Network Approach For Fault Diagnosis Research Articles

MIM-Graph: A multi-sensor network approach for fault diagnosis of HSR Bogie bearings at the IoT edge via mutual information maximization

The Internet of Things (IoT) is crucial in developing next-generation high-speed railways (HSRs). HSR IoT enables intelligent diagnosis of trains using multi-sensor data, which is critical for maintaining high speeds and ensuring passenger safety. Graph neural network (GNN)-based methods have gained popularity in HSR IoT research due to the ability to represent the sensor network as intuitive graphs. However, labeling monitoring data in the HSR scenario takes time and effort. To address this challenge, we propose a semi-supervised graph-level representation learning approach called MIM-Graph, which uses mutual information maximization to learn from a large amount of unlabeled data. First, the multi-sensor data is converted into association graphs based on their spatial topology. The unsupervised encoder is trained using global–local mutual maximization. The teacher–student framework transfers knowledge from the unsupervised encoder learned to the supervised encoder, which is trained using a small amount of labeled data. As a result, the supervised encoder learns distinguishable representations for intelligent diagnosis of HSR. We evaluate the proposed method using CWRU dataset and data from HSR Bogie test platform, and the experimental results demonstrate the effectiveness and superiority of MIM-Graph.

An end-to-end denoising autoencoder-based deep neural network approach for fault diagnosis of analog circuit

Fault diagnosis of analog circuit is critical to improve safety and reliability in electrical systems and reduce losses. Traditional fault diagnosis methods of analog circuit usually rely on the hand design feature extractor and can not generalize well to other diagnosis domains. To address these issues, an end-to-end denoising autoencoder (EEDAE)-based fault diagnosis approach is proposed. The proposed approach includes denoising autoencoder (DAE) and a softmax classifier. The DAE is designed to automatically extract fault features from the raw time series signals without any signal processing techniques and diagnostic expertise, and then the softmax classifier is used to classify the fault mode of analog circuits. Specifically, we design a novel loss function by jointly minimizing reconstruction loss and classification loss to improve training efficiency. The proposed approach just has one training stage, in which the encoder, decoder, and classifier are trained simultaneously. The experimental results demonstrate that compared with traditional methods, the proposed method has higher accuracy and lower requirements on data.

A linear ridgelet network approach for fault diagnosis of analog circuit

A linear ridgelet network combining ridgelet, linear term and the standard feed-forward neural network used for diagnosing the faults of analog circuit is constructed, and a training algorithm based on the steepest gradient descent method and momentum method for this network and the procedure for diagnosing these faults are proposed. The resulting linear ridgelet network can learn more rapidly from training samples and handle more effectively the complicated fault information of circuit under test than wavelet network and ridgelet network, classifying these faults efficiently and correctly and achieving a high classification accuracy. The simulation results demonstrate the effectiveness of this approach.

Wavelet neural network approach for fault diagnosis of analogue circuits

A systematic method for fault diagnosis of analogue circuits based on the combination of neural networks and wavelet transforms is presented. Using wavelet decomposition as a tool for removing noise from the sampled signals, optimal feature information is extracted by wavelet noise removal, multi-resolution decomposition, PCA (principal component analysis) and data normalisation. The features are applied to the proposed wavelet neural network and the fault patterns are classified. Diagnosis principles and procedures are described. The reliability of the method and comparison with other methods are shown by two active filter examples.