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Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

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Abstract

Targeting the mode-mixing problem of intrinsic time-scale decomposition (ITD) and the parameter optimization problem of least-square support vector machine (LSSVM), we propose a novel approach based on complete ensemble intrinsic time-scale decomposition (CEITD) and LSSVM optimized by the hybrid differential evolution and particle swarm optimization (HDEPSO) algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components (PRCs) and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.

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Authors and Affiliations

  1. State Key Laboratory of Engines, Tianjin University, Tianjin, 300072, China

    Jun-hong Zhang & Yu Liu

  2. China Automotive Technology and Research Center, Tianjin, 300300, China

    Yu Liu

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  1. Jun-hong Zhang
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  2. Yu Liu
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Correspondence to Yu Liu.

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Project supported by the National High-Tech R&D Program (863) of China (No. 2014AA041501)

ORCID: Yu LIU, http://orcid.org/0000-0003-0946-4488

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Zhang, Jh., Liu, Y. Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines. Frontiers Inf Technol Electronic Eng 18, 272–286 (2017). https://doi.org/10.1631/FITEE.1500337

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