Actuator fault diagnosis in autonomous underwater vehicle based on neural network

Yang, Jianzhong; Feng, Chen; He, Bo; Guo, Jia; Wang, Dianrui; Lv, Pengfei

doi:10.1016/j.sna.2021.112668

Cited by 25 publications

(12 citation statements)

References 18 publications

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“…The state vector and error covariance at the l+1th Iteration are updated by using Equations (36)−(42) Case 2. 2 The expectation of at the l+1th Iteration is determined by using Equations ( 44) to (46) and Equations (49)−(52) Case 2. 3…”

Section: Case 21mentioning

confidence: 99%

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A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

2022

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In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.

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Section: Case 21mentioning

confidence: 99%

“…Nowadays, autonomous underwater vehicles (AUVs) have been widely used by many communities for commercial, offshore, and defense applications [1]. Especially in deep-sea exploration and development, AUVs are playing significant roles [2]. This is largely due to the rapid development of underwater navigation technology.…”

Section: Introductionmentioning

confidence: 99%

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

2022

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“…9) The raw-data-wide deep convolution neural network (WDCNN) approach that uses the raw signal as input and WDCNN [43,44] for fault identification. The used network structure is the same as the model in Ref.…”

Section: Case Studiesmentioning

confidence: 99%

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

Wang

et al. 2021

Front. Mech. Eng.

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The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

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“…The ANN has been incorporated into numerous areas, including medical data classification [1][2][3], agriculture prediction [4][5][6], diagnostic industry [7][8][9], biometric [10], [11], and face recognition [12]. The performance of the ANN in the previously cited works showed significant results and is considered an effective tool in solving various complex problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

Belattar

Abdoun

Haimoudi

2022

JCOMSS

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Actuator fault diagnosis in autonomous underwater vehicle based on neural network

Cited by 25 publications

References 18 publications

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

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