A Variable Projection-Based Algorithm for Fault Detection and Diagnosis

Chen, Guang-Yong; Xu, H.J.; Gan, Min; Chen, C. L. Philip

doi:10.1109/tim.2023.3298636

IEEE Trans. Instrum. Meas.

2023

DOI: 10.1109/tim.2023.3298636

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A Variable Projection-Based Algorithm for Fault Detection and Diagnosis

Guang-Yong Chen

H.J. Xu

Min Gan

et al.

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Cited by 3 publications

References 44 publications

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Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

Wei,

Qi,

Liu

et al. 2024

Journal of Chemometrics

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To address the challenges of extracting features from complex industrial process data, the reliance of numerous fault detection methodologies on presupposed data distribution types, and the limited generalization capacity of fault detection, this manuscript introduces a sophisticated algorithm for industrial process fault detection. This algorithm harnesses the information gain adaptive (IGA) technique for feature selection and a synergistic model decision mechanism. Initially, the process involves the computation of information gain via decision trees, coupled with the determination of the value through cross‐validation. This strategy enables the adaptive selection of features, thereby facilitating data dimensionality reduction and effective feature extraction. The subsequent phase introduces a ternary statistical measure monitoring group for the detection of linear faults, while autoencoders and one‐class SVM methodologies are applied for the monitoring of nonlinear faults. The culmination of this approach is the development of an innovative weighted decision mechanism, designed to amalgamate the findings from both linear and nonlinear detection avenues, yielding more dependable detection results. The validation of this algorithm employs datasets from the water chillers process and Tennessee Eastman (TE) process, demonstrating the IGA‐combined model's superior performance over isolated linear or nonlinear detection algorithms in terms of detection accuracy and robustness. Notably, the efficacy of this method is not contingent upon specific assumptions regarding data distribution, rendering it a versatile and efficacious tool for the fault detection in industrial processes.

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Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

Wei,

Qi,

Liu

et al. 2024

Journal of Chemometrics

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Battery state of health estimation using variable separation algorithm based on quasi-Newton

Wang,

Chen,

Cheng

et al. 2024

Ionics

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HAAC: Hierarchical audio augmentation chain for ACCDOA described sound event localization and detection

Wang

et al. 2023

Applied Acoustics

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A Variable Projection-Based Algorithm for Fault Detection and Diagnosis

Cited by 3 publications

References 44 publications

Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

Battery state of health estimation using variable separation algorithm based on quasi-Newton

HAAC: Hierarchical audio augmentation chain for ACCDOA described sound event localization and detection

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