A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation

Journal article

Liang, X., Duan, F., Bennett, Ian and Mba, P.D. (2020). A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation. Applied Sciences. 10 (19), p. e6789. https://doi.org/10.3390/app10196789
AuthorsLiang, X., Duan, F., Bennett, Ian and Mba, P.D.
AbstractPumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops a fault detection and isolation scheme based on an unsupervised machine learning method, sparse autoencoder (SAE), and evaluates the model on industrial multivariate data. The Mahalanobis distance (MD) is employed to calculate the statistical difference of the residual outputs between monitoring and normal states and is used as a system-wide health indicator. Furthermore, fault isolation is achieved by a reconstruction-based two-dimensional contribution map, in which the variables with larger contributions are responsible for the detected fault. To demonstrate the effectiveness of the proposed scheme, two case studies are carried out based on a multivariate data set from a pump system in an oil and petrochemical factory. The classical principal component analysis (PCA) method is compared with the proposed method and results show that SAE performs better in terms of fault detection than PCA, and can effectively isolate the abnormal variables, which can hence help effectively trace the root cause of the detected fault.
Keywordssparse autoencoders; unsupervised learning; multivariate data; fault detection; pump
Year2020
JournalApplied Sciences
Journal citation10 (19), p. e6789
PublisherMDPI
ISSN2076-3417
Digital Object Identifier (DOI)https://doi.org/10.3390/app10196789
Publication dates
Online28 Sep 2020
Publication process dates
Accepted23 Sep 2020
Deposited20 Oct 2020
Publisher's version
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File Access Level
Open
Licensehttps://creativecommons.org/licenses/by/4.0/
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