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  • 1.
    Cao, Hongru
    et al.
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Zhong, Xiang
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Deng, Qianwang
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Yang, Xingkai
    Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
    Xuan, Jianping
    School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 China.
    Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 186-198Article in journal (Refereed)
    Abstract [en]

    The existing deep transfer learning-based intelligent fault diagnosis studies for machinery mainly consider steady speed scenarios, and there exists a problem of low diagnosis efficiency. In order to overcome these limitations, an unsupervised domain-share convolutional neural network (CNN) is proposed for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. First, a Cauchy kernel-induced maximum mean discrepancy based on unbiased estimation is developed for improving the efficiency and robustness of feature adaptation. Secondly, an unsupervised domain-share CNN is constructed to simultaneously extract the domain-invariant features from the source domain and the target domain. Finally, adjustable and segmented balance factors are designed to flexibly weigh the distribution-adaptation loss and cross-entropy loss to improve diagnosis accuracy and transferability. The proposed method analyzes raw vibration signals collected from bearings and gears under different rotating speeds. Results of case studies show that the proposed method can achieve higher diagnosis accuracy, faster convergence, and better robustness than the reported methods, which demonstrates its potential applications in machine fault transfer diagnosis from a steady speed condition to a time-varying speed condition.

  • 2.
    Haidong, Shao
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Junsheng, Cheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Hongkai, Jiang
    School of Aeronautics, Northwestern Polytechnical University, Xi’an, China.
    Yu, Yang
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Zhantao, Wu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing2020In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 188, article id 105022Article in journal (Refereed)
    Abstract [en]

    Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.

  • 3.
    Han, Songyu
    et al.
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Huo, Zhiqiang
    Institute of Health Informatics, University College London, London, UK.
    Yang, Xingkai
    Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada.
    Cheng, Junsheng
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
    End-to-end chiller fault diagnosis using fused attention mechanism and dynamic cross-entropy under imbalanced datasets2022In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 212, article id 108821Article in journal (Refereed)
    Abstract [en]

    Fault diagnosis techniques play an increasingly important role in the operation and maintenance of smart city systems. Artificial intelligence improves the efficiency of chiller system fault diagnosis, and greatly reduces the energy consumption of urban buildings. The existing intelligent fault diagnosis methods of chiller mostly rely on balanced training datasets; lacking fault samples makes these methods incompetent to extract reliable features to recognize abnormal machine conditions, resulting in the degraded performance. To overcome the deficiencies of reported studies, a new method, called end-to-end chiller fault diagnosis, is proposed using a fused attention mechanism and dynamic cross-entropy. Firstly, a one-dimensional convolution network (1D-CNN) and long-short term memory (LSTM) are combined to capture the spatial-temporal features from the original data directly. Afterwards, a fused attention mechanism is developed to further refine the extracted features to increase the contribution of crucial features and achieve high-quality diagnostic information mining. Finally, the dynamic cross-entropy (DCE) is designed for updating the imbalance factor in real-time, with more focus on the hard-classified types. The experimental analysis results demonstrate the feasibility and superiority of the proposed method in identifying chiller system faults with imbalanced datasets.

  • 4.
    He, Zhiyi
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Cheng, Junsheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Yang, Yu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Xiang, Jiawei
    College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China.
    Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals2020In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 163, article id 107965Article in journal (Refereed)
    Abstract [en]

    The methods based on traditional pattern recognition and deep learning have been successfully applied in gearbox intelligent diagnosis. However, traditional pattern recognition methods cannot directly classify feature tensors of multi-source signals, and deep learning networks hardly handle the classification of small samples. Therefore, for the gearbox intelligent diagnosis with multi-source signals, a novel tensor classifier called kernel flexible and displaceable convex hull based tensor machine (KFDCH-TM) is proposed. In KFDCH-TM, the kernel flexible and displaceable convex hull of tensor samples in tensor feature space is defined firstly. Then, an optimal separating hyper-plane between two kernel flexible and displaceable convex hulls is constructed. Meanwhile, feature tensors extracted from multi-source signals through wavelet packet transform (WPT) are used to diagnose gearbox fault by KFDCH-TM. The results of two cases demonstrate that KFDCH-TM can effectively identify gearbox fault with multi-source signals and has better robustness.

  • 5.
    He, Zhiyi
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Wang, Ping
    AECC Hunan Aviation Powerplant Research Institute. AECC Key Laboratory of Aero-engine Vibration Technology.
    Lin, Jing (Janet)
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Cheng, Junsheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Yang, Yu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples2020In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 191, article id 105313Article in journal (Refereed)
    Abstract [en]

    Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.

  • 6.
    Li, Xin
    et al.
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China.
    Li, Yong
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China.
    Yan, Ke
    Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, 117566, Singapore.
    Shao, Haidong
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, PR China.
    Lin, Janet (Jing)
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna 63220, Sweden.
    Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging2023In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 230, article id 108921Article in journal (Refereed)
    Abstract [en]

    Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.

  • 7.
    Luo, Jingjie
    et al.
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Cao, Hongru
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Chen, Xingkai
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Cai, Baoping
    College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580 China.
    Liu, Bin
    Department of Management Science, University of Strathclyde, Glasgow G1 1XQ, UK.
    Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 65, p. 180-191Article in journal (Refereed)
    Abstract [en]

    Existing researches about unsupervised cross-domain bearing fault diagnosis mostly consider global alignment of feature distributions in various domains, and focus on relatively ideal diagnosis scenario under the steady speeds. Therefore, unsupervised feature adaptation between all the corresponding subdomains under speed fluctuation remains great challenges. This paper proposes a modified deep subdomain adaptation network (MDSAN) for more practical and challenging cross-domain diagnostic scenarios from the fluctuating speeds to steady speeds. Firstly, to extract the representative features and effectively suppress negative transfer, a novel shared feature extraction module guided by multi-headed self-attention mechanism is constructed. Then, a new trade-off factor is designed to improve the convergence performance and optimization process of MDSAN. The proposed method is used for analyzing experimental bearing vibration data, and the results show that it has higher diagnostic accuracy, faster convergence, better distribution alignment, and is more suitable for unsupervised cross-domain fault diagnosis under speed fluctuation scenario compared with the existing methods.

  • 8.
    Shao, Haidong
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Zhang, Liangwei
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan 523808, China.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Donostia-San, Sebastian 20009, Spain.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance2021In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 74, p. 65-76Article in journal (Refereed)
    Abstract [en]

    Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.

  • 9.
    Shao, Haidong
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Zhang, Liangwei
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
    Wei, Muheng
    ZhenDui Industry Artificial Intelligence Co., Ltd, Shenzhen, China.
    Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra2020In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 32, no 3, p. 342-353Article in journal (Refereed)
    Abstract [en]

    Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.

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  • 10.
    Shao, Haidong
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Ziyang, Ding
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Junsheng, Cheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Hongkai, Jiang
    School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China.
    Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO2020In: ISA transactions, ISSN 0019-0578, E-ISSN 1879-2022, Vol. 105, p. 308-319Article in journal (Refereed)
    Abstract [en]

    Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines.

  • 11.
    Yan, Shen
    et al.
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
    Xiao, Yiming
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
    Liu, Bin
    Department of Management Science, University of Strathclyde, Glasgow, G1 1XQ, UK.
    Wan, Jiafu
    Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, 510641, China.
    Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 79, article id 102441Article in journal (Refereed)
    Abstract [en]

    Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.

  • 12.
    Zhang, Liangwei
    et al.
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Shao, Haidong
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Zhang, Zhicong
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
    Yan, Xiaohui
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
    Long, Jianyu
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
    End-To-End Unsupervised Fault Detection Using A Flow-Based Model2021In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 215, article id 107805Article in journal (Refereed)
    Abstract [en]

    Fault detection has been extensively studied in both academia and industry. The rareness of faulty samples in the real world restricts the use of many supervised models, and the reliance on domain expertise for feature engineering raises Other barriers. For this reason, this paper proposes an unsupervised, end-to-end approach to fault detection based on a flow-based model, the Nonlinear Independent Components Estimation (NICE) model. A NICE model models a target distribution via a sequence of invertible transformations to a prior distribution in the latent space. We prove that, under certain conditions, the L2-norm of normal samples’ latent codes in a trained NICE model is Chi-distributed. This facilitates the use of hypothesis testing for fault detection purpose. Concretely, we first apply Zero-phase Component Analysis to decorrelate the data of normal states. The whitened data are fed to a NICE model for training, in a maximum likelihood sense. At the testing stage, samples whose L2-norm of latent codes fail in the hypothesis testing are suspected of being generated by different mechanisms and hence regarded as potential faults. The proposed approach was validated on two datasets of vibration signals; it proved superior to several alternatives. We also show the use of NICE, a type of generative model, can produce real-like vibration signals because of the model's bijective nature.

  • 13.
    Zhiyi, He
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Haidong, Shao
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Junsheng, Cheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Yu, Yang
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder2020In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 152, article id 107393Article in journal (Refereed)
    Abstract [en]

    The collected vibration data with labeled information from bearing is far insufficient in engineering practice, which is challenging for training an intelligent diagnosis model. For this purpose, enhanced deep transfer auto-encoder is proposed for fault diagnosis of bearing installed in different machines. First, scaled exponential linear unit is used to improve the quality of the mapped vibration data collected from bearing. Second, nonnegative constraint is adopted for modifying the loss function to improve reconstruction effect. Then, the parameter knowledge of the well-trained source model is transferred to the target model. Finally, target training samples with limited labeled information are adopted for fine-tuning the target model to match the characteristics of the target testing samples. The proposed approach is applied for analyzing the measured vibration signals of bearings installed in different machines. The analysis results show that the proposed approach holds better transfer diagnosis performance compared with the existing approaches.

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