Abstract:A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convolution operation re… Show more
“…Based on the mapping relationship, a fault severity between fault levels can be gotten. 17 However, when UMV locates at one of SMVs, the LSGRG’s identification accuracy is lower than the GRA’s. To get the reasons, a few extra experiments are conducted according to the flow chart of the LSGRG, as illustrated in Figure 9.…”
Section: Fault Diagnosis Systemmentioning
confidence: 99%
“…To get the reasons, a few extra experiments are conducted according to the flow chart of the LSGRG, as illustrated in Figure 9. 17…”
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.
“…Based on the mapping relationship, a fault severity between fault levels can be gotten. 17 However, when UMV locates at one of SMVs, the LSGRG’s identification accuracy is lower than the GRA’s. To get the reasons, a few extra experiments are conducted according to the flow chart of the LSGRG, as illustrated in Figure 9.…”
Section: Fault Diagnosis Systemmentioning
confidence: 99%
“…To get the reasons, a few extra experiments are conducted according to the flow chart of the LSGRG, as illustrated in Figure 9. 17…”
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.
“…The detailed description of beaver II is given in Zhang et al. 15 Figure 11.Thruster configuration of the beaver II.Figure 12.Physical layout of the beaver II AUV.Figure 13.The experimental data with the first group parameters.…”
Section: Experimental Validationsmentioning
confidence: 99%
“…The derivation of equation (14) is given in Zhang et al. 15 where V′ is the control voltage applied to the thruster driver actually, V is the control voltage output by the controller, λ is the fault degree.…”
This article presents a novel thruster fault degree identification method for autonomous underwater vehicle. The novel method is developed from the fuzzy support vector domain description method, which establishes a fault identification model first, and then estimates fault degree according to the model. When establishing fault identification model for thruster based on fuzzy support vector domain description method, it is found that the relative fitting error of the model to the actual fault degree is large, making the model accuracy poor. To reduce the relative fitting error, a homomorphic membership function method is proposed. Different from fuzzy support vector domain description method, which calculates the fuzzy membership degree of fault sample in time domain, the proposed method calculates the fuzzy membership degree in log domain. On estimating thruster fault degree by fuzzy support vector domain description method, it is obtained that the estimated fault degree lags behind the actual fault degree. To shorten the lag time, a low frequency trend prediction method is proposed. Different from fuzzy support vector domain description method, which brings the fault feature extracted from the current surge speed and control voltage into the fault identification model to calculate fault degree, the proposed method firstly forward predicts surge speed and control voltage, and then takes the fault feature extracted from the predicted surge speed and control voltage into the model to acquire fault degree. The effectiveness of the proposed methods is verified by pool experiments of the experimental prototype.
“…Zhu and Sun 5 investigated the jammed thruster fault diagnosis method based on information fusion. In addition, the authors have also investigated thruster fault diagnosis method based on wavelet decomposition, 13 single-channel blind source, 14 and support vector domain description. 15 In the above references, the fault is always serious, and they have not involved with weak thruster fault diagnosis problem.…”
This article investigates weak thruster fault detection problem for autonomous underwater vehicle subject to the external disturbances. A weak thruster fault detection method is developed based on the combination of artificial immune system and single pre-processing. The number of measurable autonomous underwater vehicle's signals is limited and the weak thruster fault is easy to be compensated by the closed-loop control system. In this developed method, signal preprocessing is first used in the original autonomous underwater vehicle signals, including signal transformation, data fusion based on Dempster-Shafer evidence theory, and feature extraction based on isometric mapping algorithm, to reduce the external disturbance effect and prominently reflect thruster fault. Then artificial immune system is applied to conduct anomaly detection based on the processed autonomous underwater vehicle signals. In the process of fault detection, it includes two stages, training stage and testing stage. In training stage, the detectors are generated at first based on the autonomous underwater vehicle signals in healthy condition based on negative selection and positive selection. And then in testing stage, the generated detectors are used to detect whether fault occurs or not based on the principle of self and non-self discrimination. Finally, to evaluate the performance of the developed fault detection method, Beaver 2 autonomous underwater vehicle is used to conduct experiments. The comparative results demonstrate the effectiveness of the developed method.
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