AI for Predictive Maintenance

Machine learning models trained on your historical sensor data and failure records to predict equipment failures before they occur. Vibration signature analysis, temperature trend modelling, power consumption anomaly detection, and cycle time degradation patterns -- models trained on your specific equipment types, operating conditions, and failure modes rather than generalised benchmarks.

The feature engineering layer processes raw sensor signals into predictive features: FFT (Fast Fourier Transform) spectra from vibration accelerometers capture bearing fault frequencies at the BPFI (ball pass frequency inner raceway), BPFO (ball pass frequency outer raceway), BSF (ball spin frequency), and FTF (fundamental train frequency) -- the four bearing fault frequencies that manifest earliest in the vibration spectrum, typically 4--8 weeks before audible failure symptoms. Sensors from Brüel & Kjær or PCB Piezotronics operating in the 1--10 kHz range provide the frequency resolution needed for this analysis.

The predictive models use gradient boosted tree algorithms -- XGBoost and LightGBM -- which handle the tabular time-series feature matrices well and train on modest historical datasets. SHAP (SHapley Additive exPlanations) feature importance analysis is applied to every model so your engineering team can see which specific sensor signals and frequency bands are driving each prediction. Confidence-scored predictions with lead time estimates tell your maintenance team how urgently to act -- not just whether a failure is coming, but approximately when.

Real-time monitoring dashboards for equipment health across your plant. Sensor data ingested from PLCs, SCADA systems, and IoT edge devices via OPC-UA, Modbus TCP, or MQTT depending on what each piece of equipment exposes. OPC-UA is preferred where available because it provides structured, typed data with equipment metadata rather than raw register values that require manual mapping.

Raw sensor signals are pre-processed at the edge or ingestion layer to remove noise before storage and analysis. A Kalman filter denoising step handles the sensor noise and transient spikes that characterise vibration and pressure signals in production environments -- without this step, ML models and threshold alerts generate excessive false positives that erode maintenance team trust in the system within weeks.

The monitoring dashboard provides current condition scores, trend visualisations, and anomaly flags at both machine level and fleet level. Alert configuration covers different severity thresholds -- informational (trend to watch), warning (schedule maintenance soon), and critical (immediate action required) -- with configurable escalation routing by equipment criticality and shift. The operational visibility that gives your maintenance team a current picture of equipment health at any point during a shift, not a picture assembled manually from work orders the following morning.

Component-level remaining useful life (RUL) estimation for critical parts -- bearings, seals, drives, cutting tools, and wear components. Estimates based on current condition trends rather than elapsed calendar time, which is the fundamental distinction between predictive and preventive maintenance.

RUL models use Weibull distribution fitting on your historical failure records to characterise the failure time distribution for each component type. The current sensor signature is then used as a condition variable within the Weibull model to produce a probabilistic RUL estimate -- not a single number, but a confidence interval that tells the maintenance team the range within which failure is likely. For bearings, the FFT-derived fault frequency amplitudes provide the most reliable RUL signal; for drives and motors, power consumption trending against baseline is typically the primary indicator.

Replacement scheduling recommendations optimise part life without running to failure -- targeting replacement at 85--90% of estimated life to preserve reliability margin without the waste of replacing components with 40% life remaining on a calendar schedule. Integration with your parts inventory and procurement system, via API connections to SAP MM or equivalent, triggers automated reorder when RUL thresholds are reached so parts are available when the maintenance window arrives rather than ordered in response to an unplanned failure. The shift from fixed replacement intervals to data-driven component management typically extends average component life by 15--30%.

Automated work order creation in your CMMS when predictive models flag maintenance requirements. We integrate with IBM Maximo via the Maximo Application Framework REST API, SAP PM via BAPI and IDoc interfaces, eMaint and UpKeep via REST APIs, and Infor EAM via its standard web services layer. For custom or legacy CMMS platforms, we use database-level integration or file-based import where API access is limited.

Each work order created by the predictive system includes: equipment identifier and location, failure mode predicted, confidence score, supporting sensor data and trend chart, suggested maintenance action based on the fault classification, and required parts based on the component identified. This means the maintenance technician arriving at the machine has actionable context rather than a generic "check equipment" work order.

Post-maintenance data capture closes the feedback loop -- the technician records what was actually found and what was done, which feeds back into the prediction model training pipeline. This is critical for model improvement: the system learns from cases where it predicted correctly, cases where it predicted early, and cases where the predicted failure mode differed from what was found. The closed loop between condition data and maintenance execution is what allows the model to improve accuracy over the 6--18 months following deployment, per McKinsey benchmark data showing 10--40% maintenance cost reduction at mature implementations.

Data ingestion pipelines for high-frequency sensor data from existing equipment instrumentation. OPC-UA is the preferred industrial protocol -- it provides structured, self-describing data with equipment hierarchy metadata and supports both polling and subscription models. Where equipment exposes only Modbus RTU/TCP or raw PLC registers, we build an OPC-UA server layer that maps the registers to structured data types before they enter the analytics pipeline.

For vibration analysis applications, sensors from Brüel & Kjær or PCB Piezotronics operating in the 1--10 kHz range are specified during discovery if existing sensors do not cover the required frequency range for bearing fault detection. IEPE (Integrated Electronics Piezo-Electric) sensors with a compatible data acquisition module are the standard specification. High-frequency samples (typically 25.6 kHz sampling rate) are processed at the edge to FFT spectra before transmission to reduce bandwidth requirements -- sending spectra rather than raw time-series data reduces data volume by 100x or more.

Time-series data is stored in a purpose-built time-series database (InfluxDB or TimescaleDB) optimised for the write throughput and range query patterns that sensor analytics requires. Data quality monitoring on the pipeline itself flags sensor faults -- because a vibration sensor reading a flat 0g signal is either a machine that stopped or a sensor that failed, and both require different responses. A sensor fault that is misread as "normal" by the prediction model is the failure mode that most production predictive maintenance systems encounter eventually.

Statistical and ML-based anomaly detection on process parameters for detecting novel failure modes that have not appeared in your historical failure records. Unsupervised learning approaches -- isolation forests, variational autoencoders, and LSTM-based reconstruction error -- identify unexpected patterns without requiring labelled failure examples. This matters because new failure modes do appear in production environments, especially after equipment modifications, process changes, or material substitutions, and a purely supervised model has no ability to flag them.

The anomaly detection layer runs alongside the supervised failure prediction models. Supervised models handle known failure modes with high specificity; the unsupervised anomaly detector provides broader coverage and catches edge cases that fall outside the training distribution. Early warning alerts surface process parameter deviations before they reach failure thresholds -- typically catching anomalies 3--14 days before a threshold-based alarm would fire, depending on the degradation rate.

Root cause analysis assistance correlates anomalies across sensors to identify likely failure mechanisms. When a combination of elevated motor current draw, increased bearing vibration amplitude, and rising gearbox temperature appear together, the correlation analysis surfaces all three signals simultaneously rather than generating three separate alerts that a technician has to manually connect. Kalman filter-based signal processing removes noise from individual sensor streams before they enter the anomaly detector, reducing false positive rates to a level the maintenance team can sustain without alert fatigue. The detection that catches what rule-based threshold monitoring misses.