Sound-Based Industrial Machine Malfunction Identification System by Deep Learning Approach

Malfunctions in industrial machines pose significant challenges and require timely identification to prevent disruptions in manufacturing processes. Traditional methods of detection involve manual inspection by technicians, which are often inefficient. Nowadays, sound processing technology has been introduced as a potential way of identifying malfunctioning machines by integrating automated systems. This work offers a new system aimed at detecting improper functioning of industrial machines employing the sound processing technology. The Mel-filterbank energy (MFE) technique is used for feature extraction because it has been proven to perform well in non-voice acoustic scenarios. The classification process incorporates Convolutional Neural Network (CNN) procedures to improve the accuracy of the malfunction detection. The machine learning model is developed on the Edge Impulse platform and subsequently embedded in an ESP32 microcontroller, enabling real-time processing at the edge. To evaluate the system's performance, the publicly available Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset is utilized, comprising four distinct types of industrial machines: fan, valve, pump, and slide rail. Each machine type consists of two classes, normal and abnormal. The system is controlled and monitored through an Android smartphone application via Bluetooth communication. Experimental tests with 10 normal and 10 abnormal samples for each machine type demonstrate promising results: 80% accuracy for fan malfunctions, 75% for valve malfunctions, 95% for pump malfunctions, and 100% for slide rail malfunctions. The overall computing time is measured at 213 ms, highlighting the efficiency and real-time capabilities of the proposed system. This research provides an opportunity to discover various methods of industrial machine malfunction detection, except conventional visual inspection. The combination of sound processing technology, MFE feature extraction and CNN classification on edge devices appears to be a more practical approach with possibilities in numerous industrial fields.