Diagnosis of bearing defects under variable speed conditions using energy distribution maps of acoustic emission spectra and convolutional neural networks

Tra, Viet; Khan, Sheraz Ali; Kim, Jong‐Myon

doi:10.1121/1.5065071

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…Table 3 shows the accuracy of the proposed FD method, where the results are compared to four existing AE-based FD methods for compound faults detection under variations in the rotational speed. Firstly, we compared two CNN-based FD methods [10,11] using the spectra of AE signals to create two-dimensional (2D) energy distribution maps (EDMs). The created EDMs fed a generic CNN based on Lenet-5 architecture to extract the bearing fault features.…”

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

“…The created EDMs fed a generic CNN based on Lenet-5 architecture to extract the bearing fault features. After extracting features, in [11], a hybrid ensemble MLP-SVM classifier is used to classify the faults from extracted features. On the other hand, in [10], classification is performed by multilayer perceptron classifiers.…”

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

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Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

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Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

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Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

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“…AE is the process of the generation of transient elastic waves from sudden cyclic fatigue, fraction, impacting, etc. [2][3][4][5]. Regarding bearings, the acoustic waves can be generated when the rolling elements of the bearing hit the cracked surface on the inner race, outer race, and rolling element.…”

Section: Introductionmentioning

confidence: 99%

“…of convolutional neural network (CNN) and DNN-based approaches. From these papers we could conclude that the CNN-based techniques are much better than DNN-based methods in terms of fault diagnosis performance [3,12,15,16]. Although DNN or CNN-based methods have achieved high classification accuracy, there are still two issues that must be resolved to make these methods highly applicable to real applications.…”

mentioning

confidence: 99%

Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Kim

2020

Applied Sciences

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Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure. of convolutional neural network (CNN) and DNN-based approaches. From these papers we could conclude that the CNN-based techniques are much better than DNN-based methods in terms of fault diagnosis performance [3,12,15,16]. Although DNN or CNN-based methods have achieved high classification accuracy, there are still two issues that must be resolved to make these methods highly applicable to real applications. The first issue is that the trained neural network, in general, can be only reliable on the specific machine since the patterns of the raw signals strongly depend on the operating conditions of the machinery such as load, installation, external vibration, etc. The second concern is that the trained feature representation is uninterpretable due to the black box-like operation of the neural networks.This paper proposes a new CNN-based rolling element bearing fault diagnosis approach to resolve the aforementioned problems. To address the first issue, the proposed method utilizes the normalized bearing characteristic components (NBCC) as the input data of CNN rather than raw AE signal itself. Since the bearing characteristic frequencies are induced by appearing bearing failures, NBCC is a more effective representation for diagnosing the bearing failure symptoms. To resolve the second issue, this paper applies the gradient-weighted class activation mapping (Grad-CAM) to visualize important regions in NBCC. According to the literature, Grad-CAM is a promising method that provides visual explanations of the classification result of a CNN in object detection and recognition [17].The remainder of this paper is organized as follows. Section 2 introduces the proposed methodology for diagnosing rolling element bearing faults using AE signals. In Section 3 the bearing fault simulator used for collecting AE signals is presented. The fault diagnosis results demonstrated and discussed in Section 4. Finally, Section 5 contains the concluding remarks. Proposed...

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“…Feature analysis -based fault diagnosis methods have long been used on industrial devices [15][16][17] and have been adopted to detect leakages, cracks, and corrosion in devices working under high pressure [18][19][20]. Typical fault diagnosis methods consist of two basic steps: fault feature calculation and fault classification.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Tra

Duong

Sohaib

et al. 2019

Entropy

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This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study.

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Diagnosis of bearing defects under variable speed conditions using energy distribution maps of acoustic emission spectra and convolutional neural networks

Cited by 19 publications

References 11 publications

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

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