Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning

Chen, Hongtian; Chai, Zheng; Dogru, Oguzhan; Jiang, Bin; Huang, Biao

doi:10.1109/tnnls.2021.3071292

Cited by 59 publications

(20 citation statements)

References 42 publications

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“…It is well known that it is difficult for fault diagnosis of nonlinear systems [21,22]. Moreover, ocean currents perturbations could produce noise and further increase the difficulty of thruster fault diagnosis.…”

Section: Thruster Fault Diagnostics For Auvmentioning

confidence: 99%

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

et al. 2022

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Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Ocean currents perturbations could produce noise for thruster fault diagnosis, in order to solve the thruster fault diagnostics, a possibilistic fuzzy C-means (PFCM) algorithm is proposed to realize the fault classification in this paper. On the basis of the results of fault diagnostics, a fuzzy control strategy is proposed to solve the fault tolerant control for AUV. Considering the uncertainty of ocean currents, it proposes a min-max robust optimization problem to optimize the fuzzy controller, which is solved by a cooperative particle swarm optimization (CPSO) algorithm. Simulation and underwater experiments are used to verify the accuracy and feasibility of the proposed method of thruster fault diagnostics and fault tolerant control.

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Section: Thruster Fault Diagnostics For Auvmentioning

confidence: 99%

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

et al. 2022

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“…In the FD algorithm, statistics and their corresponding thresholds define the boundaries of system prediction. T 2 and SPE are the two most commonly used statistics in FD [33][34][35][36]. Taking the two data matrices P x and P y perform separately FD.…”

Section: Monitoring Statistics Of Fd Modelsmentioning

confidence: 99%

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

et al. 2022

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Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random noises and many other real-world constraints. This paper proposed fault detection (FD) models using canonical correlation analysis (CCA) and just-in-time learning (JITL) to process scalar signals of high-speed train gears, named as CCA-JITL. After data preprocessing and normalization, CCA transforms covariance matrices of high-dimension historical data into low-dimension subspaces and maximizes correlations between the most important latent dimensions. Then, JITL components formulate local FD models which utilize subsets of testing samples with larger Euclidean distances to training data. A case study introduced a novel system design of an online FD architecture and demonstrated that CCA-JITL FD models significantly outperformed traditional CCA models. The approach is applicable to other dimension reduction FD models such as PCA and PLS.

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“…Taking the topological structure of system data into account, a graph convolutional network was developed in [20] to diagnose machine faults. Most recently, [21] proposed two fault detection schemes, where the first design is based on the finite impulse response filter using a fully connected neural network and the second one constructs a recursive residual generator using a recurrent neural network.…”

Section: Introductionmentioning

confidence: 99%

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

Chen

Liu

Alippi

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning

Cited by 59 publications

References 42 publications

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

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

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

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