scholarly journals WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4394
Author(s):  
Zhiyu Zhu ◽  
Lanzhi Wang ◽  
Gaoliang Peng ◽  
Sijue Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Wasserstein Distance ◽  
Health Condition ◽  
Ball Screw ◽  
Computing Power ◽  
Bearing Fault Diagnosis ◽  
Average Accuracy ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn–Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.

scholarly journals An Anti-Noise Fault Diagnosis Method of Bearing based on Multi-Scale 1DCNN

2020 ◽  
Author(s):  
Jie Cao ◽  
Zhidong He ◽  
Jinhua Wang ◽  
Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Activation Function ◽  
Diagnostic Model ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Convolution Structure ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Comparison Algorithms

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

scholarly journals Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6696
Author(s):  
Zihao Sun ◽  
Xianfeng Yuan ◽  
Xu Fu ◽  
Fengyu Zhou ◽  
Chengjin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Data Distribution ◽  
Attention Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Accuracy

In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.

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.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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