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Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion EI SCIE
期刊论文 | 2019 , 180 , 811-821 | Energy Conversion and Management
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Abstract :

Recently, bearing health condition monitoring has attracted considerable attention due to its great significance to prolong the lifespan and improve the system reliability of key industrial equipments. For the purpose of improving the reliability and effectiveness of energy harvesting in the monitoring node of industrial equipments, this paper proposes circular Halbach electromagnetic energy harvesters for extracting the electrical energy from the rotational motion of bearings to supply the monitoring units. The magnetic distribution model of the circular Halbach array is derived to investigate the magnetic field enhancement using different arrangement modes and structural parameters. The effect of gap, magnetic shape and distribution radius on the magnetic field is numerically discussed to obtain the proper configuration of circular Halbach array for performance enhancement. The experimental results of the fabricated prototype demonstrate the effectiveness of the proposed model and optimization design for enhancing the energy harvesting performance. Moreover, voltage response and power output under different connection modes of multi-coil are experimentally discussed for increasing efficiency and reducing the cost and difficulty of interface circuits. Under the rotational speed from 600 rpm to 1000 rpm, the proposed harvester can generate the voltage of 2.79–4.59 V and the maximum average power of 50.8–131.1 mW. © 2018

Keyword :

Experimental verification Halbach array Health monitoring Magnetic distributions Magnetic field enhancements Modeling and optimization Performance enhancements Structural parameter

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GB/T 7714 Zhang, Ying , Cao, Junyi , Zhu, Hongyu et al. Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion [J]. | Energy Conversion and Management , 2019 , 180 : 811-821 .
MLA Zhang, Ying et al. "Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion" . | Energy Conversion and Management 180 (2019) : 811-821 .
APA Zhang, Ying , Cao, Junyi , Zhu, Hongyu , Lei, Yaguo . Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion . | Energy Conversion and Management , 2019 , 180 , 811-821 .
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Applications of stochastic resonance to machinery fault detection: A review and tutorial EI SCIE
期刊论文 | 2019 , 122 , 502-536 | Mechanical Systems and Signal Processing
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Abstract :

Fault detection is a key tool to ensure the safety and reliability of machinery. In machinery fault detection, signal processing methods are extensively applied to extract fault characteristics. Widely used signal processing methods attempt to eliminate the noise imbedded in signals for discovering fault characteristics. Different from widely used signal processing methods, stochastic resonance (SR) is able to utilize the noise imbedded in signals to extract weak fault characteristics from the signals. Therefore, it has been extensively applied to fault characteristic extraction and machinery fault detection. Up to now, massive literature on the applications of SR to machinery fault detection has been published in academic journals, conference proceedings, etc. This paper attempts to survey and summarize the current progress of SR applied in machinery fault detection, providing comprehensive references for researchers concerning with the subject and further helping them identify future trends for research. First, this paper elaborates SR from its original mechanism to fundamental theory. Then, the literature on machinery fault detection using SR is reviewed in terms of critical rotary components prone to faults, such as rolling element bearings, gears and rotors. Moreover, a tutorial on how to use SR for machinery fault detection is provided. What's more, the key issues and prospects of SR in machinery fault detection are pointed out and discussed. It is expected that this review would inspire researchers to explore the potential of SR as well as develop advanced research in this field. © 2018 Elsevier Ltd

Keyword :

Academic journal Advanced researches Fault characteristics Fundamental theory Machinery fault detection Rolling Element Bearing Rotary components Stochastic resonances

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GB/T 7714 Qiao, Zijian , Lei, Yaguo , Li, Naipeng . Applications of stochastic resonance to machinery fault detection: A review and tutorial [J]. | Mechanical Systems and Signal Processing , 2019 , 122 : 502-536 .
MLA Qiao, Zijian et al. "Applications of stochastic resonance to machinery fault detection: A review and tutorial" . | Mechanical Systems and Signal Processing 122 (2019) : 502-536 .
APA Qiao, Zijian , Lei, Yaguo , Li, Naipeng . Applications of stochastic resonance to machinery fault detection: A review and tutorial . | Mechanical Systems and Signal Processing , 2019 , 122 , 502-536 .
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An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings EI SCIE
期刊论文 | 2019 , 122 , 692-706 | Mechanical Systems and Signal Processing
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Intelligent fault diagnosis of rolling element bearings has made some achievements based on the availability of massive labeled data. However, the available data from bearings used in real-case machines (BRMs) are insufficient to train a reliable intelligent diagnosis model. Fortunately, we can easily simulate various faults of bearings in a laboratory, and the data from bearings used in laboratory machines (BLMs) contain diagnosis knowledge related to the data from BRMs. Therefore, inspired by the idea of transfer learning, we propose a feature-based transfer neural network (FTNN) to identify the health states of BRMs with the help of the diagnosis knowledge from BLMs. In the proposed method, a convolutional neural network (CNN) is employed to extract transferable features of raw vibration data from BLMs and BRMs. Then, the regularization terms of multi-layer domain adaptation and pseudo label learning are developed to impose constraints on the parameters of CNN so as to reduce the distribution discrepancy and the among-class distance of the learned transferable features. The proposed method is verified by two fault diagnosis cases of bearings, in which the health states of locomotive bearings in real cases are identified by using the data respectively collected from motor bearings and gearbox bearings in laboratories. The results show that the proposed method is able to effectively learn transferable features to bridge the discrepancy between the data from BLMs and BRMs. Consequently, it presents higher diagnosis accuracy for BRMs than existing methods. © 2018 Elsevier Ltd

Keyword :

Convolutional neural network Domain adaptation Intelligent fault diagnosis Rolling Element Bearing Transfer learning

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GB/T 7714 Yang, Bin , Lei, Yaguo , Jia, Feng et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings [J]. | Mechanical Systems and Signal Processing , 2019 , 122 : 692-706 .
MLA Yang, Bin et al. "An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings" . | Mechanical Systems and Signal Processing 122 (2019) : 692-706 .
APA Yang, Bin , Lei, Yaguo , Jia, Feng , Xing, Saibo . An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings . | Mechanical Systems and Signal Processing , 2019 , 122 , 692-706 .
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Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era EI CSCD Scopus PKU
期刊论文 | 2018 , 54 (5) , 94-104 | Jixie Gongcheng Xuebao/Journal of Mechanical Engineering
SCOPUS Cited Count: 2
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Abstract :

Faults are a potential killer of large-scale mechanical equipment, such as wind power equipment, aircraft engines and high-end CNC machine. And fault diagnosis plays an irreplaceable role in ensuring the health operation of such equipment. Since the amount of the equipment diagnosed is great and the number of the sensors for the equipment is large, massive data are acquired by the high sampling frequency after the long-time operation of equipment. Such massive data promote fault diagnosis to enter the era of big data. And machinery intelligent fault diagnosis is a promising tool to deal with mechanical big data. In the big data era, new opportunities have been brought to intelligent fault diagnosis. For instance, data-centric academic thinking will become mainstream, it makes fault diagnosis in the system level possible, and a comprehensive analysis of faults becomes a trend. Meanwhile, new challenges have also been brought: the data are big but fragmentary, the fault feature extraction relies on much prior knowledge and diagnostics expertise, and the generalization ability of the shallow diagnosis model is weak. The characteristics of big data in intelligent fault diagnosis are discussed, and the inland and overseas research advances are reviewed from the three steps of intelligent fault diagnosis. The existing key problems of the current research in the era of big data are pointed out, and the approaches and research directions to these problems are discussed in the end. © 2018 Journal of Mechanical Engineering.

Keyword :

Comprehensive analysis Fault feature extractions Generalization ability High sampling frequencies Intelligent fault diagnosis Long-time operation Mechanical equipment Shallow diagnosis

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GB/T 7714 Lei, Yaguo , Jia, Feng , Kong, Detong et al. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era [J]. | Jixie Gongcheng Xuebao/Journal of Mechanical Engineering , 2018 , 54 (5) : 94-104 .
MLA Lei, Yaguo et al. "Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era" . | Jixie Gongcheng Xuebao/Journal of Mechanical Engineering 54 . 5 (2018) : 94-104 .
APA Lei, Yaguo , Jia, Feng , Kong, Detong , Lin, Jing , Xing, Saibo . Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era . | Jixie Gongcheng Xuebao/Journal of Mechanical Engineering , 2018 , 54 (5) , 94-104 .
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基于SCADA数据的风机叶片结冰检测方法
期刊论文 | 2018 , (1) , 58-62 | 发电与空调
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Abstract :

针对工作在寒冷地区的风机易出现的叶片结冰现象,提出一种基于SCADA数据的风机叶片结冰检测方法.根据叶片结冰会增大发电机的功率损耗,选择风速与网侧有功功率2个变量,利用主成分分析技术构造对叶片结冰敏感的风速与网侧有功功率在非主成分方向投影特征,通过选择最优阈值使逻辑回归分类器适用于不平衡分类,可以实现风机叶片结冰检测自动化与智能化.通过中国工业大数据创新竞赛数据验证了该方法的有效性.

Keyword :

风机叶片结冰检测 SCADA数据 非主成分方向投影特征 最优阈值选择 不平衡分类

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GB/T 7714 李宁波 , 闫涛 , 李乃鹏 et al. 基于SCADA数据的风机叶片结冰检测方法 [J]. | 发电与空调 , 2018 , (1) : 58-62 .
MLA 李宁波 et al. "基于SCADA数据的风机叶片结冰检测方法" . | 发电与空调 1 (2018) : 58-62 .
APA 李宁波 , 闫涛 , 李乃鹏 , 孔德同 , 刘庆超 , 雷亚国 . 基于SCADA数据的风机叶片结冰检测方法 . | 发电与空调 , 2018 , (1) , 58-62 .
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Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox EI SCIE Scopus
期刊论文 | 2018 , 98 , 16-31 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
WoS CC Cited Count: 10 SCOPUS Cited Count: 15
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Abstract :

In modern rotating machinery, rotary encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the encoder signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the encoder signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox. (C) 2017 Elsevier Ltd. All rights reserved.

Keyword :

Fault diagnosis Instantaneous angular speed Local polynomial differentiator Planetary gearbox Health monitoring

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GB/T 7714 Zhao, Ming , Jia, Xiaodong , Lin, Jing et al. Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2018 , 98 : 16-31 .
MLA Zhao, Ming et al. "Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 98 (2018) : 16-31 .
APA Zhao, Ming , Jia, Xiaodong , Lin, Jing , Lei, Yaguo , Lee, Jay . Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2018 , 98 , 16-31 .
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A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines EI SCIE Scopus
期刊论文 | 2018 , 272 , 619-628 | NEUROCOMPUTING
WoS CC Cited Count: 34 SCOPUS Cited Count: 38
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Abstract :

In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these methods less automatic. Among deep learning techniques, autoencoders may be a potential tool for automatic feature extraction of mechanical signals. However, traditional autoencoders have two following shortcomings. (1) They may learn similar features in mechanical feature extraction. (2) The learned features have shift variant properties, which leads to the misclassification of mechanical health conditions. To overcome the aforementioned shortcomings, a local connection network (LCN) constructed by normalized sparse autoencoder (NSAE), namely NSAE-LCN, is proposed for intelligent fault diagnosis. We construct LCN by input layer, local layer, feature layer and output layer. When raw vibration signals are fed to the input layer, LCN first uses NSAE to locally learn various meaningful features from input signals in the local layer, then obtains shift-invariant features in the feature layer and finally recognizes mechanical health conditions in the output layer. Thus, NSAE-LCN incorporates feature extraction and fault recognition into a general-purpose learning procedure. A gearbox dataset and a bearing dataset are used to validate the performance of the proposed NSAE-LCN. The results indicate that the learned features of NSAE are meaningful and dissimilar, and LCN helps to produce shift-invariant features and recognizes mechanical health conditions effectively. Through comparing with commonly used diagnosis methods, the superiority of the proposed NSAE-LCN is verified. (C) 2017 Elsevier B.V. All rights reserved.

Keyword :

Deep learning Intelligent fault diagnosis Local connection network Normalized sparse autoencoder

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GB/T 7714 Jia, Feng , Lei, Yaguo , Guo, Liang et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines [J]. | NEUROCOMPUTING , 2018 , 272 : 619-628 .
MLA Jia, Feng et al. "A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines" . | NEUROCOMPUTING 272 (2018) : 619-628 .
APA Jia, Feng , Lei, Yaguo , Guo, Liang , Lin, Jing , Xing, Saibo . A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines . | NEUROCOMPUTING , 2018 , 272 , 619-628 .
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A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears EI SCIE Scopus
期刊论文 | 2018 , 106 , 355-366 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
WoS CC Cited Count: 7 SCOPUS Cited Count: 7
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Abstract :

Tooth damage often causes a reduction in gear mesh stiffness. Thus time-varying mesh stiffness (TVMS) can be treated as an indication of gear health conditions. This study is devoted to investigating the mesh stiffness variations of a pair of external spur gears with tooth pitting, and proposes a new model for describing tooth pitting based on probability distribution. In the model, considering the appearance and development process of tooth pitting, we model the pitting on the surface of spur gear teeth as a series of pits with a uniform distribution in the direction of tooth width and a normal distribution in the direction of tooth height, respectively. In addition, four pitting degrees, from no pitting to severe pitting, are modeled. Finally, influences of tooth pitting on TVMS are analyzed in details and the proposed model is validated by comparing with a finite element model. The comparison results show that the proposed model is effective for the TVMS evaluations of pitting gears. (C) 2018 Elsevier Ltd. All rights reserved.

Keyword :

Pitting Probability distribution Time-varying mesh stiffness Gear tooth damage

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GB/T 7714 Lei, Yaguo , Liu, Zongyao , Wang, Delong et al. A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2018 , 106 : 355-366 .
MLA Lei, Yaguo et al. "A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 106 (2018) : 355-366 .
APA Lei, Yaguo , Liu, Zongyao , Wang, Delong , Yang, Xiao , Liu, Huan , Lin, Jing . A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2018 , 106 , 355-366 .
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Machinery health indicator construction based on convolutional neural networks considering trend burr EI SCIE Scopus
期刊论文 | 2018 , 292 , 142-150 | NEUROCOMPUTING
WoS CC Cited Count: 6 SCOPUS Cited Count: 8
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Abstract :

In the study of data-driven prognostic methods of machinery, much attention has been paid to constructing health indicators (HIs). Most of the existing HIs, however, are manually constructed for a specific degradation process and need the prior knowledge of experts. Additionally, for the existing HIs, there are usually some outlier regions deviating to an expected degradation trend and reducing the performance of HIs. We refer to this phenomenon as trend burr. To deal with these problems, this paper proposes a convolutional neural network based HI construction method considering trend burr. The proposed method first learns features through convolution and pooling operations, and then these learned features are constructed into a HI through a nonlinear mapping operation. Furthermore, an outlier region correction technique is applied to detect and remove outlier regions existing in the HIs. Unlike traditional methods in which HIs are manually constructed, the proposed method aims to automatically construct HIs. Moreover, the outlier region correction technique enables the constructed HIs to be more effective. The effectiveness of the proposed method is verified using a bearing dataset. Through comparing with commonly used HI construction methods, it is demonstrated that the proposed method achieves better results in terms of trendability, monotonicity and scale similarity. (c) 2018 Elsevier B.V. All rights reserved.

Keyword :

Deep learning Machinery health indicator Convolutional neural network Outlier region correction Trend burr

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GB/T 7714 Guo, Liang , Lei, Yaguo , Li, Naipeng et al. Machinery health indicator construction based on convolutional neural networks considering trend burr [J]. | NEUROCOMPUTING , 2018 , 292 : 142-150 .
MLA Guo, Liang et al. "Machinery health indicator construction based on convolutional neural networks considering trend burr" . | NEUROCOMPUTING 292 (2018) : 142-150 .
APA Guo, Liang , Lei, Yaguo , Li, Naipeng , Yan, Tao , Li, Ningbo . Machinery health indicator construction based on convolutional neural networks considering trend burr . | NEUROCOMPUTING , 2018 , 292 , 142-150 .
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A LOF-based method for abnormal segment detection in machinery condition monitoring CPCI-S
会议论文 | 2018 , 125-128 | Prognostics and System Health Management Conference (PHM-Chongqing)
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Abstract :

Machinery condition monitoring has entered the era of big data and some research has been done based on big data. Abnormal segments, such as missing segments and drift segments, are inevitable in big data acquired from harsh industrial environment due to temporary sensor failures, network segment transmission delays, or accidental loss of some collected data and so on. Being independent of the machinery condition, the abnormal segments not only reduce the quality of the data for condition monitoring and big data analysis, but also bring a heavy computation load. However, there are few reports to address abnormal segment detection for further data cleaning in the field of machinery condition monitoring. Therefore, an abnormal segment detection method is proposed to improve the quality of big data. First, a sliding window is used to separate the data into different segments. Then, 14 kinds of time-domain features are extracted from each segment and principle component analysis (PCA) is employed to extract the principle components from these features. In addition, local outlier factor (LOF) is calculated based on the principle components to evaluate the degree of being an outlier for each segment. Finally, the data, including a drift segment from a real wind turbine, are used to verify the effectiveness of the proposed method.

Keyword :

abnormal segment detection machinery condition monitoring local outlier factor big data outlier

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GB/T 7714 Xu, Xuefang , Lei, Yaguo , Zhou, Xin . A LOF-based method for abnormal segment detection in machinery condition monitoring [C] . 2018 : 125-128 .
MLA Xu, Xuefang et al. "A LOF-based method for abnormal segment detection in machinery condition monitoring" . (2018) : 125-128 .
APA Xu, Xuefang , Lei, Yaguo , Zhou, Xin . A LOF-based method for abnormal segment detection in machinery condition monitoring . (2018) : 125-128 .
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