A fault diagnosis approach for autonomous underwater vehicle thrusters using time-frequency entropy enhancement and boundary constraint–assisted relative gray relational grade

Abstract

This article presents a novel thruster fault diagnosis approach for an autonomous underwater vehicle. In the novel approach, a time-frequency entropy enhancement is used to extract feature, and then a boundary constraint–assisted relative gray relational grade is applied to identify thruster fault. The time-frequency entropy enhancement is developed from the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy. First, the energy distributions of autonomous underwater vehicle dynamic signals are given in the time-frequency plane. And then the energy concentration in the energy distribution is enhanced based on a serial signal processing, including wavelet decomposition, modified Bayes’ classification algorithm, and two dimensional convolution operation, successively. After that the Shannon entropy of the energy distribution is calculated. The boundary constraint–assisted relative gray relational grade comes from the gray relational analysis. A mapping function between the relative gray relational grade and the fault severity is established. And then the boundary constraints of relative gray relational grades at each standard fault level are determined. Moreover, the mapping function is modified based on the boundary constraints. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results demonstrate the effectiveness of the developed approaches in terms of improving the sensitivity of the fault feature to the fault severity, compared with the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy, and increasing the identification accuracy, compared with the gray relational analysis.

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

“…In order to solve the problem of insufficient sensor data from the working engine, information entropy and deep belief networks are used for gas turbine engine fault diagnosis in [148]. A method based on time-frequency entropy enhancement and boundary constraint assisted relative gray relational grade are applied for fault diagnosis for an autonomous underwater vehicle, as given in [149]. Shannon entropy is used to measure the uniformity of exhaust temperature and vibration data in [144,150].…”

“…In [159], singular spectrum entropy, power spectrum entropy, and approximate entropy are extracted in vibration signals by Shannon entropy, and the feature fusion model is constructed to classify and diagnose the fault signals. Chen et al [145] variational mode decomposition + energy entropy 3 Tang et al [146] manifold learning + Shannon wavelet support vector machine 4 Xiao et al [147] dual-tree complex wavelet transform + energy entropy 5 Feng et al [148] information entropy + deep belief networks 6 Yin et al [149] time-frequency entropy enhancement + boundary constraint assisted relative gray relational grade 7 Chen et al [150] ensemble multiwavelet + Shannon entropy 8…”

Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

“…An autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment. As an important part of AUV, the thruster directly determines the efficiency and safety with strong working intensity for AUV, However, the thruster fault usually happens in engineering practice [1,2]. Therefore, how to make thruster fault diagnosis and fault tolerant control for AUV is the premise for completing underwater missions [3,4].…”

A fault diagnosis method for underwater thruster based on RFR-SVM

“…The thruster fault diagnosis based on signal processing mainly include neural network, 10,11 principal component analysis (PCA), 12,13 gray model, 14,15 support vector machine (SVM), [16][17][18][19][20] etc. Zheng et al 16 proposed a rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and SVM.…”