TinyML-Based Predictive Maintenance System for Low-Cost Industrial Equipment

Small and medium enterprises run large numbers of low-cost motors, pumps, fans and compressors that are maintained only when they break down, because commercial condition-monitoring systems cost more than the machines themselves. This paper presents the design of a TinyML-based predictive maintenance system that runs the complete analysis on a microcontroller attached to each machine. An ESP32-S3 samples a three-axis ADXL345 accelerometer at 3200 Hz, a clamp-on current sensor and a casing temperature sensor, computes 113 time- and frequency-domain features every 0.32 s, and scores them with an 8-bit quantized autoencoder trained only on data from the healthy machine. The anomaly score is smoothed into a health index that drives green, amber and red alerts on the device, while only a 100-byte summary is sent over MQTT every 10 s; a velocity check against ISO 20816 zones and an optional fault classifier run in parallel. A review of 17 related studies shows that most TinyML fault detectors are trained and tested on laboratory datasets with labelled faults, which are rarely available for a specific low-cost machine. Design analysis shows that the autoencoder has 7,929 parameters and needs about 7,700 multiply– accumulate operations per window, that the signal buffers and model fit in under 30 KB of the ESP32-S3's 512 KB SRAM, and that on-device inference cuts the data sent from 1.66 GB to 0.86 MB per machine per day. A validation protocol using a motor test rig with seeded faults and public bearing datasets is provided.