Low-cost edge AI fault diagnosis system for rotating machinery based on ESP32-S3

Abstract

Within the framework of Industry 4.0, the implementation of real-time and economical fault diagnostics for rotating machinery is vital for predictive upkeep. Conventional cloud-reliant strategies grapple with drawbacks like excessive latency and substantial bandwidth needs. At the same time, specialized diagnostic apparatus is typically too expensive for smaller enterprises. This work introduces VibeSense, a remarkably inexpensive edge AI platform designed for the instantaneous identification of imbalance faults in rotating machinery. The described setup employs an ESP32-S3 microcontroller paired with an MPU6050 MEMS accelerometer, keeping the entire hardware expenditure below 50 RMB (approx. 7 USD), which is, to the best of our knowledge, one of the lowest-cost edge AI solutions for fault diagnosis reported to date. The system executes on-device Fast Fourier Transform (FFT) for spectral vibration evaluation and derives an 11-parameter feature array covering time-domain and frequency-domain attributes. A streamlined Random Forest classifier, containing 50 decision trees, undergoes training and is subsequently installed straight onto the ESP32-S3 via translation into C-based if-else branching code. This tactic considerably lowers memory consumption relative to conventional TinyML solutions. Testing outcomes indicate a fault identification success rate of 100% on artificial datasets and upwards of 95% during actual fan-based trials. The minimal expense, straightforward implementation, and precision of the platform underscore its promise as a feasible and expandable edge AI instrument for industrial predictive servicing. All source code and documentation have been open-sourced and are publicly available at https://github.com/vibezhang/ VibeSense.