ML prediction of equipment failures from sensor data

I create machine learning models that predict equipment failure in advance, so you can schedule repairs instead of emergency fixes after a breakdown. Starting with raw sensor streams and maintenance logs, I engineer features from time series — rolling statistics, spectral characteristics, degradation trends — then train models that estimate remaining useful life and detect anomalies as they emerge. You get early and reliable warning that a bearing, pump, motor, or compressor is heading toward failure, with time to plan repairs.

I own the entire path from data to deployment. I clean and synchronize heterogeneous sensor histories, handle missing data and drift, label rare failure events, and validate models against real downtime so numbers reflect your actual floor. I calibrate alert thresholds to your tolerance for false positives: a model you don't trust gets turned off.

You receive calibrated failure probabilities, remaining useful life estimates, and anomaly alerts that feed into tools your technicians already use. I document every assumption, deliver reproducible code, and stay involved to retrain models as equipment evolves.

— Features from vibration, temperature, current, and pressure signals
— Remaining useful life estimation with confidence intervals
— Anomaly detection without labels for components with rare failures
— Threshold tuning, backtesting, and clear handover documentation