Few-shot learning fault diagnosis of rolling bearings based on siamese network
Sign Up to like & getrecommendations! Published in 2024 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/ad57d9
Abstract: This paper focuses on the fault diagnosis problem in the scenario of scarce bearing samples, facing two main challenges: complex noise background and variations in operating conditions. While deep learning-based fault diagnosis methods have achieved… read more here.
Keywords: diagnosis; based siamese; learning fault; shot learning ... See more keywords
Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.2986006
Abstract: In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the… read more here.
Keywords: subway trains; subway; cooperative adaptive; adaptive iterative ... See more keywords
Composite Neural Learning Fault-Tolerant Control for Underactuated Vehicles With Event-Triggered Input
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2020.3005800
Abstract: This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden… read more here.
Keywords: fault tolerant; learning fault; event triggered; neural learning ... See more keywords
Clustering-Based Contrastive Learning for Fault Diagnosis With Few Labeled Samples
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2023.3346494
Abstract: In recent years, the utilization of deep learning (DL) for fault diagnosis has become more and more prevalent. However, most DL methods rely on a large amount of labeled data to train models, which could… read more here.
Keywords: clustering based; learning fault; fault diagnosis; contrastive learning ... See more keywords
Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults
Sign Up to like & getrecommendations! Published in 2022 at "Transactions of the Institute of Measurement and Control"
DOI: 10.1177/01423312211072207
Abstract: This paper is concerned with an iterative learning fault-tolerant control strategy for discrete-time nonlinear systems where actuator faults arbitrarily occur. First, the stochastic faults occurring in multiplicative and additive manner are considered. Then, statistical behaviors… read more here.
Keywords: time; control; actuator; iterative learning ... See more keywords
Continual learning for fault diagnosis considering variable working conditions
Sign Up to like & getrecommendations! Published in 2024 at "Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability"
DOI: 10.1177/1748006x241252469
Abstract: Traditional Neural Networks (NNs) trained in a one-stage process often struggle to perform well when presented with new classes or domain shifts in testing datasets. In fault diagnosis, it is essential to handle a sequence… read more here.
Keywords: continual learning; learning fault; fault diagnosis; working conditions ... See more keywords