Deep learning and its applications to machine health monitoring

A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

A survey on Deep Learning based bearing fault diagnosis

https://escholarship.org/content/qt4999765n/qt4999765n.pdf?t=psy2c4

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

Due to the real working conditions and data acquisition equipment, the collected working data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal state at most times, it is easy to raise the imbalance problem of fault types which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative adversarial network (GAN) and provide a comparative study in detail. The key idea is utilizing GAN, a kind of deep learning technique, to generate synthetic samples for minority fault class and then improve the generalization ability of the fault diagnosis model. First, this method applies fast Fourier transform to pre-process the original vibration signal and then obtains the frequency spectrum of fault samples. Second, it uses the spectrum data as the input of GAN to generate the synthetic minority samples following the data distribution of the real samples. Finally, it puts the synthetic samples into the training set and builds a stacked denoising auto encoder model for fault diagnosis. To testify the effectiveness of the proposed method, a series of comparative experiments is carried out on the CWRU bearing dataset. The results show that the proposed method can provide a better solution for imbalanced fault diagnosis on the basis of generating similar fault samples. As a comparative study, the proposed method is compared to several diagnostic methods with traditional time-frequency domain characteristics. Moreover, we also demonstrate that the proposed method outperforms three widely used sample synthesis techniques, such as random oversampling, synthetic minority oversampling technique, and the principal curve-based oversampling method in terms of diagnosis accuracy and numerical stability.

/pdf/imbalanced-fault-diagnosis-of-rolling-bearing-based-on-50v0kzflw6.pdf

Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. Ho...

Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study:

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extracti...

https://journals.sagepub.com/doi/pdf/10.1177/1687814018817184

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network:

In recent years, fire recognition methods have received more and more attention in the fields of academy and industry. Current sensor-based recognition methods rely heavily on the external physical signals, which will probably reduce the recognition precision if the external environment changes dramatically. With the rapid development of high-definition camera, the methods based on image feature extraction provide another solution which tries to conduct pattern recognition for the monitoring video. However, these methods couldn’t be widely and successfully applied to fire detection due to two deficiencies: (1) there are too many interference items like lamplight and car highlight in the room or tunnel, which will disturb the recognition performance largely; (2) The features depend on much prior knowledge about flame and smoke, and there lacks a universal and automatic extraction method for various fire scenes. As a breakthrough in pattern recognition, deep learning is capable of exploring the useful information from raw data, and can automatically provide accurate recognition results. Therefore, based on deep learning idea, a novel fire recognition method based on multi-channel convolutional neural network is proposed in this paper to overcome the deficiencies mentioned above. First, three channel colorful images are constructed as the input of convolutional neural network; Second, the hidden layers with multiple-layer convolution and pooling are constructed, and simultaneously, the model parameters are find tuned by using back propagation; Finally, softmax method is used to conduct the classification about fire recognition. To save the training time, we utilize GPU to construct training and test models. From public fire dataset and Internet, we collect 7000 images for training and 4494 images for test, and then run experiments with the comparison of four baseline methods including deep neural network, support vector machine based on scale-invariant feature transform feature, stack auto-encoder and deep belief network. The experimental results show that the proposed method is more capable of restoring the features of input image by means of hidden output figure, and for various flame scenes and types, the proposed method can reach 98% or more classification accuracy, getting improvement of around 2% than the traditional feature-based method. Also, the proposed method always outperforms other Deep Learning methods in terms of ROC curve, recall rate, precision rate and F1-score.

Fire Recognition Based On Multi-Channel Convolutional Neural Network

The original version of this article unfortunately contained a mistake in “Acknowledgement” section. The funding information of “National Natural Science Foundation of China (No. U1704158)” is missing in the original publication. The corrected Acknowledgements section is given below:

/pdf/correction-to-fire-recognition-based-on-multi-channel-tdxarxz4us.pdf

Yuan Li

Papers

Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study:

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network:

Fire Recognition Based On Multi-Channel Convolutional Neural Network

Correction to: Fire Recognition Based On Multi-Channel Convolutional Neural Network