/pdf/a-perspective-survey-on-deep-transfer-learning-for-fault-m5e3nrkv.pdf

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

pdf/a-comprehensive-review-on-convolutional-neural-network-in-1f58bn8ncl.pdf

A comprehensive review on convolutional neural network in machine fault diagnosis

A systematic review of deep transfer learning for machinery fault diagnosis

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

Generative adversarial networks for data augmentation in machine fault diagnosis

This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram (ECG) arrhythmias classification. The LSTM-based AE network (LSTM-AE) is used to learn the features from ECG arrhythmias signals, and the SVM is used to classify those signals from the learned features. The LSTM-AE consists of an encoder model, which extracts high-level feature information from ECG arrhythmias signals through LSTM network, and a decoder model which outputs reconstruct ECG arrhythmias signals from high-level features through LSTM network. Experiments show that the proposed method can learn better features than the traditional method without any prior knowledge, presenting a good potential for the ECG arrhythmias classification. In the classification of five heartbeats types, including normal, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complexes (APC), premature ventricular contractions (PVC), the proposed method achieved average accuracy, sensitivity, and specificity of 99.74%, 99.35%, and 99.84%, respectively, in the beat-based cross-validation approach, and 85.20%, 62.99%, and 90.75%, respectively, in the record-based cross-validation approach, in public MIT-BIH Arrhythmia Database. While based on the Advancement of Medical Instrumentation (AAMI) standards, the proposed method achieved average accuracy, sensitivity, and specificity of 99.45%, 98.63%, and 99.66%, respectively, in the beat-based cross-validation approach.

LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification

This paper aims at proposing an abnormality detection framework for electrocardiogram (ECG) signals, which owns unbalance distribution among different classes and gaining high accuracy in rhythm/morphology abnormalities classification. The proposed framework is composed of two models: data augmentation model and classification model. In this framework, data augmentation model is designed to recast a class-balanced training dataset by generating artificial data of minor class. The outputs of augmentation model are transferred into classification model. The classification model is designed to identify abnormalities accurately after training using both the experimental and generated datasets. Data augmentation model is supported by auxiliary classifier generative adversarial network (ACGAN). We construct Generator and Discriminator of the ACGAN by stacking multiple 1-dimensional convolutional layers with small kernel size. Dropout function and batch normalization are added to prevent gradients vanish and speed up convergence. In order to evaluate the performance of augmentation model, a set of quantitative indicators are introduced to verify the quality of generated ECG signals. We establish classification model based on stacked residual network parallel connected with long short-term memory (LSTM) network. The experimental study is conducted for single heartbeat detection and consecutive heartbeat detection. The results based on standard benchmark, MIT-BIH, and competition database provided by 2018 China physiological signal challenge (CPSC) have verified the proposed framework can achieve high performance in robustness and accuracy for class-imbalanced dataset.

/pdf/ecg-arrhythmias-detection-using-auxiliary-classifier-1pcqhwi2zk.pdf

ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network

This paper presents an improved cross recurrence quantitative analysis (CRQA) method for bearing degradation evaluation. It extracts CRQA variable from bearings' vibration signals to establish the degradation trend. Due to the fact that traditional process for calculating the hyperparameters of CRQA requires a large amount of time to reconstruct phase space, this step is replaced by calculating divergence rate of each signal sample to cut down the time consumption. After establishing the degradation trend based on the extracted variable, nonlinear auto-regressive neural network (NARNN) is developed to predict future degradation trend. Furthermore, a new failure detection method is investigated to indicate the first failure point during degradation tracking. The method applies temperature signals as auxiliary information to calculate the adaptive threshold and classify extracted features into different health status. The experiments on bearing vibration signals have verified that the improved CRQA method can reduce time consumption by more than 90%. In addition, defect characteristic frequencies extracted using wavelet analysis have validated the detection accuracy.

/pdf/bearing-degradation-evaluation-using-improved-cross-3larcb65t4.pdf

Bearing Degradation Evaluation Using Improved Cross Recurrence Quantification Analysis and Nonlinear Auto-Regressive Neural Network

Recently, there has been a remarkable increase in the interest towards skeleton-based action recognition within the research community, owing to its various advantageous features, including computational efficiency, representative features, and illumination invariance. Despite this, researchers continue to explore and investigate the most optimal way to represent human actions through skeleton representation and the extracted features. As a result, the growth and availability of human action recognition datasets have risen substantially. In addition, deep learning-based algorithms have gained widespread popularity due to the remarkable advancements in various computer vision tasks. Most state-of-the-art contributions in skeleton-based action recognition incorporate a Graph Neural Network (GCN) architecture for representing the human body and extracting features. Our research demonstrates that Convolutional Neural Networks (CNNs) can attain comparable results to GCN, provided that the proper training techniques, augmentations, and optimizers are applied. Our approach has been rigorously validated, and we have achieved a score of 95% on the NTU-60 dataset

Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)

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