Predictive Maintenance Using AI and ML

The predictive maintenance system functions by continuously capturing acoustic and vibrational data from a running machine or motor using a connected microphone (such as a voice bonnet, USB, or Bluetooth mic). This audio and vibrational stream is processed by the Raspberry Pi 5 running Python and Node.js software utilities like SoX and GStreamer to handle stream capture and audio routing. The Edge Impulse Linux SDK runs the trained machine learning model locally on the edge device. Instead of relying on traditional threshold-based alerts, the ML model evaluates the frequency spectrum and sound patterns in real time, comparing them against learned baseline operational states. When the model detects anomalies or audio signatures indicative of mechanical wear, bearing failure, or impending component degradation, the system triggers predictive maintenance alerts. This edge-based architecture allows for continuous, low-latency health monitoring directly at the machinery without requiring constant cloud connectivity.