Abstract
Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality. At the same time, it plays a great role in several other domains due to the fact that anomaly may reveal rare but represent an important phenomenon. The objective of this paper is to detect anomalies and identify the possible variables that caused these anomalies on historical assembly data for two series of products. Multiple anomaly detection techniques were performed; HBOS, IForest, KNN, CBLOF, OCSVM, LOF, and ABOD. Moreover, we used AUROC and Rank Power as performance metrics, followed by Boosting ensemble learning method to ensure the best anomaly detectors robustness. The techniques that gave the highest performance are KNN, ABOD for both product series datasets with 0.95 and 0.99 AUROC respectively. Finally, we applied a statistical root cause analysis on the detected anomalies with the use of Pareto chart to visualize the frequency of the possible causes and its cumulative occurrence. The results showed that there are seven rejection causes for both product series, whereas the first three causes are responsible for 85% of the rejection rates. Besides, assembly machines engineers reported a significant reduction in the rejection rates in both assembly machines after tuning the specification limits of the rejection causes identified by this research results.
Highlights
IntroductionIn Manufacturing Industry, Assembly machines are considered as essential components and are widely used in the production lines
Manufacturing Industry has been adopting new quality measurement tools that led to an intensive-data environment, paving the way for using Machine Learning (ML) methods to extract information from the data as an endeavor to reduce the production cost and enhance the product quality [1].In Manufacturing Industry, Assembly machines are considered as essential components and are widely used in the production lines
We developed a machine learning model using several anomaly detection techniques such as; Histogram-based Outlier Score (HBOS), Isolation Forest (IForest), k-Nearest Neighbors Detector (KNN), Cluster-Based Local Outlier Factor (CBLOF), One-Class Support Vector Machines (OCSVM), Local Outlier Factor (LOF), and Angle-based Outlier Detection (ABOD) to detect the anomalies in the assembly line for 54104, 54132 product series and to identify the possible variables that caused these anomalies by performing a root cause analysis on the product series anomalies
Summary
In Manufacturing Industry, Assembly machines are considered as essential components and are widely used in the production lines. To ensure the final product quality, each assembly machine has an integrated inspection system that is supported by high-speed vision cameras to measure each assembled piece’s dimensions. Each assembly machine has different specification limits depending on the design of the product to be assembled [2]. The final product quality is ensured bypassing the product design specification limits to the inspection system in the assembly machine and after the inspection system measuring each assembled part dimensions. In case any part’s measurements exceeded the design specification limits, the assembled part will be considered as an anomaly and automatically rejected and thrown to the trash [2], [3]
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