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• W3088553651 abstract "Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequency-domain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson’s correlation-BPSO ( $rho $ -BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filter-wrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed $rho $ -BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods." @default.
• W3088553651 created "2020-10-01" @default.
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• W3088553651 date "2020-01-01" @default.
• W3088553651 modified "2023-10-16" @default.
• W3088553651 title "A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features" @default.
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• W3088553651 doi "https://doi.org/10.1109/access.2020.3025909" @default.
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