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Publications (10 of 12) Show all publications
Yan, S., Shao, H., Xiao, Y., Liu, B. & Wan, J. (2023). Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robotics and Computer-Integrated Manufacturing, 79, Article ID 102441.
Open this publication in new window or tab >>Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
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2023 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 79, article id 102441Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Machine tools, Deep learning, Unsupervised anomaly detection, Hybrid robust convolutional autoencoder, Noises
National Category
Computer Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-92934 (URN)10.1016/j.rcim.2022.102441 (DOI)000858951200003 ()2-s2.0-85136655549 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-09-16 (hanlid);

Funder: National Natural Science Foundation of China (51905160); Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017)

Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2022-11-09Bibliographically approved
Han, S., Shao, H., Huo, Z., Yang, X. & Cheng, J. (2022). End-to-end chiller fault diagnosis using fused attention mechanism and dynamic cross-entropy under imbalanced datasets. Building and Environment, 212, Article ID 108821.
Open this publication in new window or tab >>End-to-end chiller fault diagnosis using fused attention mechanism and dynamic cross-entropy under imbalanced datasets
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2022 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 212, article id 108821Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Chiller fault diagnosis, Dynamic cross-entropy, Fused attention mechanism, Imbalanced datasets, Smart city systems
National Category
Computer Systems
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-89161 (URN)10.1016/j.buildenv.2022.108821 (DOI)000829304100005 ()2-s2.0-85123754923 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-02-10 (sofila);

Funder: National Key Research andDevelopment of China (No. 2020YFB1712100); the National NaturalScience Foundation of China (No. 51905160); the Natural ScienceFund for Excellent Young Scholars of Hunan Province (No.2021JJ20017)

Available from: 2022-02-10 Created: 2022-02-10 Last updated: 2022-10-28Bibliographically approved
Luo, J., Shao, H., Cao, H., Chen, X., Cai, B. & Liu, B. (2022). Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation. Journal of manufacturing systems, 65, 180-191
Open this publication in new window or tab >>Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation
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2022 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 65, p. 180-191Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier B.V., 2022
Keywords
Cross-domain bearing fault diagnosis, Modified deep subdomain adaptation network, Multi-headed self-attention mechanism, New trade-off factor, Speed fluctuation
National Category
Computer Sciences
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93768 (URN)10.1016/j.jmsy.2022.09.004 (DOI)000911575300007 ()2-s2.0-85138051221 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-01 (joosat);

Funder: Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017); National Natural Science Foundation of China (51905160); Student Innovation Training Program (S202110532210)

Available from: 2022-11-01 Created: 2022-11-01 Last updated: 2023-05-08Bibliographically approved
Cao, H., Shao, H., Zhong, X., Deng, Q., Yang, X. & Xuan, J. (2022). Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds. Journal of manufacturing systems, 62, 186-198
Open this publication in new window or tab >>Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
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2022 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 186-198Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Unsupervised domain-share CNN, Fault transfer diagnosis, Time-varying speeds, Cauchy kernel-induced maximum mean difference, Adjustable and segmented factors
National Category
Control Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-88192 (URN)10.1016/j.jmsy.2021.11.016 (DOI)000784306700006 ()2-s2.0-85120330983 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-12-02 (johcin)

Available from: 2021-12-02 Created: 2021-12-02 Last updated: 2022-10-28Bibliographically approved
Shao, H., Lin, J., Zhang, L., Galar, D. & Kumar, U. (2021). A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance. Information Fusion, 74, 65-76
Open this publication in new window or tab >>A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
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2021 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 74, p. 65-76Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Collaborative maintenance, Prognostics and health management, Multi-sensor information fusion, fault diagnosis, Stacked wavelet auto-encoder, Planetary gearbox
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-83400 (URN)10.1016/j.inffus.2021.03.008 (DOI)000659136200005 ()2-s2.0-85103775860 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-04-19 (johcin);

Finansiär: National Natural Science Foundation of China (51905160, 71801045); the Natural Science Foundation of Hunan Province (2020JJ5072); National Key Research and Development Program of China (2020YFB1712103); Fundamental Research Funds for the Central Universities (531118010335); Research start-up funds of DGUT (GC300502-46)

Available from: 2021-03-25 Created: 2021-03-25 Last updated: 2022-10-28Bibliographically approved
Zhang, L., Lin, J., Shao, H., Zhang, Z., Yan, X. & Long, J. (2021). End-To-End Unsupervised Fault Detection Using A Flow-Based Model. Reliability Engineering & System Safety, 215, Article ID 107805.
Open this publication in new window or tab >>End-To-End Unsupervised Fault Detection Using A Flow-Based Model
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2021 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 215, article id 107805Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Prognostics and health management, Fault detection, Deep learning, Unsupervised learning, Flow-based models
National Category
Software Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-84635 (URN)10.1016/j.ress.2021.107805 (DOI)000690283800019 ()2-s2.0-85107630379 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-06-18 (johcin);

Forskningsfinansiärer: National Natural Science Foundation of China (71801045, 71801046, 51905160); the Research start-up funds of DGUT (GC300502-46); the Natural Science Foundation of Hunan Province (2020JJ5072); the National Key Research and Development Program of China (2020YFB1712103); the Fundamental Research Funds for the Central Universities (531118010335)

Available from: 2021-05-26 Created: 2021-05-26 Last updated: 2022-10-28Bibliographically approved
Shao, H., Lin, J., Zhang, L. & Wei, M. (2020). Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra. Quality Engineering, 32(3), 342-353
Open this publication in new window or tab >>Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra
2020 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 32, no 3, p. 342-353Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2020
Keywords
Adaptive dual-tree complex wavelet packet, compound faults, prognostics and health management, rolling bearing, singular value decomposition
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-78978 (URN)10.1080/08982112.2020.1749654 (DOI)000536343700001 ()2-s2.0-85085294687 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-08-18 (johcin)

Available from: 2020-05-25 Created: 2020-05-25 Last updated: 2022-10-28Bibliographically approved
He, Z., Shao, H., Wang, P., Lin, J. (., Cheng, J. & Yang, Y. (2020). Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples. Knowledge-Based Systems, 191, Article ID 105313.
Open this publication in new window or tab >>Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
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2020 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 191, article id 105313Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Deep transfer multi-wavelet auto-encode, Gearbox fault, Transfer diagnosis, Variable working conditions, Few target training samples
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-77568 (URN)10.1016/j.knosys.2019.105313 (DOI)000517663200035 ()2-s2.0-85076527537 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-03-02 (alebob)

Available from: 2020-01-30 Created: 2020-01-30 Last updated: 2022-10-28Bibliographically approved
Haidong, S., Junsheng, C., Hongkai, J., Yu, Y. & Zhantao, W. (2020). Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing. Knowledge-Based Systems, 188, Article ID 105022.
Open this publication in new window or tab >>Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
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2020 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 188, article id 105022Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Enhanced deep gated recurrent unit, Bearing, Early fault prognosis, Energy moment entropy, Modified training algorithm
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76058 (URN)10.1016/j.knosys.2019.105022 (DOI)000513295000018 ()2-s2.0-85071880024 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-01-28 (johcin)

Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2022-10-28Bibliographically approved
Shao, H., Ziyang, D., Junsheng, C. & Hongkai, J. (2020). Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO. ISA transactions, 105, 308-319
Open this publication in new window or tab >>Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO
2020 (English)In: ISA transactions, ISSN 0019-0578, E-ISSN 1879-2022, Vol. 105, p. 308-319Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Intelligent fault diagnosis, Different rotating machines, Novel stacked transfer auto-encoder, Parameter transfer strategy, Particle swarm optimization
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-79264 (URN)10.1016/j.isatra.2020.05.041 (DOI)000579489500025 ()32473735 (PubMedID)2-s2.0-85085291671 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-10-27 (alebob)

Available from: 2020-06-08 Created: 2020-06-08 Last updated: 2022-10-28Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8018-1774

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