Core Service
Predictive Analytics
Equipment failure forecasting using ML models on IoT and operational data
Overview
Predictive analytics transforms reactive maintenance into proactive, data-driven strategies that minimize downtime and reduce costs. Our solutions integrate IoT sensor data, operational logs, and historical maintenance records to forecast equipment failures days or weeks in advance, enabling optimized maintenance scheduling and spare parts inventory management.
Key Features
Survival Analysis & Cox Models
Statistical models that handle censored data and estimate failure probabilities over time, accounting for operating conditions and maintenance interventions.
RUL Estimation & MTBF
Remaining Useful Life predictions and Mean Time Between Failures analysis for condition-based maintenance planning and asset lifecycle management.
Digital Twin Integration
Connect predictive models to digital twin simulations for scenario analysis, what-if planning, and maintenance strategy optimization.
IoT Data Pipeline
Real-time data ingestion, preprocessing, and feature engineering from diverse sensor types and industrial protocols.
Technical Approach
Our predictive analytics methodology delivers actionable insights:
- Data Integration: Consolidate sensor data, SCADA systems, CMMS logs, and environmental factors
- Feature Engineering: Extract degradation indicators, trend features, and contextual variables from raw sensor streams
- Model Development: Build ensemble models combining survival analysis, gradient boosting, and LSTMs
- Validation: Backtesting on historical failure events and prospective validation on live systems
- Deployment: Real-time scoring with alert generation and maintenance work order integration
Use Cases
Predictive analytics prevents costly failures across industries:
- Manufacturing: Predict bearing failures, motor breakdowns, and conveyor malfunctions to avoid production disruptions
- Energy & Utilities: Forecast turbine degradation, transformer failures, and grid component health
- Transportation: Anticipate vehicle component failures for fleet maintenance optimization
- Oil & Gas: Predict pump cavitation, compressor issues, and pipeline integrity risks
Expected Outcomes
Predictive analytics delivers measurable maintenance improvements:
- 30-50% reduction in unplanned downtime through early failure detection
- 20-30% maintenance cost savings from optimized scheduling and parts inventory
- 10-20% asset lifetime extension through condition-based interventions
- 7-14 day failure prediction horizons for planned maintenance windows
Ready to Prevent Failures?
Let's discuss how predictive analytics can optimize your maintenance strategy and reduce costs.