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A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults EI
期刊论文 | 2022 , 162 | Mechanical Systems and Signal Processing
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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 :

Fault detection Semantics Learning systems Failure analysis

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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 , 2022 , 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 (2022) .
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 , 2022 , 162 .
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Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective EI
期刊论文 | 2022 , 217 | Reliability Engineering and System Safety
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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 :

Systems engineering Image segmentation Maximum principle Stochastic systems Parameter estimation Bayesian networks Mathematical transformations Maximum likelihood estimation Lithium-ion batteries

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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 , 2022 , 217 .
MLA Si, Xiao-Sheng et al. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective" . | Reliability Engineering and System Safety 217 (2022) .
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 , 2022 , 217 .
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A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds EI
期刊论文 | 2022 , 165 | Mechanical Systems and Signal Processing
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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 :

Wind turbines Maintenance Degradation Condition monitoring Speed Forecasting

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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 , 2022 , 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 (2022) .
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 , 2022 , 165 .
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Severity level diagnosis of Parkinson's disease by ensemble K-nearest neighbor under imbalanced data EI
期刊论文 | 2022 , 189 | Expert Systems with Applications
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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 Neurodegenerative diseases Motion compensation Disease control Pattern recognition Nearest neighbor search Gait analysis

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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 , 2022 , 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 (2022) .
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 , 2022 , 189 .
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Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems EI SCIE
期刊论文 | 2021 , 68 (11) , 11482-11491 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
WoS CC Cited Count: 1
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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 :

Sensors State-space methods Inspection prognostic degradation modeling state-space model remaining useful life (RUL) prediction Degradation Sensor systems Predictive models Monitoring Multi-sensor data particle filtering (PF)

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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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Research on Data Quality Assurance for Health Condition Monitoring of Machinery EI
期刊论文 | 2021 , 57 (4) , 1-9 | Journal of Mechanical Engineering
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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 :

Health Machinery Data acquisition Wind power Condition monitoring Wavelet transforms Tensors Time domain analysis Quality assurance Recovery

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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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Remaining useful life prediction based on a multi-sensor data fusion model EI SCIE
期刊论文 | 2021 , 208 | RELIABILITY ENGINEERING & SYSTEM SAFETY
WoS CC Cited Count: 7
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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 State-space model Prognostic degradation modeling Multi-sensor fusion Remaining useful life prediction

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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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Degradation modeling and remaining useful life prediction for dependent competing failure processes EI SCIE
期刊论文 | 2021 , 212 | Reliability Engineering and System Safety
WoS CC Cited Count: 5
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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 :

Parameter estimation Monte Carlo methods Machinery Forecasting

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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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Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines EI SCIE
期刊论文 | 2021 , 156 | Mechanical Systems and Signal Processing
WoS CC Cited Count: 3
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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 :

Transfer learning Deep learning Epicyclic gears Network architecture Learning systems Health

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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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Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines under New Working Conditions EI SCIE
期刊论文 | 2021 , 68 (3) , 2617-2625 | IEEE Transactions on Industrial Electronics
WoS CC Cited Count: 15
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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 Fault detection Learning systems Failure analysis

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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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