Bearing fault diagnosis under different operating conditions based on cross domain feature projection and domain adaptation

2019 ◽  
Author(s):  
Shuzhi Dong ◽  
Guangrui Wen ◽  
Zhifen Zhang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Operating Conditions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Feature Projection

scholarly journals A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yongchao Zhang ◽  
Zhaohui Ren ◽  
Shihua Zhou
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Classification Error ◽  
Mechanical Equipment ◽  
Target Domain ◽  
Source Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

scholarly journals Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 320 ◽  
Author(s):  
Xiaodong Wang ◽  
Feng Liu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Great Success ◽  
Target Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Level ◽  
Triplet Loss ◽  
Data Level

Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.

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

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.

scholarly journals Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Guangxing Niu ◽  
Bin Zhang ◽  
Paul Ziehl ◽  
Frank Ferrese ◽  
Michael Golda
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Operating Conditions ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Ae Signal

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Bearing fault diagnosis is therefore essential and significant to safe and reliable operation of systems. For bearing condition monitoring, acoustic emission (AE) signals attract more and more attention due to its advantages on sensitivity over the extensively used vibration signal. In bearing fault diagnosis and prognosis, feature extraction is a critical and tough work, which always involves complex signal processing and computation. Moreover, features greatly rely on the characteristics, operating conditions, and type of data. With consideration of changes in operating conditions and increase of data complexity, traditional diagnosis approaches are insufficient in feature extraction and fault diagnosis. To address this problem, this paper proposes a Deep Belief Network (DBN) and Principal Component Analysis (PCA) based fault diagnosis approach using AE signal. This proposed approach combines the advantages of deep learning and statistical analysis, DBN automatically extracts features from AE signal, PCA is applied to dimensionality reduction. Different bearing fault modes are identified by least squares support vector machine (LS-SVM) using the extracted features. An experimental case is conducted with a tapered roller bearing to verify the proposed approach. Experimental results demonstrate that the proposed approach has excellent feature extraction ability and high fault classification accuracy.

scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

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