Automated electrical motor quality control via machine learning based vibration analysis

Abstract

Industry 4.0 aims at the digital transformation to increase the reliability and capacity of production. With the integration of Sensor Analytics (SA) and Artificial Intelligence (AI) to manufacturing, the design of automated and optimized processes becomes more accessible. One of the areas where AI tools and SA are used is quality control tests of products. The main target of this thesis is to automate the quality control step based on vibration analysis by finding mechanical failures of Brushless Direct Current (BLDC) motors as an example of an AI-powered sensor analytics application. In addition, the feasibility and assessment of popular machine learning models are investigated. Two architectures are proposed to classify motors' quality. These methods are called sAIQC, Single-Stage AI-Powered Quality Control, and dAIQC, Double-Stage AI-Powered Quality Control. In the first method, motors are classified into healthy (pass) or faulty (fail), regardless of the data quality of the signal. The second proposed method is composed of two stages. The first stage makes a binary classification based on data quality, and then, the separated groups are classified at two independent classifiers in the second stage as pass or fail. Unweighted accuracy (UA), defined as the average accuracy of each class, is used as a performance metric of the classifiers. In experiments with the dataset containing 671 samples, the performance of sAIQC method was 84.9%; this performance with the dAIQC method was increased to 92.9\%. Furthermore, in experiments using big data set consisting of 25580 vibration recordings and without a data quality label, the performance of the SAIQC method is 73.5% percent. In contrast, the performance of the dAIQC method is 89.5\% percent.