Unsupervised anomaly detection in unbalanced time series data from screw driving processes using k-means clustering

Abstract

Since bolted joints are ubiquitous in manufacturing, their effective and reliable quality assurance is particularly important. Most tightening processes rely on statistical methods to detect faulty screw connections already during assembly. In this paper, we address the detection of faulty tightening processes using a clustering based approach from the field of Unsupervised Machine Learning. In particular, we deploy the k-Means algorithm on a real-world dataset from the automotive industry. The model uses Dynamic Time Warping to determine the similarity between the normal and abnormal tightening processes, treating each one as an independent temporal sequence. This approach offers three advantages compared to existing supervised methods: 1.) time series with different lengths can be utilized without extensive preprocessing steps, 2.) errors never seen before can be detected using the unsupervised approach, and 3.) extensive manual efforts to generate labels are no longer necessary. To evaluate the approach, it is applied in a scenario where actual class labels are available. This allows evaluating the clustering results using traditional classification scores. The approach manages to achieve an accuracy of up to 88.89% and a macro-average F1-score of up to 63.65%.