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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 ：
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 ：
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 ：
With the rapid development of Industrial Internet of Things, more and more sensors have been used for condition monitoring and prognostics of industrial systems. Big data collected from sensor networks bring abundant information resources as well as technical challenges for remaining useful life (RUL) prediction. The major technical challenges include how to select informative sensors and fuse multi-sensor data to improve the prediction performance. To deal with the challenges, this paper proposes a RUL prediction method based on a multi-sensor data fusion model. In this method, the inherent degradation process of the system state is expressed using a state transition function following a Wiener process. Multi-sensor signals are explicated as various proxies of the inherent system degradation process using a multivariate measurement function. The system state is estimated by fusing multi-sensor signals using particle filtering. Informative sensors are selected by a prioritized sensor group selection algorithm. This algorithm first prioritizes sensors according to their individual performances in RUL prediction, and then selects an optimal sensor group based on their combined performances. The effectiveness of the proposed method is demonstrated using a simulation study and aircraft engine degradation data from NASA repository.
Keyword ：
Big data Multi-sensor fusion Prognostic degradation modeling Remaining useful life prediction State-space model
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| GB/T 7714 | Li, Naipeng , Gebraeel, Nagi , Lei, Yaguo et al. Remaining useful life prediction based on a multi-sensor data fusion model [J]. | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2021 , 208 . |
| MLA | Li, Naipeng et al. "Remaining useful life prediction based on a multi-sensor data fusion model" . | RELIABILITY ENGINEERING & SYSTEM SAFETY 208 (2021) . |
| APA | Li, Naipeng , Gebraeel, Nagi , Lei, Yaguo , Fang, Xiaolei , Cai, Xiao , Yan, Tao . Remaining useful life prediction based on a multi-sensor data fusion model . | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2021 , 208 . |
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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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Abstract ：
Transforming nonlinear degradation paths into nearly linear ones has been widely used for nonlinear degradation modeling and prognostics. However, types of the current transformation functions are difficult to determine. This paper addresses issues in nonlinear stochastic degradation modeling and prognostics from a Box-Cox transformation (BCT) perspective. Specifically, the BCT is first used to transform the nonlinear degradation data into nearly linear data, and then the Wiener process with random drift is utilized to model the evolving process of the transformed data. To determine the model parameters, a two-stage estimation procedure is developed including offline stage and online stage. In the offline stage, the parameters are determined via maximum likelihood estimation method based on the historical degradation data and such estimated values are used to initialize the online stage. During the online stage, the Bayesian method is adopted to update the model parameters using the data of the degrading system in service, in which the hyperparameters are updated by the expectation maximization algorithm. A closed-form solution to remaining useful life with updated model parameters is further derived for prognostics. Finally, case studies for lithium-ion batteries and liquid coupling devices are provided to demonstrate the proposed approach. © 2021
Keyword ：
Bayesian networks Image segmentation Lithium-ion batteries Mathematical transformations Maximum likelihood estimation Maximum principle Parameter estimation Stochastic systems Systems engineering
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| GB/T 7714 | Si, Xiao-Sheng , Li, Tianmei , Zhang, Jianxun et al. Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective [J]. | Reliability Engineering and System Safety , 2021 , 217 . |
| MLA | Si, Xiao-Sheng et al. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective" . | Reliability Engineering and System Safety 217 (2021) . |
| APA | Si, Xiao-Sheng , Li, Tianmei , Zhang, Jianxun , Lei, Yaguo . Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective . | Reliability Engineering and System Safety , 2021 , 217 . |
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Abstract ：
To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accuracy of deep prognostics networks and limiting their generalization. To overcome the aforementioned weaknesses, a new deep prognostics framework named multiscale convolutional attention network (MSCAN) is proposed in this article for predicting the remaining useful life (RUL) of machinery. In the proposed MSCAN, self-attention modules are first constructed to effectively fuse the input multisensor data. Then, a multiscale learning strategy is developed to automatically learn representations from different temporal scales. Finally, the learned high-level representations are fed into dynamic dense layers to perform regression analysis and RUL estimation. The proposed MSCAN is evaluated using multisensor monitoring data from life testing of milling cutters, and also compared with some state-of-the-art prognostics approaches. Experimental results demonstrate the effectiveness and superiority of the proposed MSCAN in fusing multisensor information and improving RUL prediction accuracy.
Keyword ：
Convolution Convolutional neural network (CNN) deep learning Degradation Estimation Feature extraction Machinery Monitoring multiscale learning remaining useful life (RUL) prediction self-attention mechanism Sensors
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| GB/T 7714 | Wang, Biao , Lei, Yaguo , Li, Naipeng et al. Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (8) : 7496-7504 . |
| MLA | Wang, Biao et al. "Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68 . 8 (2021) : 7496-7504 . |
| APA | Wang, Biao , Lei, Yaguo , Li, Naipeng , Wang, Wenting . Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (8) , 7496-7504 . |
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Abstract ：
Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted to fuse multi-sensor signals to predict remaining useful life (RUL). Majority of them, however, ignored the partially available state observations which can be viewed as ground truth measurements of physical degradation. To deal with this problem, this article proposes a multi-sensor data-driven RUL prediction method for semi-observable systems, which leverages degradation information from online multi-sensor signals as well as offline state observations. This method is developed based on a generalizable state-space model combined with particle filtering framework. In the framework, a state transition function is used to describe the degradation process of system states. A multidimensional measurement function is constructed to describe the mapping between states and multi-sensor signals. To enhance the performance of prediction, an algorithm named prioritized sensor group selection is also proposed to select the optimal sensor group for RUL prediction. The effectiveness of the proposed method is demonstrated using an experiment of cutting tool wear.
Keyword ：
Degradation Inspection Monitoring Multi-sensor data particle filtering (PF) Predictive models prognostic degradation modeling remaining useful life (RUL) prediction Sensors Sensor systems State-space methods state-space model
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| GB/T 7714 | Li, Naipeng , Lei, Yaguo , Gebraeel, Nagi et al. Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (11) : 11482-11491 . |
| MLA | Li, Naipeng et al. "Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68 . 11 (2021) : 11482-11491 . |
| APA | Li, Naipeng , Lei, Yaguo , Gebraeel, Nagi , Wang, Zhijian , Cai, Xiao , Xu, Pengcheng et al. Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (11) , 11482-11491 . |
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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 ：
Parkinson's disease (PD) is a progressive neurological disorder that primarily leads to a series of motor impairments. Therefore, human gait patterns and information obtained from various sensors are employed to extract distinctive features for recognizing the difference between healthy controls and PD patients. However, improper analysis of these gait symptoms may mislead the diagnosis of PD due to gradually progressive characteristics of gait disorders. Moreover, individual differences of measuring signals are often preferable to the gait intrinsic changes induced by PD. To deal with those issues, the mean, coefficient variance (CV), and asymmetry index (AI) of temporal, VGRF/BW based, and ED-based features are extracted and compared by the violin plot and Mann-Whitney U-Test to find the distinctive features and discernible changes of the PD gait. Moreover, ensemble decision trees is proposed for accurate PD diagnosis. The ensemble decision trees with features from time, VGRF/BW, and ED are tested and evaluated by the prediction accuracy. Results show that based on the mean, CV, and AI of VGRF/BW at both posterior, inside and outside heel, inside and outside arch, inside and outside sole, toe, and the total force of left and right, the proposed ensemble tree method achieves a mean accuracy of 99.52% with a standard deviation of 0.10%. The distinctive features and accurate diagnosis will be helpful for the home-based and continuous monitoring to improve treatment and therapy of PD patients.
Keyword ：
Distinctive features Gait PD diagnosis Vertical ground reaction force
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| GB/T 7714 | Zhao, Huan , Cao, Junyi , Wang, Ruixue et al. Accurate identification of Parkinson's disease by distinctive features and ensemble decision trees [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2021 , 69 . |
| MLA | Zhao, Huan et al. "Accurate identification of Parkinson's disease by distinctive features and ensemble decision trees" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 69 (2021) . |
| APA | Zhao, Huan , Cao, Junyi , Wang, Ruixue , Lei, Yaguo , Liao, Wei-Hsin , Cao, Hongmei . Accurate identification of Parkinson's disease by distinctive features and ensemble decision trees . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2021 , 69 . |
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