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学者姓名：雷亚国
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Abstract ：
The application of robotic technologies in building construction leads to great convenience and productivity improvement, and construction robots (CRs) bring enormous opportunities for the way we conduct design and construction. To get a better understanding of the trends and track the application of CRs for on-site conditions, this paper conducts a systematic review of control models and status monitoring of CRs, which are two key aspects that determine construction accuracy and efficiency. Control accuracy and flexibility are primary needs for CRs applied in different scenes, so the control methods based on driving models are vitally important. Status monitoring on CRs contains knowledge in fault detection, intelligence maintenance, and fault-tolerant control, and multiple objectives need to be met and optimized in the whole drive chain. Moreover, the state-of-the-art is comprehensively summarized, and new insights are also provided to carry on promising researches.
Keyword ：
construction robot control strategy dynamic model intelligent operation and maintenance
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| GB/T 7714 | Shi, Huaitao , Li, Ranran , Bai, Xiaotian et al. A review for control theory and condition monitoring on construction robots [J]. | JOURNAL OF FIELD ROBOTICS , 2023 . |
| MLA | Shi, Huaitao et al. "A review for control theory and condition monitoring on construction robots" . | JOURNAL OF FIELD ROBOTICS (2023) . |
| APA | Shi, Huaitao , Li, Ranran , Bai, Xiaotian , Zhang, Yixing , Min, Linggang , Wang, Dong et al. A review for control theory and condition monitoring on construction robots . | JOURNAL OF FIELD ROBOTICS , 2023 . |
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Abstract ：
Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are still extremely limited. In other words, fault diagnosis of real machines is actually a few-shot diagnosis problem. To deal with few-shot diagnosis, this article proposes adaptive knowledge transfer with multiclassifier ensemble (AKTME) under the paradigm of continual machine learning. In AKTME, knowledge learned by DL models is considered to be represented by the learnable filter kernels (FKs). The key of AKTME is a proposed continual weighted updating (CWU) technique of FKs. By CWU, shared FKs are distilled from multiple auxiliary tasks and adaptively transferred to the target task. Then by multiclassifier ensemble, AKTME is able to recognize faults with few fault data accessible. AKTME is applied on two few-shot diagnosis cases. Results verify that AKTME achieves higher diagnosis accuracies than recently proposed methods. Moreover, AKTME tends to improve the diagnosis accuracy as it prelearns on more auxiliary tasks continually.
Keyword ：
Adaptation models Continual machine learning (CML) Data models Fault diagnosis few-shot learning Kernel Knowledge transfer mechanical fault diagnosis restricted Boltzmann machine Task analysis Training transfer learning
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| GB/T 7714 | Xing, Saibo , Lei, Yaguo , Yang, Bin et al. Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2022 , 69 (2) : 1968-1976 . |
| MLA | Xing, Saibo et al. "Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 69 . 2 (2022) : 1968-1976 . |
| APA | Xing, Saibo , Lei, Yaguo , Yang, Bin , Lu, Na . Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2022 , 69 (2) , 1968-1976 . |
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Abstract ：
Remaining useful life (RUL) prediction and maintenance optimization are two critical and sequentially connected modules in the prognostics and health management of machines. Due to the advantages of obtaining more accurate RUL prediction results and the effectiveness of addressing replacement scheduling and spare parts provision dynamically, ensemble RUL prediction and online joint replacement-order optimization are paid specific attention to. Despite substantial works on those two aspects, there are still two limitations that compromise their performances in practical applications: 1) Existing ensemble RUL prediction methods neglected the nonlinear relationships among individual prediction models. 2) No online joint optimization model that utilizes ensemble RUL information is available. Faced with these two limitations, this paper first proposes a nonlinear ensemble RUL prediction method, which takes nonlinear relationships among models into consideration. Furthermore, an online joint replacement-order model is formulated using the ensemble RUL prediction results, and an iterated local search based optimization algorithm is utilized for dynamically finding the near-optimal joint policies. Through the experimental study of milling cutter life tests, the proposed nonlinear ensemble RUL prediction method is verified with higher accuracy, and the joint optimization model utilizing the ensemble RUL results is shown to provide more effective joint policies.
Keyword ：
machine Nonlinear ensemble Online joint optimization Prognostics and health management of  Remaining useful life prediction
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| GB/T 7714 | Yan, Tao , Lei, Yaguo , Li, Naipeng et al. Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 173 . |
| MLA | Yan, Tao et al. "Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 173 (2022) . |
| APA | Yan, Tao , Lei, Yaguo , Li, Naipeng , Si, Xiaosheng , Pintelon, Liliane , Dewil, Reginald . Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 173 . |
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Abstract ：
Remaining useful life (RUL) prediction is critical for ensuring the safe and efficient operation of machinery. Due to the existence of multiple influencing factors, the degradation of machinery is often described as dependent competing failure processes (DCFPs). Extensive studies have been conducted on the degradation modeling and RUL prediction for DCFPs. However, they suffer from two limitations: 1) no analytical expression is available for RUL prediction under the first passage time (FPT) concept, and 2) the offline estimation and online update of parameters have not been jointly addressed. Faced with these limitations, this paper investigates the degradation modeling and RUL prediction for DCFPs. The considered DCFPs comprise of soft failure processes subject to gradual degradation and random shocks, and hard failure processes induced by random shocks. First, degradation models for both soft and hard failure processes are formulated, and the FPT-based analytical expression of RUL is correspondingly derived. Second, the offline estimation and online update of parameters are jointly addressed. A sequential estimation scheme is developed for offline estimation, then the estimated results are updated using a specifically designed total variation multiple model particle filter. Finally, a numerical example and an experimental study are provided for demonstration. © 2021 Elsevier Ltd
Keyword ：
Forecasting Machinery Monte Carlo methods Parameter estimation
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| GB/T 7714 | Yan, Tao , Lei, Yaguo , Li, Naipeng et al. Degradation modeling and remaining useful life prediction for dependent competing failure processes [J]. | Reliability Engineering and System Safety , 2021 , 212 . |
| MLA | Yan, Tao et al. "Degradation modeling and remaining useful life prediction for dependent competing failure processes" . | Reliability Engineering and System Safety 212 (2021) . |
| APA | Yan, Tao , Lei, Yaguo , Li, Naipeng , Wang, Biao , Wang, Wenting . Degradation modeling and remaining useful life prediction for dependent competing failure processes . | Reliability Engineering and System Safety , 2021 , 212 . |
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Abstract ：
Applications of deep transfer learning to intelligent fault diagnosis of machines commonly assume symmetry among domains: 1) the samples from target machines are balanced across all health states, and 2) the diagnostic knowledge required by target machines is consistent with source machines. In reality, however, such assumptions cannot be justified as machines operate normally in most of the time with only occasional faults. As a result, the collected monitoring data from target machines contain massive healthy samples but a small number of faulty samples, and some health states experienced in source machines may never happen in target machines. Therefore, if sufficient labeled data are available with diverse health states from the source machines, only partial diagnostic knowledge can be transferred to a target machine in presence of domain asymmetry. In order to selectively transfer diagnostic knowledge across asymmetric domains, we propose an adversarial network architecture named deep partial transfer learning network (DPTL-Net). The DPTL-Net uses a domain discriminator to automatically learn domain-asymmetry factors, by which the source machine samples are weighted to block irrelevant knowledge in the maximum mean discrepancy based distribution adaptation. The performance of the DPTL-Net is demonstrated in two case studies, where the diagnostic knowledge is transferred across different working conditions of a planet gearbox and across different yet related bearings. The results show that the DPTL-Net achieves better diagnostic performance than other transfer learning methods due to its transfer capability in presence of domain asymmetry. © 2021 Elsevier Ltd
Keyword ：
Deep learning Epicyclic gears Health Learning systems Network architecture Transfer learning
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| GB/T 7714 | Yang, Bin , Lee, Chi-Guhn , Lei, Yaguo et al. Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines [J]. | Mechanical Systems and Signal Processing , 2021 , 156 . |
| MLA | Yang, Bin et al. "Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines" . | Mechanical Systems and Signal Processing 156 (2021) . |
| APA | Yang, Bin , Lee, Chi-Guhn , Lei, Yaguo , Li, Naipeng , Lu, Na . Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines . | Mechanical Systems and Signal Processing , 2021 , 156 . |
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Abstract ：
As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed. However, these methods seldom considered the appearance of new working conditions during the operation of real machines. Varying working conditions lead to a change of feature distributions and finally result in low diagnosis accuracies. Therefore, we propose a distribution-invariant DBN (DIDBN) to learn distribution-invariant features directly from raw vibration data. DIDBN consists of a locally connected RBM (LCRBM) layer, a fully connected RBM layer, and an RBM layer with a mean discrepancy maximum (MDM-RBM). The LCRBM layer is designed with a locally connected structure. By proposing MDM, the MDM-RBM layer is able to obtain features that have close distributions under varying working conditions. Followed by a softmax classifier, DIDBN is able to recognize faults. The proposed method is applied to two diagnosis cases. Results verify that DIDBN is able to learn distribution-invariant features and achieve higher diagnosis accuracies than recently proposed methods. Moreover, the reason why DIDBN is able to learn distribution-invariant features is explained by visualizing the feature learning process. © 1982-2012 IEEE.
Keyword ：
Deep learning Failure analysis Fault detection Learning systems
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| GB/T 7714 | Xing, Saibo , Lei, Yaguo , Wang, Shuhui et al. Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines under New Working Conditions [J]. | IEEE Transactions on Industrial Electronics , 2021 , 68 (3) : 2617-2625 . |
| MLA | Xing, Saibo et al. "Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines under New Working Conditions" . | IEEE Transactions on Industrial Electronics 68 . 3 (2021) : 2617-2625 . |
| APA | Xing, Saibo , Lei, Yaguo , Wang, Shuhui , Jia, Feng . Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines under New Working Conditions . | IEEE Transactions on Industrial Electronics , 2021 , 68 (3) , 2617-2625 . |
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Abstract ：
It has always been an issue of significance to diagnose compound faults of machines. Existing intelligent diagnosis methods have to be trained by sufficient data of each compound fault. However, both labeled and unlabeled data of mechanical compound faults are usually difficult to collect or even completely inaccessible for training in real scenarios. Therefore, compound faults are usually unseen fault patterns. Unseen fault patterns are those that have no labeled or unlabeled training data. Without training data of compound faults, the current intelligent diagnosis methods usually fail in recognizing compound faults. This paper proposes a zero-shot intelligent diagnosis method for unseen compound faults of machines. The proposed method contains three stages, i.e., the feature learning, pre-judgment and fault recognition. The key to this method is a label description space embedded model for intelligent fault diagnosis (LDS-IFD) in Stage 3. In LDS-IFD, a label description space (LDS) is built to construct the relationship among different fault patterns. LDS is embedded between the feature space (FS) and the health condition label space (HCLS). Then the projection between FS and LDS is constructed by a linear supervised autoencoder (LSAE). By similarity evaluation in LDS or FS, LDS-IFD is able to recognize mechanical compound faults when only the data of single faults are accessible for training. The proposed method is demonstrated on a bearing dataset and a planetary gearbox dataset. Results show that the proposed method is effective in diagnosing unseen compound faults of machines. © 2021 Elsevier Ltd
Keyword ：
Failure analysis Fault detection Learning systems Semantics
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| GB/T 7714 | Xing, Saibo , Lei, Yaguo , Wang, Shuhui et al. A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults [J]. | Mechanical Systems and Signal Processing , 2021 , 162 . |
| MLA | Xing, Saibo et al. "A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults" . | Mechanical Systems and Signal Processing 162 (2021) . |
| APA | Xing, Saibo , Lei, Yaguo , Wang, Shuhui , Lu, Na , Li, Naipeng . A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults . | Mechanical Systems and Signal Processing , 2021 , 162 . |
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Abstract ：
Health condition monitoring of machinery has entered into the big data era, which brings new opportunities to machinery fault diagnosis. However, due to the abnormal operating environment, disturbance from human and fault data acquisition devices, condition-monitoring data generally include lots of data with abnormal or missing values, which reduces the quality of data seriously. Wrong diagnosis results are probably obtained from the analysis of the low-quality data, leading to inappropriate strategy of machinery maintenance. To solve this problem, a condition-monitoring vibration data recovery method is proposed based on tensor decomposition. A four-order tensor including rotational speed, time-domain window, multi-scale using wavelet transform, and time is constructed. Tucker decomposition is used to process this four-order tensor for extracting the information of health condition and missing data are recovered by tensor completion. Simulated data and real vibration data are used to verify the effectiveness of the proposed method, respectively. The result shows that the data recovered by the proposed method are more close to the real data, compared with traditional data recovery methods, which demonstrates its effectiveness for data recovery in data quality assurance. The proposed method is applied to improve the quality of the condition-monitoring data collected from wind power equipment. © 2021 Journal of Mechanical Engineering.
Keyword ：
Condition monitoring Data acquisition Health Machinery Quality assurance Recovery Tensors Time domain analysis Wavelet transforms Wind power
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| GB/T 7714 | Lei, Yaguo , Xu, Xuefang , Cai, Xiao et al. Research on Data Quality Assurance for Health Condition Monitoring of Machinery [J]. | Journal of Mechanical Engineering , 2021 , 57 (4) : 1-9 . |
| MLA | Lei, Yaguo et al. "Research on Data Quality Assurance for Health Condition Monitoring of Machinery" . | Journal of Mechanical Engineering 57 . 4 (2021) : 1-9 . |
| APA | Lei, Yaguo , Xu, Xuefang , Cai, Xiao , Li, Naipeng , Kong, Detong , Zhang, Yongming . Research on Data Quality Assurance for Health Condition Monitoring of Machinery . | Journal of Mechanical Engineering , 2021 , 57 (4) , 1-9 . |
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Abstract ：
Predictive maintenance is one of the most promising ways to reduce the operation and maintenance (O&M) costs of wind turbines (WTs). Remaining useful life (RUL) prediction is the basis for predictive maintenance decision. Self-data-driven methods predict the RUL of a WT driven by its own condition monitoring data without depending on failure event data. Therefore, they are applicable in industrial cases where no sufficient failure event data is available. One challenging issue for RUL prediction of WTs is that they generally suffer from varying rotating speeds. The speed variation has serious impact on the degradation rates as well as the amplitudes of state observations. To deal with this issue, this paper proposes a self-data-driven RUL prediction method for WTs considering continuously varying speeds. In the method, a generalized cumulative degradation model is constructed to describe the degradation process of WTs under continuously varying speeds. A baseline transformation algorithm is developed to transform health state observations under varying speeds into a baseline speed. A continuous trigging algorithm is employed to determine the first degradation time (FDT) for degradation modeling and the first predicting time (FPT) for RUL prediction. The best fitting model is selected adaptively to keep in line with the degradation trend of interest. The effectiveness of the method is demonstrated using a simulation case study and an industrial case study. © 2021 Elsevier Ltd
Keyword ：
Condition monitoring Degradation Forecasting Maintenance Speed Wind turbines
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| GB/T 7714 | Li, Naipeng , Xu, Pengcheng , Lei, Yaguo et al. A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds [J]. | Mechanical Systems and Signal Processing , 2021 , 165 . |
| MLA | Li, Naipeng et al. "A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds" . | Mechanical Systems and Signal Processing 165 (2021) . |
| APA | Li, Naipeng , Xu, Pengcheng , Lei, Yaguo , Cai, Xiao , Kong, Detong . A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds . | Mechanical Systems and Signal Processing , 2021 , 165 . |
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Abstract ：
The reliable and explainable diagnosis of severity level for Parkinson's disease (PD) is significant for the therapy. Nonetheless, there are little data for severe PD patients but abundant data for slight PD patients, and this imbalanced distribution reduces the accuracy of diagnosis. Besides, the intrinsic differences for different severity levels are still unclear due to the individual differences and similarity of gait. To figure out the gait differences toward the development of PD severity level, gait features like time and force features as well as their coefficient of variance and asymmetry index have been extracted and compared. To overcome the imbalance influence during the severity level diagnosis, an ensemble K-nearest neighbor (EnKNN) is proposed. The K-nearest neighbor algorithm is applied to construct the base classifiers with extracted features, then the weight of each base classifier is calculated by the G-mean score and the F-measure. Finally, base classifiers are integrated by weight voting. Results show that the proposed EnKNN can achieve an average accuracy of 95.02% (0.44%) for PD severity level diagnosis overwhelming the imbalanced distribution of data. Additionally, some gait features exhibit distinct change with the increase of PD severity level which helps to a reliable and explainable diagnosis. © 2021 Elsevier Ltd
Keyword ：
Classification (of information) Diagnosis Disease control Gait analysis Motion compensation Nearest neighbor search Neurodegenerative diseases Pattern recognition
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| GB/T 7714 | Zhao, Huan , Wang, Ruixue , Lei, Yaguo et al. Severity level diagnosis of Parkinson's disease by ensemble K-nearest neighbor under imbalanced data [J]. | Expert Systems with Applications , 2021 , 189 . |
| MLA | Zhao, Huan et al. "Severity level diagnosis of Parkinson's disease by ensemble K-nearest neighbor under imbalanced data" . | Expert Systems with Applications 189 (2021) . |
| APA | Zhao, Huan , Wang, Ruixue , Lei, Yaguo , Liao, Wei-Hsin , Cao, Hongmei , Cao, Junyi . Severity level diagnosis of Parkinson's disease by ensemble K-nearest neighbor under imbalanced data . | Expert Systems with Applications , 2021 , 189 . |
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