition data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Temperature, vibration, current, pressure, noise, humidity, energy, or other variables.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Context data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Load, environment, shift, operating regime, product, age, location, or criticality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Design data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Parameters, specifications, limits, and configurations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Events<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Shutdowns, maintenance activities, replacements, and configuration changes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A typical architecture can be represented as follows:<\/p>\n\n\n\n<figure class=\"a3a-mermaid\"><svg id=\"a3a-diagram-2\" width=\"100%\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"flowchart\" style=\"max-width:min(1773.171875px, 100%);height:auto;display:block;margin:0 auto\" viewBox=\"0 0 1773.171875 68.5\" role=\"graphics-document document\" aria-roledescription=\"flowchart-v2\" aria-labelledby=\"chart-title-a3a-diagram-2\"><title id=\"chart-title-a3a-diagram-2\">Predictive analytics architecture applied to engineering assets and systems<\/title><style>#a3a-diagram-2{font-family:Roboto,sans-serif;font-size:15px;fill:var(--a3a-diag-text, #0a0a0a);}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes 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-11.25)\"><rect><\/rect><foreignObject width=\"115.671875\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Decision rule<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-H-13\" transform=\"translate(1668.0390625, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-97.1328125\" y=\"-26.25\" width=\"194.265625\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-67.1328125, -11.25)\"><rect><\/rect><foreignObject width=\"134.265625\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Engineering action<\/p><\/span><\/div><\/foreignObject><\/g><\/g><\/g><\/g><\/g><\/svg><figcaption>Predictive analytics architecture applied to engineering assets and systems<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Forecast quality is limited by historical-data quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Quality<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Missing data, uncalibrated sensors, and inconsistent records need to be addressed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Representativeness<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Historical data need to contain conditions comparable to current use.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Labeling<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Failures and events need to be correctly identified.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Synchronization<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Time series require alignment of timestamps and sampling frequency.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Context change<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A plant may operate differently after a retrofit, maintenance intervention, or process change.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When data come from sensors and operational systems, the content on <a href=\"\/conteudo\/artigos-tecnicos\/iiot-industrial-internet-of-things-engenharia-automacao\/\">IIoT in Engineering<\/a> helps explain the connectivity and telemetry layer.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Applications in assets, failures, energy, and projects<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Asset failure<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The best-known application is estimating failure risk.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">IBM Maximo Predict, for example, uses operational data, condition data, failure history, and machine-learning models to estimate failure probability and expected time to failure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The objective is not to replace maintenance, but to anticipate risk.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Anomaly detection<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Models can learn normal behavior and identify deviations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is useful when there is not enough failure history.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Remaining Useful Life<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">RUL seeks to estimate remaining useful life.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is particularly useful when degradation evolves over time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Energy and performance<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Forecasts can estimate consumption, load, demand, efficiency, or thermal behavior.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Quality<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Models can identify conditions associated with production or performance deviations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Project risks<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Historical schedule, cost, change, and productivity data can support risk estimates.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Schedule and cost forecasting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Progress, productivity, earned value, and change series can feed trend models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"\/conteudo\/artigos-tecnicos\/gestao-valor-agregado-projetos-engenharia\/\">Earned Value Management in Engineering Projects<\/a> already provides a deterministic performance baseline. Predictive models can complement this view when sufficient historical data exist.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Asset management<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">ISO 55001:2024 reinforces decision-making, risk, performance, data, and knowledge as elements of an asset management system. The 2024 edition itself includes a specific section on predictive action.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This connection matters: prediction creates value only when it is transformed into a decision within asset governance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"\/servicos\/operacao\/gestao-de-ativos-de-engenharia\/\">Engineering Asset Management<\/a> is the service layer that organizes asset registers, criticality, lifecycle, and performance.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">How to validate predictions and avoid false signals<\/h2>\n\n\n\n<div class=\"wp-block-a3a-destaque\">\n<p class=\"wp-block-paragraph\">Prediction without criticality context can lead to poor decisions. The same estimated risk needs to be interpreted according to consequence, redundancy, safety, and operational impact.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"\/servicos\/operacao\/engenharia-de-confiabilidade-e-disponibilidade\/\">Reliability and Availability Engineering<\/a><\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">A model that appears accurate can still be useless in operations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Train-test separation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The model should not be evaluated only on the data used for training.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Temporal data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For time series, chronological order needs to be preserved.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Per-class accuracy<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Rare failures can be hidden by high overall accuracy.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Precision and recall<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If false negatives are critical, recall may be more relevant.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">False positive<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Too many alerts reduce trust and generate cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Calibration<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An 80% probability needs to have coherent operational meaning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lead time<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Predicting failure only minutes in advance may not leave enough time for intervention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Drift<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Behavior can change with aging, retrofit, or a new operating regime.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ground truth<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Events need to be confirmed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Validation should also answer: does the model improve the decision compared with the baseline?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A simple baseline may be fixed periodicity, a moving average, a threshold, or a statistical model. If machine learning does not consistently outperform it, the additional complexity may not be justified.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Operational validation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Before automation, predictions can run in parallel with the current process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The team records what the model would have recommended and compares it with the actual outcome.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This shadow mode reduces the risk of introducing logic that has not yet been proven.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When the output influences availability, safety, or risk, <a href=\"\/servicos\/operacao\/engenharia-de-confiabilidade-e-disponibilidade\/\">Reliability and Availability Engineering<\/a> helps interpret criticality, failure modes, and consequences.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Integration with maintenance, Digital Twin, and asset management<\/h2>\n\n\n\n<div class=\"wp-block-a3a-destaque\">\n<p class=\"wp-block-paragraph\">Prediction creates value only when it reaches maintenance, asset management, and decision-making processes. A dashboard without an intervention workflow tends to produce little operational change.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"\/servicos\/operacao\/gestao-de-ativos-de-engenharia\/\">Engineering Asset Management<\/a><\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"\/conteudo\/artigos-tecnicos\/manutencao-preditiva-como-funciona-tecnicas-criterios\/\">Predictive Maintenance<\/a> uses condition monitoring to anticipate interventions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Predictive analytics can be one of the techniques used within this process.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">CBM<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Condition-Based Maintenance makes decisions based on the current state.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Predictive<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Seeks to anticipate future evolution or failure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prescriptive<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Suggests an intervention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">RCM<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Defines strategy based on functions, failures, and criticality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These approaches should be combined rather than treated as competitors.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"\/conteudo\/artigos-tecnicos\/digital-twin-gemeo-digital-bim-gestao-ativos\/\">Digital Twin<\/a> can provide asset context and integrate data, model, and condition.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But a Digital Twin is not a prerequisite for predictive analytics. A well-governed data foundation may be sufficient.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Criticality<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The same predictive score can lead to different actions depending on the consequence of failure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A low-criticality redundant device may simply be monitored; an asset without redundancy may require immediate intervention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Work order<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The prediction needs to reach the maintenance process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If the result remains only on a dashboard, its value is limited.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Feedback<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">After intervention, the outcome needs to return to the dataset.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This cycle improves learning and auditability.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When an organization needs to structure strategy, indicators, and backlog, <a href=\"\/servicos\/operacao\/engenharia-de-manutencao\/\">Maintenance Engineering<\/a> connects prediction to the maintenance routine.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">How to implement and procure predictive analytics<\/h2>\n\n\n\n<div class=\"wp-block-a3a-destaque\">\n<p class=\"wp-block-paragraph\">Predictive pilots need to compare the model against a simple baseline, measure real improvement, and validate in shadow mode before automating decisions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"\/servicos\/contratacao-integrada\/servicos-continuados-de-engenharia-consultiva\/\">Ongoing Engineering Consulting Services<\/a><\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">A pilot should begin with a measurable problem.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Use case<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Which event will be predicted?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Population<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Which assets or processes?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Which variables and historical data?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Horizon<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">How far in advance does the prediction need to occur?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Metric<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">How will success be measured?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Baseline<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">What is the current method?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Integration<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">How does the prediction reach operations?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Validation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Who confirms the event?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Governance<\/h3>\n\n\n\n<p 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nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Data<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-C-3\" transform=\"translate(394.296875, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-58.953125\" y=\"-26.25\" width=\"117.90625\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-28.953125, -11.25)\"><rect><\/rect><foreignObject width=\"57.90625\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Baseline<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-D-5\" transform=\"translate(558.578125, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-55.328125\" y=\"-26.25\" width=\"110.65625\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" 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transform=\"translate(917.5390625, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-78.1796875\" y=\"-26.25\" width=\"156.359375\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-48.1796875, -11.25)\"><rect><\/rect><foreignObject width=\"96.359375\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Shadow mode<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-G-11\" transform=\"translate(1107.796875, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-62.078125\" y=\"-26.25\" width=\"124.15625\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-32.078125, -11.25)\"><rect><\/rect><foreignObject width=\"64.15625\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Production<\/p><\/span><\/div><\/foreignObject><\/g><\/g><g class=\"node default\" id=\"flowchart-H-13\" transform=\"translate(1302.1796875, 34.25)\"><rect class=\"basic label-container\" style=\"\" x=\"-82.3046875\" y=\"-26.25\" width=\"164.609375\" height=\"52.5\"><\/rect><g class=\"label\" style=\"\" transform=\"translate(-52.3046875, -11.25)\"><rect><\/rect><foreignObject width=\"104.609375\" height=\"22.5\"><div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\"><span class=\"nodeLabel\"><p>Monitoring<\/p><\/span><\/div><\/foreignObject><\/g><\/g><\/g><\/g><\/g><\/svg><figcaption>Predictive analytics implementation roadmap in engineering<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Procurement also needs to define data ownership, model versioning, revalidation frequency, and rollback criteria.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When the program involves multiple disciplines and suppliers, <a href=\"\/servicos\/contratacao-integrada\/engenharia-do-proprietario\/\">Owner&#8217;s Engineering<\/a> can preserve requirements, validation, and acceptance from the owner&#8217;s perspective.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <strong>ENGiOS&#x2122;<\/strong> also fits this architecture as a technical management platform when predictions need to be related to projects, documents, assets, records, and decisions in a governed environment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model types and when to use each approach<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">There is no single predictive model. The technique depends on the variable to be estimated and the type of available data. Regression is suitable for continuous outputs; classification for discrete states; survival models for time-to-event; time-series models for chronological dependence; and anomaly detection when there is little labeled failure history.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More complex models are not automatically better. A well-calibrated regression can outperform a sophisticated architecture when data volume is small, the phenomenon is stable, and interpretability matters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Feature engineering and domain knowledge<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In engineering, derived variables often carry more information than isolated measurements. Temperature difference, vibration trend, current normalized by load, pressure-to-flow ratio, hours since the last intervention, and number of starts are examples of features with physical meaning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Domain knowledge helps create variables that reflect real degradation mechanisms and exclude spurious relationships without causal meaning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Data leakage and temporal validation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Data leakage occurs when future information inadvertently enters training. This can happen when using a field that is updated only after a failure or when randomly splitting time series so that later records appear in the training set.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In time series, train-test separation needs to preserve chronological order and reproduce real operating conditions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Class imbalance and error cost<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Relevant failures are often rare. A dataset with 99.5% normal operation can produce 99.5% accuracy simply by predicting \u201cnormal\u201d in every case. For that reason, accuracy alone is an inadequate metric.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Precision, recall, F1, and the cost of false negatives or false positives need to be analyzed according to operational consequences. For critical assets, missing a failure can be far more serious than issuing additional alerts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Explainability, thresholds, and uncertainty<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Engineering teams need to understand why a model raised the risk level. Contributing variables, feature importance, and comparison with historical behavior help verify physical plausibility.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Converting probability into action requires a threshold. A limit acceptable for one asset may be inappropriate for another because criticality, redundancy, safety, cost, and mobilization time differ. Whenever possible, the prediction should also present a confidence interval or confidence score.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">MLOps, monitoring, and revalidation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">After deployment, the model, data, features, threshold, and environment need to be controlled. Data drift, performance loss, process changes, and new failure modes should be monitored.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Updates need to be tested against a regression set before replacing the production version. The organization should define who approves a new version, how rollback is executed, and what happens if the prediction infrastructure becomes unavailable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Examples by discipline<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In electrical systems, predictive analytics can combine temperature, current, load, and events. In rotating machinery, vibration, temperature, and speed can support degradation models. In HVAC systems, consumption, pressure, and temperature can indicate efficiency loss. In projects, progress, productivity, scope changes, and response time can feed delay-risk models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The method changes with the phenomenon, but the discipline remains the same: contextualized data, a baseline, independent validation, and decisions proportional to the consequence.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When not to use predictive analytics<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Not every engineering problem needs machine learning. If there is little data, the phenomenon is well understood, and a reliable deterministic rule exists, thresholds, physics-based calculations, or condition-based maintenance may be more appropriate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It also makes little sense to build a model when no action is possible after an alert. Predicting a failure without an intervention window, parts, personnel, or authority to act produces information without operational value.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">From pilot to enterprise scale<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Scaling requires more than replicating the pilot notebook. It is necessary to standardize ingestion, features, versioning, observability, revalidation criteria, and integration with the systems that execute maintenance or asset management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The organization also needs to define which models can be reused across assets and which require specific training. Nominally identical equipment may operate under different regimes and exhibit different behavior.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Final considerations<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Predictive analytics expands engineering&#8217;s ability to anticipate events from data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It can support analysis of failures, degradation, energy, quality, schedule, cost, and risk.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But a prediction has value only when it is technically validated and integrated into the decision-making process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The mature sequence is <strong>problem \u2192 data \u2192 baseline \u2192 model \u2192 validation \u2192 operations \u2192 feedback \u2192 improvement<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The greater the consequence of the decision, the greater the rigor required for data, metrics, drift, criticality, and authority to act.<\/p>\n\n\n\n<div class=\"wp-block-a3a-destaque\">\n<p class=\"wp-block-paragraph\">When models, data, and platforms come from different suppliers, requirements, validation, and acceptance need to remain under the owner&#8217;s governance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"\/servicos\/contratacao-integrada\/engenharia-do-proprietario\/\">Owner&#8217;s Engineering<\/a><\/p>\n<\/div>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Technical references<\/summary>\n<p class=\"wp-block-paragraph\">[1] IBM. What is Predictive Analytics? IBM Think. Available at: <a href=\"https:\/\/www.ibm.com\/think\/topics\/predictive-analytics\">https:\/\/www.ibm.com\/think\/topics\/predictive-analytics<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[2] IBM. What is Predictive Maintenance? Updated June 3, 2026. Available at: <a href=\"https:\/\/www.ibm.com\/think\/topics\/predictive-maintenance\">https:\/\/www.ibm.com\/think\/topics\/predictive-maintenance<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[3] IBM. Maximo Application Suite \u2014 Asset Performance Management. Available at: <a href=\"https:\/\/www.ibm.com\/products\/maximo\/asset-performance-management\">https:\/\/www.ibm.com\/products\/maximo\/asset-performance-management<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[4] INTERNATIONAL ORGANIZATION FOR STANDARDIZATION. ISO 55001:2024 \u2014 Asset management \u2014 Asset management system \u2014 Requirements. Geneva: ISO, 2024. Available at: <a href=\"https:\/\/www.iso.org\/standard\/83054.html\">https:\/\/www.iso.org\/standard\/83054.html<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[5] INTERNATIONAL ORGANIZATION FOR STANDARDIZATION. ISO 55000:2024 \u2014 Asset management \u2014 Vocabulary, overview and principles. Geneva: ISO, 2024. Available at: <a href=\"https:\/\/www.iso.org\/standard\/83053.html\">https:\/\/www.iso.org\/standard\/83053.html<\/a><\/p>\n<\/details>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Frequently asked questions<\/summary>\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-o-que-an-lise-preditiva-na-engenharia-7ad49306\"><strong class=\"schema-faq-question\">What is predictive analytics in engineering?<\/strong> <p class=\"schema-faq-answer\">It is the use of historical and current data, statistical modeling, and machine learning to estimate future events relevant to assets, systems, projects, and operations.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-an-lise-preditiva-e-manuten-o-preditiva-s-o-a-me-8eca1171\"><strong class=\"schema-faq-question\">Are predictive analytics and predictive maintenance the same thing?<\/strong> <p class=\"schema-faq-answer\">No. Predictive maintenance is a specific application of predictive analytics focused on physical assets. Predictive analytics can also be used in energy, quality, risk, projects, and other areas.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-qual-a-diferen-a-entre-an-lise-preditiva-e-presc-6c67d710\"><strong class=\"schema-faq-question\">What is the difference between predictive and prescriptive analytics?<\/strong> <p class=\"schema-faq-answer\">Predictive analytics estimates what will probably occur. Prescriptive analytics seeks to recommend which action to take in response to the predicted scenario.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-necess-rio-ter-digital-twin-d99a883e\"><strong class=\"schema-faq-question\">Is a Digital Twin required?<\/strong> <p class=\"schema-faq-answer\">No. A Digital Twin can enrich context, but a well-governed and integrated data foundation may be sufficient for many use cases.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-como-validar-um-modelo-preditivo-661708d6\"><strong class=\"schema-faq-question\">How should a predictive model be validated?<\/strong> <p class=\"schema-faq-answer\">With independent test data, appropriate metrics, comparison with a baseline, evaluation of false positives and false negatives, lead time, calibration, and drift monitoring.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-o-que-rul-58c9f1a4\"><strong class=\"schema-faq-question\">What is RUL?<\/strong> <p class=\"schema-faq-answer\">Remaining Useful Life is the estimated remaining service life of an asset or component before it reaches a defined limit or failure condition.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-como-evitar-alarmes-demais-b9edde00\"><strong class=\"schema-faq-question\">How can excessive alerts be avoided?<\/strong> <p class=\"schema-faq-answer\">By evaluating precision, thresholds, criticality, and consequences. The objective is not to maximize sensitivity in isolation, but to generate alerts that are useful for decision-making.<\/p><\/div><div class=\"schema-faq-section\" id=\"faq-question-o-que-deve-constar-na-contrata-o-b087ad87\"><strong class=\"schema-faq-question\">What should be included in the procurement scope?<\/strong> <p class=\"schema-faq-answer\">Use case, population, data, horizon, metrics, baseline, validation, integration, governance, versioning, handover, and acceptance criteria.<\/p><\/div><\/div>\n<\/details>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Additional technical materials<\/summary>\n<h4 class=\"wp-block-heading\">Related solutions<\/h4>\n\n<ul class=\"wp-block-list\"><li><a href=\"\/solucoes\/solucoes-digitais\/engios\/\">ENGiOS&#x2122; \u2014 Management Platform for Engineering Companies<\/a><\/li><\/ul>\n\n<h4 class=\"wp-block-heading\">Related services<\/h4>\n\n<ul class=\"wp-block-list\"><li><a href=\"\/servicos\/operacao\/gestao-de-ativos-de-engenharia\/\">Engineering Asset Management<\/a><\/li><li><a href=\"\/servicos\/operacao\/engenharia-de-confiabilidade-e-disponibilidade\/\">Reliability and Availability Engineering<\/a><\/li><li><a href=\"\/servicos\/operacao\/engenharia-de-manutencao\/\">Maintenance Engineering<\/a><\/li><li><a href=\"\/servicos\/contratacao-integrada\/servicos-continuados-de-engenharia-consultiva\/\">Ongoing Engineering Consulting Services<\/a><\/li><li><a href=\"\/servicos\/contratacao-integrada\/engenharia-do-proprietario\/\">Owner&#8217;s Engineering<\/a><\/li><\/ul>\n\n<h4 class=\"wp-block-heading\">Main content on the topic<\/h4>\n\n<ul class=\"wp-block-list\"><li><a href=\"\/conteudo\/artigos-tecnicos\/inteligencia-artificial-na-engenharia\/\">Artificial Intelligence in Engineering<\/a><\/li><li><a href=\"\/conteudo\/artigos-tecnicos\/manutencao-preditiva-como-funciona-tecnicas-criterios\/\">Predictive Maintenance<\/a><\/li><li><a href=\"\/conteudo\/artigos-tecnicos\/digital-twin-gemeo-digital-bim-gestao-ativos\/\">Digital Twin<\/a><\/li><\/ul>\n\n<h4 class=\"wp-block-heading\">Related technical content<\/h4>\n\n<ul class=\"wp-block-list\"><li><a href=\"\/conteudo\/artigos-tecnicos\/iiot-industrial-internet-of-things-engenharia-automacao\/\">IIoT<\/a><\/li><li><a href=\"\/conteudo\/artigos-tecnicos\/revisao-analise-criticidade-ativos-riscos-prioridades-intervencao\/\">Asset Criticality Analysis<\/a><\/li><li><a href=\"\/conteudo\/artigos-tecnicos\/fmea-fmeca-manutencao-criticidade-gestao-riscos\/\">FMEA and FMECA in Maintenance Engineering<\/a><\/li><li><a href=\"\/conteudo\/artigos-tecnicos\/gestao-valor-agregado-projetos-engenharia\/\">Earned Value Management in Engineering Projects<\/a><\/li><li><a href=\"\/conteudo\/whitepapers\/engios-plataforma-gestao-tecnica-empresas-engenharia\/\">ENGiOS \u2014 Technical Management Platform for Engineering Companies<\/a><\/li><\/ul>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Learn how predictive analytics uses engineering data, statistical models and machine learning to estimate failures, degradation, risks, energy and project performance.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"template":"","meta":{"_a3a_global_related_solutions":[],"_a3a_global_related_services":[],"_a3a_global_related_materials":[],"_a3a_post_lang":"en-us","_a3a_translation_group_id":"89e41af6-d3fc-4f9d-a51c-7fd7becceeca","_a3a_i18n_canonical_slug":"predictive-analytics-engineering-assets-failures-risks-decisions","_a3a_prod_post_id":"","_a3a_lang_url_en-us":"","_a3a_lang_url_es-es":""},"categories":[],"segments":[],"mercados":[],"etapas":[],"class_list":["post-81367","articles","type-articles","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/articles\/81367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/articles"}],"about":[{"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/types\/articles"}],"author":[{"embeddable":true,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":1,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/articles\/81367\/revisions"}],"predecessor-version":[{"id":81369,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/articles\/81367\/revisions\/81369"}],"wp:attachment":[{"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/media?parent=81367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/categories?post=81367"},{"taxonomy":"segments","embeddable":true,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/segments?post=81367"},{"taxonomy":"mercados","embeddable":true,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/mercados?post=81367"},{"taxonomy":"etapas","embeddable":true,"href":"https:\/\/a3aengenharia.com\/en-us\/wp-json\/wp\/v2\/etapas?post=81367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}