Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data

Taehoon Ko (Seoul National University, Gwanak-gu, Republic of Korea)
Je Hyuk Lee (Seoul National University, Gwanak-gu, Republic of Korea)
Hyunchang Cho (Seoul National University, Gwanak-gu, Republic of Korea)
Sungzoon Cho (Seoul National University, Gwanak-gu, Republic of Korea)
Wounjoo Lee (Doosan Infracore Co. Ltd, Seoul, Republic of Korea)
Miji Lee (Doosan Infracore Co. Ltd, Seoul, Republic of Korea)

Industrial Management & Data Systems

ISSN: 0263-5577

Publication date: 12 June 2017

Abstract

Purpose

Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines.

Design/methodology/approach

By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset.

Findings

By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped.

Originality/value

Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

Keywords

Citation

Ko, T., Lee, J., Cho, H., Cho, S., Lee, W. and Lee, M. (2017), "Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data", Industrial Management & Data Systems, Vol. 117 No. 5, pp. 927-945. https://doi.org/10.1108/IMDS-06-2016-0195

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Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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