Predictive Maintenance ML: Equipment Failure Forecasting

I build machine learning models that predict equipment failure before it happens, so your team can fix machines on schedule instead of scrambling after a breakdown. From raw sensor streams and maintenance logs, I engineer features from time series — rolling statistics, spectral signatures, and degradation trends — then train models that estimate remaining useful life and flag anomalies as they emerge. The result is an early, trustworthy warning that a bearing, pump, motor, or compressor is drifting toward trouble, with lead time to plan the fix.

My work covers the full path from data to decision. I clean and align messy sensor histories, handle gaps and drift, label sparse failure events, and validate models against real downtime so the numbers reflect the floor. I tune alert thresholds to your tolerance for false alarms, because a model nobody trusts gets switched off.

You get calibrated failure probabilities, remaining-useful-life estimates, and anomaly alerts you can route into the tools your technicians already use. I document every assumption, hand over reproducible code, and stay available to retrain as your equipment and conditions change.

— Feature engineering from vibration, temperature, current, and pressure signals
— Remaining-useful-life estimation with confidence ranges
— Unsupervised anomaly detection for assets with little failure history
— Threshold tuning, backtesting, and clear handover documentation