Unsupervised anomaly detection in unbalanced time series data from screw driving processes using k-means clustering
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Procedia CIRP, 2023, 120, pp. 1185-1190
- Issue Date:
- 2023
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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
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