Predictive Maintenance with Machine Learning Training Course

Participants can select priority assets and define failure modes suitable for prediction.

• Asset criticality matrix scoring consequence, likelihood, detectability and redundancy
• FMEA identifies functional failures, causes, effects and existing controls
• Failure mode categories separated by mechanical, electrical, process and human causes
• P-F interval concepts determine feasible detection and intervention windows
• Bad actors identified from downtime, cost, repeat failure and safety records
• Asset hierarchy aligned between equipment registers, CMMS and sensor tags

Participants can identify the evidence needed to detect developing equipment faults.

• Vibration measurements reveal imbalance, misalignment, looseness and bearing defects
• Thermography detects abnormal resistance, friction, insulation and cooling conditions
• Oil analysis tracks contamination, wear debris, viscosity and lubricant condition
• Ultrasound signals indicate leaks, lubrication faults and electrical discharge
• Process variables provide load, speed, pressure, flow and ambient context
• CMMS histories supply work orders, failure codes, actions and materials

Participants can write a precise problem statement and decision design for a predictive use case.

• Decision canvas defines asset, failure mode, user, action and value
• Prediction target states failure event, anomaly score or remaining life
• Alert horizon matches maintenance lead time and P-F interval
• Maintenance response rules specify inspection, intervention, deferment or escalation
• False-positive and false-negative consequences documented before model development
• Success measures combine model accuracy, work quality and avoided loss

Participants can assess whether available data is fit for a predictive maintenance use case.

• Data profiling measures completeness, uniqueness, consistency, timeliness and validity
• Timestamp alignment reconciles sensor, historian, CMMS and production-system clocks
• Missing-value patterns separated into random gaps, outages and planned downtime
• Failure codes standardised using controlled taxonomies and defect classifications
• Duplicate work orders and inconsistent asset identifiers identified systematically
• Data-quality register assigns owners, remediation actions and acceptance thresholds

Participants can prepare labelled data sets that represent equipment condition and failure timing.

• Resampling converts irregular readings into consistent analytical time intervals
• Operating states segmented into startup, shutdown, steady load and upset
• Failure labels anchored to functional failure, repair start or replacement
• Prediction windows exclude post-failure evidence and maintenance-induced signals
• Censoring handled where assets have not failed during observation periods
• Data leakage prevented through strict separation of future and past information

Participants can specify meaningful features from condition, operating and maintenance data.

• Rolling mean, variance, slope and range summarise changing signals
• Lag features capture delayed relationships between load and condition
• Frequency-domain features identify harmonic energy, sidebands and spectral kurtosis
• Operating-context normalisation adjusts readings for speed, load and temperature
• Maintenance-event features represent time since overhaul, repair and lubrication
• Feature catalogue records formula, source, refresh rate and engineering rationale

Participants can identify patterns that distinguish normal operation from developing faults.

• Trend plots reveal drift, step changes, cycles and sudden excursions
• Control charts distinguish common-cause variation from special-cause signals
• Scatter plots test relationships between condition measures and operating context
• Fault signatures compared across known failures, repairs and normal periods
• Correlation screening identifies redundant features and possible sensor problems
• Domain review tests whether detected patterns have plausible physical causes

Participants can design anomaly detection models for assets with limited failure history.

• Baseline models define normal behaviour from healthy operating periods
• Z-score and multivariate control limits flag statistical deviations
• Isolation Forest isolates rare observations through random partitioning
• Principal component analysis detects abnormal multivariable process patterns
• Autoencoder reconstruction error measures departures from learned normal states
• Anomaly scores calibrated against inspections, alarms and maintenance findings

Participants can select and interpret classification models for known failure modes.

• Binary classification predicts failure occurrence within a defined horizon
• Multiclass models distinguish bearing, seal, electrical and process faults
• Logistic regression provides interpretable baseline probability estimates
• Decision trees express condition rules through readable split logic
• Random Forest combines multiple trees for stable nonlinear predictions
• Gradient boosting improves predictive accuracy through sequential error correction

Participants can frame remaining useful life estimation and judge its operational suitability.

• Remaining useful life defined from current condition to functional failure
• Degradation trajectories model progressive wear under comparable duty cycles
• Survival analysis estimates failure probability across future time intervals
• Weibull analysis characterises failure behaviour and age-related risk
• Cox proportional hazards models incorporate condition and operating covariates
• RUL uncertainty communicated through ranges, confidence bounds and decision rules

Participants can manage rare-event data without overstating model performance.

• Class imbalance occurs when failures represent few historical observations
• Accuracy rejected when normal-operation records dominate evaluation samples
• Precision and recall quantify alert usefulness and missed-fault exposure
• F1 score balances precision and recall for threshold comparison
• Class weights penalise missed failures according to operational consequence
• Resampling methods assessed carefully to avoid synthetic operational distortion

Participants can test predictive models against realistic future operating conditions.

• Time-based train, validation and test splits preserve event chronology
• Cross-validation adapted for grouped assets and sequential observations
• Confusion matrices expose true alerts, false alarms and missed failures
• Precision-recall curves support threshold choice for rare failure events
• Backtesting compares predicted alerts with historical maintenance opportunities
• Acceptance criteria combine technical performance, lead time and workload capacity

Participants can explain model recommendations and subject them to engineering challenge.

• Feature importance ranks variables contributing to model predictions
• SHAP values explain individual alerts using positive and negative drivers
• Partial dependence plots show predicted risk across changing conditions
• Engineering review challenges correlations lacking credible failure mechanisms
• Decision logs record evidence, expert judgement and alert dispositions
• Explainability requirements differ for safety-critical and routine maintenance decisions

Participants can translate model output into controlled maintenance workflows.

• Alert tiers define advisory, inspection, planned intervention and urgent response
• Thresholds reflect failure consequence, lead time and crew capacity
• CMMS integration creates notifications, work requests and linked asset records
• Inspection checklists capture findings needed to confirm or reject alerts
• Planner roles sequence predictive work with shutdowns and production windows
• Closed-loop feedback returns maintenance findings to model improvement records

Participants can define a practical architecture for delivering predictive maintenance outputs.

• Data pipeline maps acquisition, storage, transformation, scoring and presentation
• Edge processing supports low-latency monitoring where network access is constrained
• Historian, data lake and CMMS roles separated within the architecture
• Batch scoring compared with streaming inference for different failure horizons
• Application programming interfaces exchange alerts, asset context and work-order status
• Deployment design assigns ownership across engineering, IT, analytics and operations

Participants can establish controls for monitoring model quality after deployment.

• Data drift detects changing sensor distributions and operating conditions
• Concept drift identifies weakening relationships between signals and failures
• Alert precision monitored through inspection outcomes and completed work orders
• Model registry records versions, features, approvals and deployment dates
• Retraining triggers defined by performance decline, equipment changes or new failures
• Fallback procedures maintain safe decisions when data feeds or models fail

Participants can identify control requirements for connected predictive maintenance solutions.

• IEC 62443 principles address industrial control system security risks
• Role-based access limits modification of models, thresholds and maintenance records
• Sensor calibration and traceability support trustworthy condition measurements
• Safety cases define human review requirements for high-consequence recommendations
• Data retention rules preserve evidence for incidents, audits and model review
• Governance board assigns approval authority for model and workflow changes

Participants can quantify value and plan a controlled expansion from pilot to portfolio.

• Avoided failure cost includes downtime, repair, quality loss and safety exposure
• Intervention cost compares planned work against emergency response alternatives
• Expected value combines failure probability, consequence and prediction performance
• Pilot scorecard tracks alerts, confirmed faults, avoided loss and user adoption
• Scaling roadmap sequences asset classes by value, repeatability and data readiness
• Operating model defines funding, support, training and continuous improvement

Participants can build, review and present a complete predictive maintenance implementation plan for a selected asset system.

• Asset and failure mode selected using documented criticality evidence
• Use-case canvas states prediction target, response workflow and value assumptions
• Data specification maps signals, CMMS fields, labels and quality controls
• Model approach justifies anomaly, classification or remaining-life method selection
• Validation plan defines time splits, metrics, thresholds and acceptance criteria
• Deployment roadmap assigns architecture, governance, monitoring and ownership
• Peer review challenges feasibility, engineering rationale and expected benefits