An Interpretable Fault Detection Approach for Industrial Processes Based on Improved Autoencoder

Deep learning has recently emerged as a promising method for data-driven fault detection in industrial processes, especially autoencoders (AEs), which have achieved great detection performance. However, the AE models are essentially black boxes, which makes it difficult to interpret and trust the de...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 13
Main Authors:	Ma, Zhen-Lei, Li, Xiao-Jian, Nian, Fu-Qiang
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Autoencoders Decoding Deep support vector data description (deep SVDD) Encoding Error detection Explainable artificial intelligence Fault detection Fault diagnosis Hyperspheres improved autoencoder (AE) Indexes Industries Machine learning Mathematical models memory module Memory modules Modules Reconstruction Representations Vectors
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
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