1. bookVolume 19 (2019): Issue 5 (October 2019)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network

Published Online: 07 Oct 2019
Volume & Issue: Volume 19 (2019) - Issue 5 (October 2019)
Page range: 195 - 203
Received: 06 Feb 2019
Accepted: 30 Aug 2019
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

Fault prognosis plays a key role in the framework of Condition-Based Maintenance (CBM). Limited by the inherent disadvantages, most traditional intelligent algorithms perform not very well in fault prognosis of hydraulic pumps. In order to improve the prediction accuracy, a novel methodology for fault prognosis of hydraulic pump based on the bispectrum entropy and the deep belief network is proposed in this paper. Firstly, the bispectrum features of vibration signals are analyzed, and a bispectrum entropy method based on energy distribution is proposed to extract the effective feature for prognostics. Then, the Deep Belief Network (DBN) model based on the Restrict Boltzmann Machine (RBM) is proposed as the prognostics model. For the purpose of accurately predicting the trends and the random fluctuations during the performance degradation of the hydraulic pump, the Quantum Particle Swarm Optimization (QPSO) is introduced to search for the optimal value of initial parameters of the network. Finally, analysis of the hydraulic pump degradation experiment demonstrates that the proposed algorithm has a satisfactory prognostics performance and is feasible to meet the requirements of CBM.

Keywords

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