Click Event Sound Detection Using Machine Learning in Automotive Industry

Artificial intelligence has been playing an important role when it comes to the automotive industry and its quality of assemblies in the production line, this is because since the arrival of the industry 4.0 it has been subject to change and continuous improvement. In the past, we've observed how many machine learning architectures have been used to create environmental sound classification systems in order to improve traditional systems, thus overcoming efficiency issues with great results. In this work, we present a machine learning solution/approach for click event sound detection using audio sensors that are used in the assembly of electric harnesses for engines, this being done on an automotive production line, where we divided our workflow into: data collection, pre-processing, feature extraction, training and inference and finally the detection of the click event sounds. We created a dataset that is composed by 25,000 audio files that have an average duration of 0.025 seconds per click sound with the purpose of training a Multi-layer Perceptron and bring it into the inference phase. In order to test this approach, we've performed various implementations in a laboratory and in the real automotive industry. We obtained 95.23% in F1-Score Metric in a laboratory, while in real conditions, we obtained less reliable results, as 84.00% as the best results.