A Signal Decomposition Method for Ultrasonic Guided Wave Generated from Debonding Combining Smoothed Pseudo Wigner-Ville Distribution and Vold–Kalman Filter Order Tracking

Proceedings of the Institution of Mechanical Engineers, Part O:

Zhang

Zhou

et al. 2022

This article investigates a novel fault identification approach to determine the percentage of the thrust loss for autonomous underwater vehicle thrusters. The novel approach is developed from a combination of the peak region energy (PRE) and support vector data description (SVDD) by considering that PRE is able to acquire a primary feature in low dimensions from signals without any secondary process and that SVDD can establish a hypersphere boundary for a class of fault samples even in the case of a small number of training samples. Three improvements, namely removing the fusion, an energy leakage and a homomorphic transform are applied to the PRE. It forms an improved PRE to increase the area under the curve. Furthermore, another three new contents, namely the least square, a multiple model fusion and a dead zone are added to the SVDD. It constructs a multiple model least square SVDD to increase the overall identification accuracy. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results indicate the effectiveness of the proposed method.

Section: The Ipre Methodsmentioning

confidence: 99%

Thruster fault identification using improved peak region energy and multiple model least square support vector data description for autonomous underwater vehicle

Proceedings of the Institution of Mechanical Engineers, Part O:

Zhang

Zhou

et al. 2022

“…The energy distribution of the sample data are calculated according to the process of SPWVD. 30 The results are shown in Figure 7.…”

Section: The Tfee Techniquementioning

confidence: 99%

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

Proceedings of the Institution of Mechanical Engineers, Part I:

Zhang

Lin

et al. 2019

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 process of time-frequency entropy extraction is presented as follows. The smoothed pseudo Wigner-Ville distribution algorithm is adopted to obtain the energy distribution SPWVD n; m ð Þ of the signal m F B n ð Þ in time-frequency domain, as shown in Formula 4 [19]. The Shannon entropy F TFH of the SPWVD n; m ð Þ is calculated by Formulas 5-6 [20].…”

Section: Fault Feature Extractionmentioning

confidence: 99%

Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal

Intelligent Robotics and Applications

Lin

Tang

et al. 2019

In order to deal with the problem of low classification accuracy in thruster fault identification based on single fault feature, a fault identification algorithm based on time-domain energy and time-frequency entropy of fusion signal is proposed. Firstly, the fault singular signals from two single aspects, such as surge speed and control voltage, are fused into a fusion signal to reflect fault information more comprehensively. Then, the peak region energy feature of the fusion signal is extracted in time domain, and the entropy feature of the fusion signal is extracted in time-frequency domain, so as to obtain the multidomain fault features. Finally, based on support vector data description algorithm, a multi-classifier is established, and the relative distance between fault sample and each hypersphere in the multi-classifier is calculated. The fault severity corresponding to the fault sample is determined by the minimum relative distance. The experimental results of an experimental prototype in a pool show that the classification accuracy of the proposed method is 95.2%. In comparison to the classification models corresponding to the surge speed and control voltage, the classification accuracy is increased by 5.2% and 22.8% respectively.