Intelligent fault diagnosis of rotating machinery based on a novel lightweight convolutional neural network

2020 ◽  
pp. 1748006X2096501
Author(s):  
Yuqi Lu ◽  
Jinhua Mi ◽  
He Liang ◽  
Yuhua Cheng ◽  
Libing Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Deep Convolutional Neural Networks ◽  
Acceleration Signal ◽  
Resource Requirements ◽  
Hardware Resource ◽  
Feature Filters

For most existing fault diagnosis methods, feature extraction is always based on a complex artificial design and the complete feature extraction from an original signal. With the gradual complication of modern industrial machinery and equipment, it has become more difficult for traditional feature extractors to achieve the desired results. Deep convolutional neural networks (DCNNs) have been developed as effective techniques for fault classification but require large-scale high-intensity computing and prohibitive hardware resource requirements. This paper proposes a lightweight CNN that can be easily used for the fault diagnosis of rotating machinery by adjusting the network structure and optimizing the network. First, the raw vibration acceleration signal is transformed into a two-dimensional gray image. Second, two mature and commonly used modules named LeNet and NIN are combined to form a new model with a simple structure. Then, through parameter adjustment and optimization, an improved and optimized CNN with a lightweight structure and fewer parameters is constructed. The experimental verification has shown that this method has high accuracy and stability in fault diagnosis. Finally, the application of this new network for the fault diagnosis of rolling bearings with different damage levels but similar fault types shows high diagnostic accuracy and good generalization ability. In addition, we attempt to explain how the feature filters of a CNN work by visualizing the convolutional layer of the network.

scholarly journals A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lei You ◽  
Wenjie Fan ◽  
Zongwen Li ◽  
Ying Liang ◽  
Miao Fang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Local Optimum ◽  
Classification Rate ◽  
Wavelet Energy ◽  
Diagnosis Model

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

2021 ◽  
Author(s):  
Hao DeChen ◽  
HuaLing Li ◽  
JinYing Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Network Structure ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Data Set ◽  
Public Data ◽  
Diagnosis Method ◽  
Diagnosis Model

Abstract Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 675-679 ◽  
Author(s):  
Fu Ze Xu ◽  
Xue Jun Li ◽  
Guang Bin Wang ◽  
Da Lian Yang
Keyword(s):  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
Coupling Fault ◽  
Fault Information

It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.

A Neuron-Fuzzy Technique for Fault Diagnosis in Rotating Machinery

2011 ◽  
Vol 204-210 ◽  
pp. 2188-2191 ◽  
Author(s):  
Zheng Yao ◽  
Qing Xin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Fuzzy Theory ◽  
Fuzzy Rule ◽  
Basic Structure ◽  
Rotating Machinery ◽  
Rule Set ◽  
On Line ◽  
Working Principle ◽  
Neural Network Theory

The on-line fault diagnostics technology for machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. On the basis of fault diagnosis theory and method, this paper presents a applications of techniques for fault detection and classification in rotating machinery based on fuzzy theory and neural network theory, the basic structure and working principle of the fault intelligent diagnosis system are introduced, the knowledge stored in the neuron-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability, the model of fuzzy fault diagnosis and the self-study principle are described. The practice proves that this is an effective method of large-scale and complicated electronic equipment, and it can also be applied to other fault diagnosis of complex systems and has certain portability.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

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