Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images

Xiao-hong, Zhu; Hou, Dibo; Zhou, Pei; Han, Zhonghe; Yuan, Yiming; Zhou, Weiwei; Yin, Qianqian

doi:10.1016/j.measurement.2019.02.022

Cited by 70 publications

(25 citation statements)

References 11 publications

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“…The raw vibration signal has also been transformed to an imagery form for diagnosis purposes using techniques like Continuous Interleaved Sampling (CIS) [108]- [112], Omnidirectional Regeneration Technique (ORT) [113] and Symmetrized Dot Pattern (SDP) [114], [115]. Armed with these transformation techniques and the transfer learning strategy, several pretrained CNNs, originally trained on natural images, were transferred to fault diagnosis applications using vibration data; examples include LeNet-5 [107], [109], [110], VGG-16 [106], AlexNet [95] and ResNet-50 [108].…”

Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…With these time-frequency images, the TLCNN model is used to extract features and achieve the classification conditions of bearing health. Zhu et al [69] proposed a method for rotor fault diagnosis using CNN with symmetrized dot pattern (SDP) images. In this method, vibration signals are transformed into SDP images then the graphical features of the SDP images of different vibration states are learned and identified using a CNN architecture.…”

Section: Introductionmentioning

confidence: 99%

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Ahmed

Nandi

2021

Chin. J. Mech. Eng.

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Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

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“…The literature has shown that tackling such problems successfully requires two major phases: feature extraction of the produced signals and an intelligent classifier for the final diagnostic mission [3,4,5,6]. Existing research on diagnosing faults in rotating machinery can be divided into studies focusing on traditional artificial intelligence techniques that include fuzzy logic [7,8], genetic algorithms [9], support vector machines [10] and neural networks [11] or more recently, deep learning artificial intelligence techniques [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…However, large data samples are needed to avoid overfitting, or flexible augmentation is needed to increase the number of samples for training deep frameworks, if possible. Zhu et al [12] proposed a deep learning model based on a convolutional neural network (CNN) that can efficiently and accurately recognize vibration faults by automatically extracting rotor vibration features. They then diagnosed the faults and reported enhanced results compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

et al. 2021

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Effective intelligent fault diagnosis of rotating machinery using its vibrational signals has a considerable influence on certain analysis factors such as the reliability, performance, and productivity of a variety of modern manufacturing machines. Traditional intelligent approaches lack generalization schemes and add the burden of extracting features from data-driven cases. On the other hand, the Deep Learning (DL) studies have reported capabilities higher than the expectations of the researchers' objectives. In this context, this paper proposes a new deep architecture based on Stacked Variant Autoencoders for multi-fault machinery identification with imbalanced samples. The proposed model starts with a Variational Autoencoder (VAE) for facilitating data augmentation of small and imbalanced data samples using Gaussian distribution. After the preparation of suitable samples based on quality and size, the preprocessed vibration signals obtained are injected into the deep framework. The proposed deep architecture contains two subsequent unsupervised Sparse Autoencoders (SAE) with a penalty term that helps in acquiring more abstract and essential features as well as avoiding redundancy. The output of the second SAE is integrated on a supervised Logistic Regression (LR) with 10 classes. This is utilized for the proposed classifier training to achieve accurate fault identification. Experimental results show the efficiency of the proposed model which achieved an accuracy of 93.2%. In addition, for extensive comparative analysis issue, the Generative Adversarial Network (GAN) and triNetwork Generative Adversarial Network (tnGAN) were both implemented on the vibrational signal data, where the proposed method reported better results in terms of training and testing time and overall accuracy.

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Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images

Cited by 70 publications

References 11 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

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