Preventing Mechanical Failures: Industrial Audio-Based Anomaly Detection Systems

Problem & Motivation: Today mechanical failures often cause on-the-job accidents and injuries and prompt prevention of these failures is vital for workplace safety. According to the U.S. Occupational Safety and Health Administration (OSHA), heavy equipment and heavy machinery accidents are among the leading causes of fatal and serious accidents that take place on construction sites. Therefore, the question we want to answer is: how can we leverage machine-generated audio data to help prevent mechanical failures in order to minimize these on-the-job accidents and injuries?

Data Source & Data Science Approach: a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). We implement the YOLOv8 (You Only Look Once) model which is a state-of-the-art (SOTA) computer vision model which performs remarkably well for image classification tasks. Our approach consists of taking the labeled audio files between normal and abnormal sounds to generate the images corresponding to the audio file’s mel spectrogram representations. We use these images to train four different types of models for fans, pumps, guard rails, and valves.

Evaluation: The YOLOv8 model is highly performant and efficient. The performance metrics on F1 scores, precision, recall, and accuracy are above 98% on each of the test sets.

Key Learnings & Impact: Using the MIMII dataset we have demonstrated that we can provide a successful machine learning solution for factory maintenance technicians to increase workplace safety. According to the U.S. Occupational Safety and Health Administration (OSHA), heavy equipment and heavy machinery accidents are among the leading causes of fatal and serious accidents that take place on construction sites. Given the results from our work, we believe that we can help minimize accidents and deaths by providing this service to factory maintenance technicians.

Acknowledgements: Fred Nugen, Danielle Cummings, Jason Rudianto