An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Xin Li, Fengrong Bi, Lipeng Zhang, Xiao Yang and Guichang Zhang
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Xin Li: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Fengrong Bi: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Lipeng Zhang: Motorcycle Design Institute, Tianjin Internal Combustion Engine Research Institute, Tianjin 300072, China
Xiao Yang: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Guichang Zhang: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China

Energies, 2022, vol. 15, issue 3, 1-17

Abstract: This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.

Keywords: echo state networks (ESNs); multi-verse optimizer (MVO); fault detection; deep learning; engine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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