Journal articles: 'Cross-domain fault diagnosis'

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Wang, Xiaodong, Feng Liu, and Dongdong Zhao. "Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation." Sensors 20, no. 13 (July 4, 2020): 3753. http://dx.doi.org/10.3390/s20133753.

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Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.

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Zhang, Yongchao, Zhaohui Ren, and Shihua Zhou. "A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions." Shock and Vibration 2020 (July 24, 2020): 1–14. http://dx.doi.org/10.1155/2020/8850976.

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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.

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Meng, Yu, Jianping Xuan, Long Xu, and Jie Liu. "Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis." Machines 10, no. 4 (March 30, 2022): 245. http://dx.doi.org/10.3390/machines10040245.

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Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative transfer have been ignored. In this paper, a reweighting method is proposed to overcome this difficulty from two aspects. First, extracted features differ greatly from one another in promoting positive transfer, and measuring the difference is important. Measured by conditional entropy, the weight of adversarial losses for those well aligned features are reduced. Second, the balance between domain adaptation and class discrimination greatly influences the transferring task. Here, a dynamic weight strategy is adopted to compute the balance factor. Consideration is made from the perspective of maximum mean discrepancy and multiclass linear discriminant analysis. The first item is supposed to measure the degree of the domain adaptation between source and the target domain, and the second is supposed to show the classification performance of the classifier on the learned features in the current training epoch. Finally, extensive experiments on several bearing fault diagnosis datasets are conducted. The performance shows that our model has an obvious advantage compared with other common transferring algorithms.

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Chang, Hong-Chan, Ren-Ge Liu, Chen-Cheng Li, and Cheng-Chien Kuo. "Fault Diagnosis of Induction Motors under Limited Data for across Loading by Residual VGG-Based Siamese Network." Applied Sciences 14, no. 19 (October 4, 2024): 8949. http://dx.doi.org/10.3390/app14198949.

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This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models.

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Li, Dan, Yudong Xu, Yuxun Zhou, Chao Gou, and See-Kiong Ng. "Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis." Mathematics 10, no. 21 (October 26, 2022): 3970. http://dx.doi.org/10.3390/math10213970.

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Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good condition of its air-conditioning systems. Often, real-time multi-sensor measurements are collected, and supervised learning algorithms are adopted to exploit the data for an effective FDD. A key issue with the supervised methods is their dependence on well-labeled fault data, which is difficult to obtain in many real-world scenarios despite the abundance of unlabelled sensor data. Intuitively, the problem can be greatly alleviated if some well-labeled fault data collected under a particular setting can be re-used and transferred to other cases where labeled fault data is challenging or costly. Bearing this idea, we proposed a novel Adversarial Cross domain Data Generation (ACDG) framework to impute missing fault data for building fault detection and diagnosis where labeled data is costly. Unlike traditional Transfer Learning (TL)-related applications that adapt models or features learned in the source domain to the target domain, ACDG essentially “generates” the unknown sensor data for the target setting (target domain). This is accomplished by capturing the data patterns and common knowledge from known counterparts in the other setting (source domain), the inter-domain knowledge, and the intra-domain relations. The proposed ACDG framework is tested with the real-world Air Handling Unit (AHU) fault dataset of the ASHRAE Research Project 1312. Extensive experimental results on the cross-domain AHU fault data showed the effectiveness of ACDG in supplementing the data for a missing fault category by exploiting the underlying commonalities between different domain settings.

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Wang, Yuanfei, Shihao Li, Feng Jia, and Jianjun Shen. "Multi-Domain Weighted Transfer Adversarial Network for the Cross-Domain Intelligent Fault Diagnosis of Bearings." Machines 10, no. 5 (April 29, 2022): 326. http://dx.doi.org/10.3390/machines10050326.

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Transfer learning is a topic that has attracted attention for the intelligent fault diagnosis of bearings since it addresses bearing datasets that have different distributions. However, the traditional intelligent fault diagnosis methods based on transfer learning have the following two shortcomings. (1) The multi-mode structure characteristics of bearing datasets are neglected. (2) Some local regions of the bearing signals may not be suitable for transfer due to signal fluctuation. Therefore, a multi-domain weighted adversarial transfer network is proposed for the cross-domain intelligent fault diagnosis of bearings. In the proposed method, multi-domain adversarial and attention weighting modules are designed to consider bearing multi-mode structure characteristics and solve the influence of local non-transferability regions of signals, respectively. Two diagnosis cases are used to verify the proposed method. The results show that the proposed method is able to extract domain invariant features for different cross-domain diagnosis cases, and thus improves the accuracy of fault identification.

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Zhang, Long, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu, and Yu Qiao. "Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis." Sensors 22, no. 24 (December 13, 2022): 9759. http://dx.doi.org/10.3390/s22249759.

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Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis.

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Jang, Gye-Bong, and Sung-Bae Cho. "Cross-Domain Adaptation Using Domain Interpolation for Rotating Machinery Fault Diagnosis." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–17. http://dx.doi.org/10.1109/tim.2022.3204093.

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Shang, Qianming, Tianyao Jin, and Mingsheng Chen. "A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs." Journal of Marine Science and Engineering 12, no. 8 (August 1, 2024): 1304. http://dx.doi.org/10.3390/jmse12081304.

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Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation of motors is crucial for ships. Existing deep learning methods typically target motors under a specific operating state and are susceptible to noise during feature extraction. To address these issues, this paper proposes a Resformer model based on bimodal input. First, vibration signals are transformed into time–frequency diagrams using continuous wavelet transform (CWT), and three-phase current signals are converted into Park vector modulus (PVM) signals through Park transformation. The time–frequency diagrams and PVM signals are then aligned in the time sequence to be used as bimodal input samples. The analysis of time–frequency images and PVM signals indicates that the same fault condition under different loads but at the same speed exhibits certain similarities. Therefore, data from the same fault condition under different loads but at the same speed are combined for cross-domain motor fault diagnosis. The proposed Resformer model combines the powerful spatial feature extraction capabilities of the Swin-t model with the excellent fine feature extraction and efficient training performance of the ResNet model. Experimental results show that the Resformer model can effectively diagnose cross-domain motor faults and maintains performance even under different noise conditions. Compared with single-modal models (VGG-11, ResNet, ResNeXt, and Swin-t), dual-modal models (MLP-Transformer and LSTM-Transformer), and other large models (Swin-s, Swin-b, and VGG-19), the Resformer model exhibits superior overall performance. This validates the method’s effectiveness and accuracy in the intelligent recognition of common cross-domain motor faults.

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Wang, Huaqing, Zhitao Xu, Xingwei Tong, and Liuyang Song. "Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers." Sensors 23, no. 4 (February 14, 2023): 2137. http://dx.doi.org/10.3390/s23042137.

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The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.

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Liu, Guokai, Weiming Shen, Liang Gao, and Andrew Kusiak. "Automated broad transfer learning for cross-domain fault diagnosis." Journal of Manufacturing Systems 66 (February 2023): 27–41. http://dx.doi.org/10.1016/j.jmsy.2022.11.003.

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Zhang, Hongpeng, Xinran Wang, Cunyou Zhang, Wei Li, Jizhe Wang, Guobin Li, and Chenzhao Bai. "Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox." Sensors 23, no. 23 (November 23, 2023): 9368. http://dx.doi.org/10.3390/s23239368.

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While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric factors. Information entropy parameters are also incorporated to assess individual sample transferability, prioritizing highly transferable samples during domain alignment. The amalgamation of these dynamic factors empowers the approach to maintain stability across varied data distributions. Comprehensive experiments on both gear and bearing data validate the method’s efficacy in cross-condition fault diagnosis. Comparative outcomes demonstrate that, when contrasted with four advanced transfer learning techniques, the dynamic conditional adversarial domain adaptation model attains superior accuracy and stability in multi-transfer tasks, making it notably suitable for diagnosing wind turbine gearbox faults.

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Bai, Jie, Xuan Liu, Bingjie Dou, Xiaohui Yang, Bo Chen, Yaowen Zhang, Jiayu Zhang, Zhenzhong Wang, and Hongbo Zou. "A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm." Processes 12, no. 10 (September 30, 2024): 2126. http://dx.doi.org/10.3390/pr12102126.

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Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.

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Chen, Zhuyun, Guolin He, Jipu Li, Yixiao Liao, Konstantinos Gryllias, and Weihua Li. "Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery." IEEE Transactions on Instrumentation and Measurement 69, no. 11 (November 2020): 8702–12. http://dx.doi.org/10.1109/tim.2020.2995441.

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Liu, Fuqiang, Wenlong Deng, Chaoqun Duan, Yi Qin, Jun Luo, and Huayan Pu. "Duplex adversarial domain discriminative network for cross-domain partial transfer fault diagnosis." Knowledge-Based Systems 279 (November 2023): 110960. http://dx.doi.org/10.1016/j.knosys.2023.110960.

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Liu, Fuzheng, Faye Zhang, Xiangyi Geng, Lin Mu, Lei Zhang, Qingmei Sui, Lei jia, Mingshun Jiang, and Junwei Gao. "Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis." Advanced Engineering Informatics 58 (October 2023): 102217. http://dx.doi.org/10.1016/j.aei.2023.102217.

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Zhang, Chao, Peng Du, Dingyu Zhou, Zhijie Dong, Shilie He, and Zhenwei Zhou. "Fault Diagnosis of Low-Noise Amplifier Circuit Based on Fusion Domain Adaptation Method." Actuators 13, no. 9 (September 23, 2024): 379. http://dx.doi.org/10.3390/act13090379.

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The Low-Noise Amplifier (LNA) is a critical component of Radio Frequency (RF) receivers. Therefore, the accuracy of LNA fault diagnosis significantly impacts the overall performance of the entire RF receiver. Traditional LNA fault diagnosis is typically conducted under fixed conditions, but varying factors in practical applications often alter the circuit’s parameters and reduce diagnostic accuracy. To address the issue of decreased fault diagnosis accuracy under varying external or internal conditions, a fusion domain adaptation method based on Convolutional Neural Networks (CNNs), referred to as FDA, is proposed. Firstly, a domain-adaptive diagnostic model was established based on the feature extraction capabilities of CNNs. The powerful deep feature extraction capabilities of CNNs and the adaptability of domain adaptation methods to changing conditions are leveraged to enhance both the generalization ability of diagnostic models and the environmental adaptability of diagnostic techniques. Secondly, the fusion of feature-mapping domain adaptation and adversarial domain adaptation further enhances the convergence speed and diagnostic accuracy of the LNA cross-domain fault diagnosis model in the target domain. Finally, various cross-domain experiments were conducted. The FDA method achieved an average fault diagnosis rate of 90.19%, which represents an improvement of over 30% in accuracy compared to a CNN and also shows enhancements over individual domain-adaptation methods.

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Zhou, Hongdi, Tao Huang, Xixing Li, and Fei Zhong. "Cross-domain intelligent fault diagnosis of rolling bearing based on distance metric transfer learning." Advances in Mechanical Engineering 14, no. 11 (November 2022): 168781322211357. http://dx.doi.org/10.1177/16878132221135740.

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Rolling bearings are present ubiquitously in mechanical equipment, timely fault diagnosis has great significance in guaranteeing the safety of mechanical operation. In real world industrial applications, the distribution of training dataset (source domain) and testing dataset (target domain) is often different and varies with operating environment, which may lead to performance degradation. In this study, a cross-domain fault diagnosis of rolling bearing method based on distance metric transfer learning (DMTL) and wavelet packet decomposition (WPD) is proposed. The Mahalanobis distance is adopted for learning the intrinsic similarity or dissimilarity between instances and learned by simultaneously maximizing the intra-class distances and minimizing the inter-class distances for target domain. The features of source domain and target domain are first extracted from original vibration signals by WPD which is a powerful tool in dealing with non-stationary signals and can provide meticulous analysis. Then, the DMTL model is adopted to eliminate the error propagation across different components, which can weaken the weight of low-quality instances and enhance the weight of high-quality samples. Finally, the k-nearest neighbor (KNN) classifier is applied to accomplish the cross-domain intelligent fault-type classification. The superiority and effectiveness of the proposed fault diagnosis model is validated by two diagnosis cases. The experimental results demonstrated that the proposed method performs better than other compared methods in recognizing various fault types and has the capability in handling the complex cross-domain adaptation scenarios.

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Zhao, Chao, and Weiming Shen. "Dual adversarial network for cross-domain open set fault diagnosis." Reliability Engineering & System Safety 221 (May 2022): 108358. http://dx.doi.org/10.1016/j.ress.2022.108358.

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Zheng, Huailiang, Rixin Wang, Yuantao Yang, Jiancheng Yin, Yongbo Li, Yuqing Li, and Minqiang Xu. "Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review." IEEE Access 7 (2019): 129260–90. http://dx.doi.org/10.1109/access.2019.2939876.

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Chao, Ko-Chieh, Chuan-Bi Chou, and Ching-Hung Lee. "Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data." Sensors 22, no. 12 (June 16, 2022): 4540. http://dx.doi.org/10.3390/s22124540.

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Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched. In this paper, the scenarios in real manufacturing site are considered, that the target domain data is much less than source domain data at the beginning, but the number of target domain data will increase as time goes by. A novel method is proposed for fault diagnosis of rolling bearing with online imbalanced cross-domain data. Finally, the proposed method which is tested on bearing dataset (CWRU) has achieved prediction accuracy of 95.89% with only 40 target samples. The results have been compared with other traditional methods. The comparisons show that the proposed online domain adaptation fault diagnosis method has achieved significant improvements. In addition, the deep transfer learning model by adaptive- network-based fuzzy inference system (ANFIS) is introduced to interpretation the results.

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Feiyan Fan, Feiyan Fan, Jiazhen Hou Feiyan Fan, and Tanghuai Fan Jiazhen Hou. "Fault Diagnosis under Varying Working Conditions with Domain Adversarial Capsule Networks." 電腦學刊 33, no. 3 (June 2022): 135–46. http://dx.doi.org/10.53106/199115992022063303011.

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<p>Most existing studies that develop fault diagnosis methods focus on performance under steady operation while overlooking adaptability under varying working conditions. This results in the low generalization of the fault diagnosis methods. In this study, a novel deep transfer learning architecture is proposed for fault diagnosis under varying working conditions. A modified capsule network is developed by combining the domain adversarial framework and classical capsule network to simultaneously recognize the machinery fault and working conditions. The novelty of the proposed architecture mainly lies in the integration of the domain adversarial mechanism and capsule network. The idea of the domain adversarial mechanism is exploited in transfer learning, which can achieve a promising performance in cross-condition fault diagnosis tasks. With the novel architecture, learned features exhibit identical or very similar distributions in the source and target domains. Hence, the deep learning architecture trained in one working condition can be applicable to discriminative conditions without being hindered by the shift between the two domains. The proposed method is applied to analyze vibrations of a bearing system acquired under different working conditions, i.e., loads and rolling speed. The experimental results indicate that the proposed method outperforms other state-of-the-art methods in fault diagnosis under varying working conditions.</p> <p> </p>

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Qin, Y. X., Y. Hong, J. Y. Long, Z. Yang, Y. W. Huang, and C. Li. "Attitude data-based deep transfer capsule network for intelligent fault diagnosis of delta 3D printers." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2184/1/012017.

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Abstract In order to improve the quality of printed products and promote the application of 3D printing, it is necessary to carry out health monitoring and fault diagnosis for 3D printers. In this paper, an attitude data-based deep transfer capsule network is proposed for intelligent fault diagnosis of delta 3D printers. Based on the forward kinematic analysis, the attitude data change of the moving platform can reflect the fault information of the printers. To extract fault features from the attitude data with rich directional pose information and complete the cross-domain diagnosis task effectively, the proposed approach consists of a feature encoder with capsule layer, a fault pattern classifier, and a domain discriminator. Through the domain adversarial training, the model can minimize the difference between the source domain and the target domain data distribution, and the trained classifier can obtain better diagnosis performance in the target domain. The experiment result demonstrates the superiority and effectiveness of the proposed method for fault diagnosis problems of delta 3D printers.

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Kim, Taeyun, and Jangbom Chai. "Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing." Sensors 21, no. 15 (July 21, 2021): 4970. http://dx.doi.org/10.3390/s21154970.

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Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy.

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Zhang, Yongchao, Zhaohui Ren, Ke Feng, Kun Yu, Michael Beer, and Zheng Liu. "Universal source-free domain adaptation method for cross-domain fault diagnosis of machines." Mechanical Systems and Signal Processing 191 (May 2023): 110159. http://dx.doi.org/10.1016/j.ymssp.2023.110159.

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Wang, Yu, Jie Gao, Wei Wang, Xu Yang, and Jinsong Du. "Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift." Mechanical Systems and Signal Processing 212 (April 2024): 111295. http://dx.doi.org/10.1016/j.ymssp.2024.111295.

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Zheng, Huailiang, Yuantao Yang, Jiancheng Yin, Yuqing Li, Rixin Wang, and Minqiang Xu. "Deep Domain Generalization Combining A Priori Diagnosis Knowledge Toward Cross-Domain Fault Diagnosis of Rolling Bearing." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–11. http://dx.doi.org/10.1109/tim.2020.3016068.

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Zou, Yingyong, Wenzhuo Zhao, Tao Liu, Xingkui Zhang, and Yaochen Shi. "Research on High-Speed Train Bearing Fault Diagnosis Method Based on Domain-Adversarial Transfer Learning." Applied Sciences 14, no. 19 (September 26, 2024): 8666. http://dx.doi.org/10.3390/app14198666.

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Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit (CRU) is proposed for this purpose. The feature extraction module, which includes a one-dimensional convolutional (Cov1d) layer, a normalization layer, a ReLU activation function, and a max-pooling layer, is integrated with the CRU to form a feature extractor capable of learning key fault-related features. Additionally, the fault identification module and domain discrimination module utilize a combination of fully connected layers and dropout to reduce model parameters and mitigate the risk of overfitting. It is experimentally validated on two sets of bearing datasets, and the results show that the performance of the proposed method is better than other diagnostic methods under cross-load conditions, and it can be used as an effective cross-load bearing fault diagnosis method.

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Xie, Fengyun, Gang Li, Qiuyang Fan, Qian Xiao, and Shengtong Zhou. "Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion." Processes 11, no. 10 (September 28, 2023): 2862. http://dx.doi.org/10.3390/pr11102862.

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Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.

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Chen, Zihan, and Chao He. "Transformer-Based Unsupervised Cross-Sensor Domain Adaptation for Electromechanical Actuator Fault Diagnosis." Machines 11, no. 1 (January 11, 2023): 102. http://dx.doi.org/10.3390/machines11010102.

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There have been some successful attempts to develop data-driven fault diagnostic methods in recent years. A common assumption in most studies is that the data of the source and target domains are obtained from the same sensor. Nevertheless, because electromechanical actuators may have complex motion trajectories and mechanical structures, it may not always be possible to acquire the data from a particular sensor position. When the sensor locations of electromechanical actuators are changed, the fault diagnosis problem becomes further complicated because the feature space is significantly distorted. The literature on this subject is relatively underdeveloped despite its critical importance. This paper introduces a Transformer-based end-to-end cross-sensor domain fault diagnosis method for electromechanical actuators to overcome these obstacles. An enhanced Transformer model is developed to obtain domain-stable features at various sensor locations. A convolutional embedding method is also proposed to improve the model’s ability to integrate local contextual information. Further, the joint distribution discrepancy between two sensor domains is minimized by using Joint Maximum Mean Discrepancy. Finally, the proposed method is validated using an electromechanical actuator dataset. Twenty-four transfer tasks are designed to validate cross-sensor domain adaptation fault diagnosis problems, covering all combinations of three sensor locations under different operating conditions. According to the results, the proposed method significantly outperforms the comparative method in terms of varying sensor locations.

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Zhang, Yizong, Shaobo Li, Ansi Zhang, Chuanjiang Li, and Ling Qiu. "A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets." Entropy 24, no. 9 (September 14, 2022): 1295. http://dx.doi.org/10.3390/e24091295.

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At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method.

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Wei, Yuqian. "Bearing fault diagnosis based on XWT-CEEMD noise reduction." Journal of Physics: Conference Series 2196, no. 1 (February 1, 2022): 012035. http://dx.doi.org/10.1088/1742-6596/2196/1/012035.

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Abstract In recent years, bearing fault diagnosis has been a research hotspot. In order to improve the reliability of acoustic fault diagnosis, this paper combines Cross Wavelet Transform (XWT) and complementary ensemble empirical mode decomposition (CEEMD) to extract bearing fault features from acoustic signals. Finally, the time-domain features and spectral centroid are input into the SVM for fault classification. The results show that the proposed method can effectively improve the reliability of acoustic fault diagnosis.

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Ha, Jong Moon, and Olga Fink. "Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis." Mechanical Systems and Signal Processing 202 (November 2023): 110680. http://dx.doi.org/10.1016/j.ymssp.2023.110680.

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Xiao, Zhiguo, Dongni Li, Chunguang Yang, and Wei Chen. "Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning." Sensors 24, no. 16 (August 10, 2024): 5181. http://dx.doi.org/10.3390/s24165181.

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To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability. Secondly, based on the domain adaptation (DA) cross-domain feature adversarial learning strategy of transfer learning methods, the extraction of optimal domain-invariant features is achieved by reducing the gap in data distribution between the target domain and the source domain, addressing the call for research on fault diagnosis across operating conditions, equipment, and virtual–real migrations. Finally, experiments were conducted to verify and optimize the effectiveness of the feature extraction and fusion network. A public bearing dataset was used as the source domain data, and special vehicle bearing data were selected as the target domain data for comparative experiments on the effect of network transfer learning. The experimental results demonstrate that the proposed method exhibits an exceptional performance in cross-domain and variable load environments. In multiple bearing cross-domain transfer learning tasks, the method achieves an average migration fault diagnosis accuracy rate of up to 98.65%. When compared with existing methods, the proposed method significantly enhances the ability of data feature extraction, thereby achieving a more robust diagnostic performance.

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Montesuma, Eduardo Fernandes, Michela Mulas, Francesco Corona, and Fred-Maurice Ngole Mboula. "Cross-domain fault diagnosis through optimal transport for a CSTR process." IFAC-PapersOnLine 55, no. 7 (2022): 946–51. http://dx.doi.org/10.1016/j.ifacol.2022.07.566.

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Tian, Jilun, Jiusi Zhang, Yuchen Jiang, Shimeng Wu, Hao Luo, and Shen Yin. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis." Reliability Engineering & System Safety 243 (March 2024): 109891. http://dx.doi.org/10.1016/j.ress.2023.109891.

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Li, Guofa, Shaoyang Liu, Jialong He, Liang Wang, Chenchen Wu, and Chenhui Qian. "A multi-domain adversarial transfer network for cross domain fault diagnosis under imbalanced data." Engineering Applications of Artificial Intelligence 136 (October 2024): 108948. http://dx.doi.org/10.1016/j.engappai.2024.108948.

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Wen, Weigang, Yihao Bai, and Weidong Cheng. "Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions." Sensors 20, no. 6 (March 18, 2020): 1685. http://dx.doi.org/10.3390/s20061685.

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Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.

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Shen, Bingbing, Min Zhang, Le Yao, and Zhihuan Song. "Novel Triplet Loss-Based Domain Generalization Network for Bearing Fault Diagnosis with Unseen Load Condition." Processes 12, no. 5 (April 26, 2024): 882. http://dx.doi.org/10.3390/pr12050882.

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In the real industrial manufacturing process, due to the constantly changing operational loads of equipment, it is difficult to collect data from all load conditions as the source domain signal for fault diagnosis. Therefore, the appearance of unseen load vibration signals in the target domain presents a challenge and research hotspot in fault diagnosis. This paper proposes a triplet loss-based domain generalization network (TL-DGN) and then applies it to an unseen domain bearing fault diagnosis. TL-DGN first utilizes a feature extractor to construct a multi-source domain classification loss. Furthermore, it measures the distance between class data from different domains using triplet loss. The introduced triplet loss can narrow the distance between samples of the same class in the feature space and widen the distance between samples of different classes based on the action of the cross-entropy loss function. It can reduce the dependency of the classification boundary on bearing operational loads, resulting in a more generalized classification model. Finally, two comparative experiments with fault diagnosis models without triplet loss and other classification models demonstrate that the proposed model achieves superior fault diagnosis performance.

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She, Daoming, Zhichao Yang, Yudan Duan, Xiaoan Yan, Jin Chen, and Yaoming Li. "A meta transfer learning method for gearbox fault diagnosis with limited data." Measurement Science and Technology 35, no. 8 (May 9, 2024): 086114. http://dx.doi.org/10.1088/1361-6501/ad4665.

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Abstract Intelligent diagnosis of mechanical faults is an important means to guarantee the safe maintenance of equipment. Cross domain diagnosis may lack sufficient measurement data as support, and this bottleneck is particularly prominent in high-end manufacturing. This paper presents a few-shot fault diagnosis methodology based on meta transfer learning for gearbox. To be specific, firstly, the subtasks for transfer diagnosis are constructed, and then joint distribution adaptation is conducted to align the two domain distributions; secondly, through adaptive manifold regularization, the data of target working condition is further utilized to explore the potential geometric structure of the data distribution. Meta stochastic gradient descent is explored to dynamically adjust the model’s parameter based on the obtained task information to obtain better generalization performance, ultimately to achieve transfer diagnosis of gearbox faults with few samples. The effectiveness of the approach is supported by the experimental datasets of the gearbox.

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Zhong, Zhidan, Zhihui Zhang, Yunhao Cui, Xinghui Xie, and Wenlu Hao. "Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions." Electronics 13, no. 11 (May 30, 2024): 2133. http://dx.doi.org/10.3390/electronics13112133.

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Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios.

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An, Jing, Ping Ai, and Dakun Liu. "Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning." Shock and Vibration 2020 (March 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/4676701.

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Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

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Zhai, Lubin, Xiufeng Wang, Zeyiwen Si, and Zedong Wang. "A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum." Sensors 24, no. 11 (May 29, 2024): 3500. http://dx.doi.org/10.3390/s24113500.

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Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

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Xu, Shu, Jian Ma, and Dengwei Song. "Open-set Federated Adversarial Domain Adaptation Based Cross-domain Fault Diagnosis." Measurement Science and Technology, July 13, 2023. http://dx.doi.org/10.1088/1361-6501/ace734.

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Abstract Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.

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Jia, Feng, Yuanfei Wang, Jianjun Shen, Lifei Hao, and Zhaoyu Jiang. "Stepwise feature norm network with adaptive weighting for open set cross-domain intelligent fault diagnosis of bearings." Measurement Science and Technology, February 9, 2024. http://dx.doi.org/10.1088/1361-6501/ad282f.

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Abstract Cross-domain fault diagnosis of bearings has attracted significant attention. However, traditional cross-domain diagnostic methods have the following shortcomings: (1) When the trained model is applied to a new scenario, it leads to severe degradation of the model and a reduction in its generalisation ability. (2) The accuracy of the open-set fault diagnosis is affected by additional faults in the target domain data. To overcome these shortcomings, a stepwise feature norm network with adaptive weighting (SFNAW) is proposed for cross-domain open-set fault diagnosis. In SFNAW, two weight extractors are designed to adaptively calculate the sample weights such that a threshold can be set to mark the additional fault samples of the target domain as unknown faults using these weights. Transferable features are obtained by adaptively increasing the feature norm stepwise to alleviate model degradation and align the source and target domains. Finally, the fault diagnosis knowledge of the source domain is transferred to fault recognition in the target domain. The proposed SFNAW method was verified using two bearing datasets. The results show that the SFNAW can effectively detect additional faults in the target domain and reduce model degradation, thereby improving the fault diagnosis accuracy. Meanwhile, the SFNAW method has a higher accuracy than other traditional methods.

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Li, Can, Guangbin Wang, Shubiao Zhao, Zhixian Zhong, and Ying Lv. "Cross-domain manifold structure preservation for transferable and cross-machine fault diagnosis." Journal of Vibroengineering, August 22, 2024. http://dx.doi.org/10.21595/jve.2024.24067.

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To address the decline or failure in the autonomous learning capability of traditional transfer learning methods when training and test samples come from different machines, resulting in low cross-machine fault diagnosis rates, we propose a cross-domain manifold structure preservation (CDMSP) method for diagnosing rolling bearing faults across machines. The CDMSP method can induce the manifold space projection matrices of the source and target domains more effectively. This method maps high-dimensional features into a low-dimensional manifold, preserving non-linear relationships and aligning distribution differences while maintaining cross-domain manifold structure consistency. Additionally, highly confidently labeled target domain samples are selected from each mapping result and added to the training dataset to enhance subspace learning in subsequent iterations. The CDMSP method is both simple and effective at capturing the underlying structures and patterns in the data. The CWRU dataset and our self-built test platform dataset were used to validate this method. Experimental results show that CDMSP, as a non-deep domain adaptation method of transfer learning, outperforms similar methods in cross-machine fault identification, achieving a maximum fault identification accuracy of 100 % with excellent convergence performance. Furthermore, simulated diagnostic experiments under noise interference indicate that CDMSP maintains high fault identification accuracy, even in noisy environments. Overall, CDMSP is an efficient and reliable new method for diagnosing cross-machine bearing faults.

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Mao, Xiaodong. "Cross domain fault diagnosis method based on MLP-mixer network." Journal of Measurements in Engineering, October 30, 2023. http://dx.doi.org/10.21595/jme.2023.23460.

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The quality of rolling bearings determines the safety of mechanical equipment operation, and bearings with more precise structures are prone to damage due to excessive operation. Therefore, cross domain fault diagnosis of bearings has become a research hotspot. To better improve the accuracy of bearing cross domain fault diagnosis, this study proposes two models. One is a cross domain feature extraction model constructed using a mixed attention mechanism, which recognizes and extracts high-level features of bearing faults through channel attention and spatial attention mechanisms. The other is a bearing cross domain fault diagnosis model based on multi-layer perception mechanism. This model takes the feature signals collected by the attention mechanism model as input to identify and align the differences between the source and target domain features, facilitating cross domain transfer of features. The experimental results show that the mixed attention mechanism model has a maximum accuracy of 97.3 % for feature recognition of different faults, and can successfully recognize corresponding signal values. The multi-layer perception model can achieve the highest recognition accuracy of 99.5 % in bearing fault diagnosis, and it can reach a stable state when it iterates to 26, and the final stable loss value is 0.28. Therefore, the two models proposed in this study have good application value.

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Wang, Pei, Jie Liu, Jianzhong Zhou, Ran Duan, and Wei Jiang. "Cross-domain fault diagnosis of rotating machinery based on graph feature extraction." Measurement Science and Technology, November 9, 2022. http://dx.doi.org/10.1088/1361-6501/aca16f.

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Abstract Transfer learning can realize the cross-domain fault diagnosis of rotating machinery, where the model trained on plenty of labeled samples collected in one working condition can be transferred to insufficient samples collected in target working condition. Currently, the data features cannot be completely extracted by existing methods when the data distribution gap of the samples collected in different working conditions is quite large. In order to fully extract the data features of rotating machinery to achieve cross-domain fault diagnosis, this paper investigated a cross-domain fault diagnosis model of rotating machinery based on graph feature extraction. The proposed method can realize unsupervised fault diagnosis on rotating machinery running under different working conditions by extracting the numerical and structural features of source and target domains. First of all, data features with large data distribution gaps need to be fully extracted, so a convolutional network based on deformable convolutional network (De-conv) is designed to extract the features with large differences in data distribution under various working conditions. Secondly, features are extracted based on convolutional neural network for data values in existing domain adaptation methods while the structure features of machine monitoring data are ignored. Therefore, a composite spectral-based graph convolutional network (CS-GCN) is designed to extract structural features of data. Thirdly, fully extracted features are input into universal domain adaptation network to achieve cross-domain fault diagnosis with private faults of rotating machinery under changing working conditions. Finally, a benchmarking dataset and a dataset collected from a practical experimental platform are used to verify the effectiveness of the proposed model, and the results show that it is more suitable for cross-domain fault diagnosis of rotating machinery than other comparison models. Keywords: Graph feature extraction; Cross-domain fault diagnosis; Deformable convolutional network; Domain adaptation; Rotating machinery.

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Lu, Weikai, Haoyi Fan, Kun Zeng, Zuoyong Li, and Jian Chen. "Self‐supervised domain adaptation for cross‐domain fault diagnosis." International Journal of Intelligent Systems, September 2, 2022. http://dx.doi.org/10.1002/int.23026.

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Liao, Yixiao, Ruyi Huang, Jipu Li, Zhuyun Chen, and Weihua Li. "Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis." Chinese Journal of Mechanical Engineering 34, no. 1 (June 4, 2021). http://dx.doi.org/10.1186/s10033-021-00566-3.

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AbstractIn machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence. In this paper, a dynamic distribution adaptation based transfer network (DDATN) is proposed for cross domain bearing fault diagnosis. DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy (IDMMD) for dynamic distribution adaptation (DDA), which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain. The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.

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Journal articles on the topic 'Cross-domain fault diagnosis'

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