scholarly journals Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5494
Author(s):  
Chen Zhao ◽  
Jianliang Sun ◽  
Shuilin Lin ◽  
Yan Peng
Keyword(s):  
Fault Diagnosis ◽  
Rolling Mill ◽  
Real Data ◽  
Vibration Signal ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Mode Decomposition ◽  
Unbalanced Dataset ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forces together. Considering the special characters of rolling mill bearing vibration signals, a fault diagnosis method combining Adaptive Multivariate Variational Mode Decomposition (AMVMD) and Multi-channel One-dimensional Convolution Neural Network (MC1DCNN) is proposed to improve the diagnosis accuracy. Additionally, Deep Convolutional Generative Adversarial Network (DCGAN) is embedded in models to solve the problem of fault data scarcity. DCGAN is used to generate AMVMD reconstruction data to supplement the unbalanced dataset, and the MC1DCNN model is trained by the dataset to diagnose the real data. The proposed method is compared with a variety of diagnostic models, and the experimental results show that the method can effectively improve the diagnosis accuracy of rolling mill multi-row bearing under unbalanced dataset conditions. It is an important guide to the current problem of insufficient data and low diagnosis accuracy faced in the fault diagnosis of multi-row bearings such as rolling mills.

scholarly journals A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals

2020 ◽  
Vol 10 (17) ◽  
pp. 5765
Author(s):  
Qiang Fu ◽  
Huawei Wang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Data Augmentation ◽  
Measured Data ◽  
Learning System ◽  
Generative Adversarial Network ◽  
Vibration Signals ◽  
Adversarial Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a fault diagnosis method combining a generative adversarial network (GAN) and stacked denoising auto-encoder (SDAE). The GAN approach augments the limited real measured data, especially in faulty conditions. The generated data are then transformed into the SDAE fault diagnosis model. The GAN-SDAE approach improves the accuracy of the fault diagnosis from the vibration signals, especially when the measured samples are few. The usefulness of this method is assessed through two condition-monitoring cases: one is a classic bearing example and the other is a more general gear failure. The results demonstrate that diagnosis accuracy for both cases is above 90% for various working conditions, and the GAN-SDAE system is stable.

scholarly journals A Semi-Supervised Fault Diagnosis Method Based on Improved Bidirectional Generative Adversarial Network

2021 ◽  
Vol 11 (20) ◽  
pp. 9401
Author(s):  
Long Cui ◽  
Xincheng Tian ◽  
Xiaorui Shi ◽  
Xiujing Wang ◽  
Yigang Cui
Keyword(s):  
Fault Diagnosis ◽  
Wasserstein Distance ◽  
Unlabeled Data ◽  
Fine Tuning ◽  
Training Procedure ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
The Stability

With the assumption of sufficient labeled data, deep learning based machinery fault diagnosis methods show effectiveness. However, in real-industrial scenarios, it is costly to label the data, and unlabeled data is underutilized. Therefore, this paper proposes a semi-supervised fault diagnosis method called Bidirectional Wasserstein Generative Adversarial Network with Gradient Penalty (BiWGAN-GP). First, by unsupervised pre-training, the proposed method takes full advantage of a large amount of unlabeled data and can extract features from vibration signals effectively. Then, using only a few labeled data to conduct supervised fine-tuning, the model can perform an accurate fault diagnosis. Additionally, Wasserstein distance is used to improve the stability of the model’s training procedure. Validation is performed on the bearing and gearbox fault datasets with limited labeled data. The results show that the proposed method can achieve 99.42% and 91.97% of diagnosis accuracy on the bearing and gear dataset, respectively, when the size of the training set is only 10% of the testing set.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

scholarly journals Fault Diagnosis of the Gyratory Crusher Based on Fast Entropy Multilevel Variational Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fengbiao Wu ◽  
Lifeng Ma ◽  
Qianqian Zhang ◽  
Guanghui Zhao ◽  
Pengtao Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Working Environment ◽  
Signal Spectrum ◽  
Variational Mode Decomposition ◽  
Fe Method ◽  
Mode Decomposition ◽  
Mining Machinery ◽  
Diagnosis Method ◽  
Heavy Workload

Gyratory crusher is a kind of commonly used mining machinery. Because of its heavy workload and complex working environment, it is prone to failure and low reliability. In order to solve this problem, this paper proposes a fault diagnosis method of the gyratory crusher based on fast entropy multistage VMD, which is used to quickly and accurately find the possible fault problems of the gyratory crusher. This method mainly extracts the vibration signal by combining fast entropy and variational mode decomposition, so as to analyze the components of the vibration signal. Among them, fast entropy is used to quickly determine the number of modes in the signal spectrum and the bandwidth occupied by the modes. The extracted parameters can be converted into the input parameters of VMD. VMD can accurately extract the modal components in the signal by inputting the number of modes and related parameters. Due to the differences between modes, using the same parameters to extract the modes often leads to inaccurate results. Therefore, the concept of multilevel VMD is proposed. The parameters of different modes are determined by fast entropy. The modes in the signals are separated and extracted with different parameters so that different signal modes can be accurately extracted. In order to verify the accuracy of the method, this paper uses the data collected from the rotary crusher to test, and the results show that the proposed FE method can quickly and effectively extract the fault components in the vibration signal.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

scholarly journals Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5222 ◽  
Author(s):  
Guo-dong Sun ◽  
You-ren Wang ◽  
Can-fei Sun ◽  
Qi Jin
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Signal Spectrum ◽  
Planetary Motion ◽  
Single Fault ◽  
Planetary Gearbox ◽  
Automatic Feature Extraction ◽  
Mode Decomposition ◽  
Intelligent Detection ◽  
Diagnosis Method

Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.—the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.

scholarly journals Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 739
Author(s):  
Chunguang Zhang ◽  
Yao Wang ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Signal Features ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Two Parameters ◽  
Envelope Spectrum

It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.

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