Detect Equipment Faults
Before They Become Failures.
Machine learning algorithms trained on compressor vibration signatures, defrost cycle behaviour, and power draw patterns i.e., detecting developing equipment faults with enough lead time to plan maintenance interventions.
High-Frequency Ingestion
Streaming vibration data at 10kHz to capture microscopic bearing faults.
Feature Extraction
Computing RMS, Kurtosis, and Skewness to isolate signal from noise.
Inference Engine
Comparing against extensive datasets of labeled training data for classification.
Failure Mode Fingerprints
Our models are trained to recognize specific physics-based signatures of failure.
| Asset Class | Failure Mode | Digital Signature | Prediction Horizon | Model Confidence |
|---|---|---|---|---|
| Screw Compressors | Liquid Slugging | Sudden drop in suction superheat (<2K) + Acoustic spike | Minutes/Hours | High |
| Condenser Fans | Bearing Degredation | High-frequency vibration (2k-5k Hz) exceeding 4g RMS | 2-4 Weeks | High |
| Evaporator Coils | Ice Formation | Gradual decline in Delta-T + Fan motor amperage increase | 24-48 Hours | High |
| Solenoid Valves | Valve Sticking | Inconsistent coil current draw vs. position command | Immediate | High |
Not Just a "Black Box"
We utilize an ensemble approach, combining multiple model architectures to maximize precision. While random forests handle categorical fault classification, LSTM networks track temporal degradation curves.
Random Forest
Classification of distinct failure modes (e.g., "Loose Belt" vs "Unbalanced Load")
LSTM (Long Short-Term Memory)
Time-series forecasting for energy consumption and temperature drift.
Isolation Forest
Unsupervised anomaly detection to find "unknown unknowns" in sensor data.
Vibration Spectrum Analysis
Real-time Fast Fourier Transform (FFT) visualization from a compressor shaft.
> Correlation with elevated bearing temp (72°C)
> CONCLUSION: Misalignment / Soft Foot