A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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

Applied Sciences ◽

10.3390/app10207068 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7068

Author(s):

Minh Tuan Pham ◽

Jong-Myon Kim ◽

Cheol Hong Kim

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Extraction Methods ◽

High Accuracy ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

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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Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2018.2868519 ◽

2019 ◽

Vol 68 (8) ◽

pp. 2819-2829 ◽

Cited By ~ 29

Author(s):

Weiguo Huang ◽

Guanqi Gao ◽

Ning Li ◽

Xingxing Jiang ◽

Zhongkui Zhu

Keyword(s):

Fault Diagnosis ◽

Variable Speed ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

Mechanisms and Machine Science - Vibration Engineering and Technology of Machinery ◽

10.1007/978-3-319-09918-7_68 ◽

2014 ◽

pp. 765-775 ◽

Cited By ~ 2

Author(s):

Guojin Feng ◽

Dong Zhen ◽

Xiange Tian ◽

Fengshou Gu ◽

Andrew D. Ball

Keyword(s):

Fault Diagnosis ◽

Sensor Node ◽

Wireless Sensor ◽

Wireless Sensor Node ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Novel Method ◽

Envelope Spectrum

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Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

Volume 2: Nuclear Policy; Nuclear Safety, Security, and Cyber Security; Operating Plant Experience; Probabilistic Risk Assessments; SMR and Advanced Reactors ◽

10.1115/icone2020-16861 ◽

2020 ◽

Author(s):

Ilyoung Han ◽

Jangbom Chai ◽

Chanwoo Lim ◽

Taeyun Kim

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

High Accuracy ◽

Training Data ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

System Noise ◽

Novel Method ◽

Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

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An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

Measurement ◽

10.1016/j.measurement.2020.107667 ◽

2020 ◽

Vol 157 ◽

pp. 107667 ◽

Cited By ~ 20

Author(s):

Ying Zhang ◽

Kangshuo Xing ◽

Ruxue Bai ◽

Dengyun Sun ◽

Zong Meng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

IEEE Access ◽

10.1109/access.2019.2902344 ◽

2019 ◽

Vol 7 ◽

pp. 37611-37619 ◽

Cited By ~ 4

Author(s):

Lin Bo ◽

Guanji Xu ◽

Xiaofeng Liu ◽

Jing Lin

Keyword(s):

Fault Diagnosis ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Sensors ◽

10.3390/s20236886 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6886

Author(s):

Minh Tuan Pham ◽

Jong-Myon Kim ◽

Cheol Hong Kim

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Embedded Systems ◽

Image Size ◽

Bearing Faults ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Machine Health Monitoring ◽

System Resources

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

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Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

Journal of Vibration and Control ◽

10.1177/1077546320932030 ◽

2020 ◽

pp. 107754632093203

Author(s):

Hongdi Zhou ◽

Fei Zhong ◽

Tielin Shi ◽

Wuxing Lai ◽

Jian Duan ◽

...

Keyword(s):

Fault Diagnosis ◽

Frequency Domain ◽

Hebbian Learning ◽

Component Analysis ◽

Operating Conditions ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Class Information ◽

Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

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