Predicting Equipment Failures Before They Happen: An AI-Based Predictive Maintenance Scheduler for the Energy and Utilities Sector.
Client Overview
An energy and utilities company operating large industrial assets, facing recurring equipment failures, costly downtime, and inefficient reactive maintenance cycles.
Project Goal
To implement an AI-based Predictive Maintenance Scheduler that forecasts equipment failures, prioritizes maintenance, and automates scheduling.
Approach
We built a machine learning pipeline using historical IoT sensor data to predict potential equipment failures.
The focus was on early warning systems, intelligent task creation, and clear visual dashboards for maintenance teams.
Solution
- Data Integration: ETL pipeline ingests and cleans sensor data from multiple sources.
- ML Forecasting Model: LSTM neural network predicts failure probability per machine.
- Risk Scoring: Each asset receives a live risk score and maintenance urgency level.
- Automated Tasking: High-risk cases trigger Trello or Slack tasks for the assigned technician.
- Visualization: Grafana dashboards display real-time equipment health and maintenance history.
Value Delivered
The system transformed maintenance operations from reactive firefighting to proactive planning — reducing breakdowns, improving efficiency, and extending asset lifespan.
Quantified Outcomes
| Metric | Before | After |
|---|---|---|
| Unplanned downtime | 14% | 3% |
| Maintenance cost | 100% baseline | 68% |
| Equipment lifespan | 5 years | 6.5 years |
