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Detect Equipment Failures Before They Happen
Unplanned downtime is costly. Our predictive maintenance solution enables continuous, on-device machine health monitoring, identifying anomalies before failures occur. By running entirely at the edge, systems gain real-time insights without cloud latency, connectivity dependencies or data-privacy concerns.
This solution is optimized for both retrofitting to existing machines and for new equipment designs, allowing engineers to deploy scalable predictive maintenance across a wide range of industrial assets.
Designed to Solve Real-World Problems
Early Fault Detection
By identifying anomalies before they escalate into failures, systems can shift from reactive repairs to planned, condition-based maintenance.
Edge-Based Analytics
Local processing ensures deterministic response times while maintaining operation even in disconnected or bandwidth-constrained environments.
Low-Power Operation
Efficient on-device inference allows systems to remain always-on without impacting energy budgets or thermal constraints.
Scalable and Retrainable
Models can be adapted with new data to support varying equipment types, operating conditions, and deployment requirements without redesigning hardware.
Production-Ready Implementation
Delivered as a turnkey firmware solution that runs entirely on a low-power microcontroller, our predictive maintenance solution eliminates the need to build or train models from scratch. Well-defined interfaces and tooling simplify integration into existing applications, enabling faster evaluation and deployment.
Key Advantages
Supports vibration, temperature and current (Iq) sensing
Continuous monitoring on energy-constrained systems
Retrainable ML models for different assets and use cases
Optimized power management for long-term deployment
Production-ready reference design with integrated security
From Data to Deployment
Sensor data is captured from machines in their operating environment and used to train or select an appropriate machine learning model. The resulting model is then deployed at the edge, enabling real-time condition monitoring without reliance on cloud connectivity.
Smart Sensor Analytics
Sensor systems can detect aging and environment-driven degradation, predict and prevent system failures and provide early warning for hazardous leaks to ensure optimal safety and efficiency.
Explore Our Other AI-Based Solutions
Microchip’s Edge AI portfolio extends beyond audio to include intelligent facial recognition interfaces, predictive maintenance, and safety-critical monitoring. These solutions are optimized for real-world deployment, delivering secure, low-power inference at the edge without reliance on the cloud.
Always-On Keyword Spotting
Our keyword spotting solution is a low-power audio inference solution that enables always-on wake word detection and command recognition, delivering fast response times without streaming audio to the cloud.
Facial Recognition with Liveness Detection
Our facial recognition with liveness detection solution enables accurate, secure, and fully on-device facial recognition, eliminating reliance on the cloud.
Arc Fault Detection
Our arc fault detection solution is a safety-critical edge AI solution that identifies hazardous arc faults in Alternating Current (AC) and Direct Current (DC) systems in real time, enabling faster fault detection and improved system protection with minimal false positives.