Predictive Maintenance
IoT sensors on factory floors feed real-time acoustic and vibrational data to an AI model that predicts machine failure 2 weeks in advance.
Project Details
A full breakdown of the features and modules delivered as part of this project.
Category
Manufacturing
Year
2022
Duration
7 Months
Stack Size
6 Technologies
Key Features
6 ModulesHigh-frequency IoT sensor integration capturing acoustic and vibrational signatures from CNC machines.
PyTorch LSTM model trained to detect acoustic anomaly precursors up to 2 weeks before failure.
Real-time time-series data storage and querying via InfluxDB for sub-millisecond alerting.
Individual machine health scoring dashboard with predicted remaining lifespan estimates.
Automated maintenance work order creation integrated with the client's existing ERP system.
Spare parts inventory optimization engine suggesting purchase orders based on predicted failure timelines.
Key Results & Impact
Measurable business outcomes delivered through this project.
Reduced unplanned machine downtime by an incredible 75%.
Saved the facility $2.1M annually in lost production revenue and emergency repairs.
Optimized the spare parts inventory by 30% through predictive ordering.
Extended average machine operational lifespan by 15%.