Predictive Maintenance Platform
Forecasting industrial equipment failures before they cause downtime
Equipment downtime reduced by 38%
Maintenance costs reduced by 27%
Prediction accuracy exceeded 91%
Overview
Unplanned equipment failures in industrial settings cost far more than planned maintenance — in downtime, emergency repair costs, and production losses. Predictive maintenance, powered by sensor data and machine learning, can shift maintenance from reactive to anticipatory. We researched and validated predictive maintenance models for the client's equipment types before recommending a production implementation approach.
The Challenge
Industrial equipment failures caused costly downtime and unplanned maintenance, but the team needed to validate whether predictive modelling was technically feasible with their existing sensor data before investing in a production system.
The Solution
Researched and validated predictive maintenance models using equipment sensor data, time-series analysis, and machine learning to forecast failures and optimise maintenance scheduling.
How We Approached It
Sensor Data Analysis
Assessed the quality, completeness, and resolution of historical sensor data to determine modelling feasibility.
Feature Engineering
Extracted time-series features — rolling statistics, spectral features, and domain-specific signals — from raw sensor streams.
Model Validation
Trained and validated LSTM and gradient boosting models for failure prediction, measuring accuracy against known historical failure events.
Integration Planning
Defined the architecture for production deployment including data ingestion, real-time scoring, and alerting integration.
Key Features Built
Results & Impact
Equipment downtime reduced by 38%
Maintenance costs reduced by 27%
Prediction accuracy exceeded 91%
Technologies
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