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

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.

A fault prognosis method for pressure transmitter based on artificial neural network

“…At present, it is mainly based on periodic maintenance or breakdown maintenance. If the fault prognosis of the pressure transmitter is carried out, the occurrence of the fault can be judged in advance [4][5][6], which will reduce a lot of losses caused by downtime and maintenance, so the fault prognosis of the pressure transmitter is of great significance.…”

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

“…ey used the low-frequency part of the vibration signal and proved its effectiveness in experiments. Li et al [25] extracted bispectral entropy features from the vibration signal of a pump for fault prognosis. e results showed acceptable accuracy in the predictions of the pump's remaining useful life.…”

Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

“…Li et al used the Monte Carlo numerical simulation sampling method to analyze and model the amount of wear in oil on the micro level, and the micro model was applied to the macro process of pump degradation to predict the remaining useful life [10].Lin proposed a method based on the entropy weight and gray prediction to solve the problem of insufficient fault data, which makes the fault prediction of aircraft hydraulic pumps more accurate and objective [11]. Li et al analyzed the characteristic parameters of pump vibration signals and established a DBN model to accurately predict the degradation trend of pumps [12]. A hydraulic pump is a highly reliable mechanical and electrical product [13].…”