A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains

Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various statis...

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Bibliographic Details
Published in:Supply Chain Analytics Vol. 2; p. 100011
Main Author: Lahmiri, Salim
Format: Journal Article
Language:English
Published: Elsevier 01.06.2023
Subjects:
Discriminant analysis
Fault diagnosis
K-Nearest Neighbor algorithm
Kernel naïve Bayes
Machine learning
Support vector machine
ISSN:2949-8635, 2949-8635
Online Access:Get full text
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Summary:Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various statistical machine learning systems in modeling large data in the condition monitoring of electric drive trains in supply chains. Large data is used to train linear discriminant analysis, K-nearest neighbor algorithm, naïve Bayes, kernel naïve Bayes, decision trees, and support vector machine to distinguish between eleven fault states. The experimental results from the testing data set show that the decision trees achieved 93.8% accuracy, followed by kernel naïve Bayes (91.9%), radial basis function (Gaussian) support vector machine (89.3%), linear discriminant analysis (84.5%), k-NN algorithm (80.5%), and Gaussian naïve Bayes (71.3%). Accordingly, the choice of statistical machine learning algorithm influences classification accuracy related to electric drive fault diagnosis. In addition, decision trees take only few seconds to learn and classify new instances from big data. This makes the selection of decision trees trivial for condition monitoring and management of electric drive trains.
ISSN:2949-8635
2949-8635
DOI:10.1016/j.sca.2023.100011