Inaugural Addresses (Industrial Engineering)

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Browsing Inaugural Addresses (Industrial Engineering) by Subject "Cluster analysis"

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    Predictive maintenance using clustering methods for the use-case of bolted connections in the automotive industry
    (Stellenbosch : Stellenbosch University, 2023-03) Bekker, Emmarentia Lydia; Matope, Stephen; Grobler, Jacomine; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: The rise of Industry 4.0 and the implementation of “smart machines” have opened new opportunities to utilise all the data gathered from machines in an automotive manufacturing environment. A big obstacle faced by maintenance staff is understanding the data retrieved from the machines as the volume is often overwhelming and needs to be processed to be fully useful. The most prevalent model found in predictive maintenance is data-driven and based on statistical process control, pattern recognition, or machine learning algorithms. This thesis explores the feasibility of predictive maintenance, using clustering, specifically for the use case of bolted connections using electronically controlled nutrunners in a modern, high-volume manufacturing environment. The data for this study is collected from the wheel bolting machine of an automotive factory and consists of process parameters already recorded by the current system. The data retrieved from the system is unlabeled, time-series based and records the torque, angle, and time from the bolting process. During this period, the failure rate of one nutrunner was significantly higher than the others. After changing the socket, the machine showed a 2% improvement and the bolting graphs returned to the expected format. This thesis aims to retrospectively establish if the failure was predictable from the data by using clustering algorithms. The study includes a critical analysis of the mechanical machine-train of the nutrunner system based on literature and domain knowledge. A failure analysis is done to understand the key characteristics of common failures as identified on the bolting process curves. Due to the format of the data, a comprehensive data exploration phase had to be conducted to find and understand the outliers and data quality. Furthermore, the required features for the dataset used during modelling had to be designed. The clustering algorithms investigated are agglomerative hierarchical clustering (AHC), density-based spatial clustering of applications with noise (DBSCAN) and a selforganising feature map (SOFM). Each algorithm underwent extensive parameter optimisation and fine-tuning in order to establish the best clusters. The performance metrics used to compare and evaluate the clusters were the silhouette coefficient score (SC) and variation rate criterion (VRC). The SOFM clustering performed the best and the resulting clusters were used to further perform clustering analysis. The cluster analysis showed promising results as the clusters were well-defined and decipherable using domain knowledge. The results of this thesis show that it is feasible to use clustering to improve the maintenance strategy of nutrunners in the automotive industry.