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学者姓名：陈雪峰
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| GB/T 7714 | Li, Weihua , Chen, Xuefeng . IMS Technical Committee TC-3: Condition Monitoring and Fault Diagnosis Instrument [J]. | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE , 2022 , 25 (8) : 5-9 . |
| MLA | Li, Weihua 等. "IMS Technical Committee TC-3: Condition Monitoring and Fault Diagnosis Instrument" . | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 25 . 8 (2022) : 5-9 . |
| APA | Li, Weihua , Chen, Xuefeng . IMS Technical Committee TC-3: Condition Monitoring and Fault Diagnosis Instrument . | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE , 2022 , 25 (8) , 5-9 . |
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Abstract ：
Investigations on the acoustic modes generated by the ducted fan can provide indispensable guidance for active control of the aero-engine noise. To achieve this, the circumferential pressure of the duct needs to be measured. However, the direct way, mounting a full sensor array (FSA) on the duct wall, requires numerous microphones and leads to a highly complex measuring system. With the introduction of compressive sampling (CS), the azimuthal mode analysis (AMA) can be conducted with much fewer microphones than FSA. In this article, an improved CS-based AMA (ICSAMA) approach is developed to promote the measuring accuracy while the measuring system can be further simplified. To be specific, the k-sparsity-constrained generalized minimax-concave (GMC) regularization is adopted to estimate the Tyler-Sofrin modes; meanwhile the Tikhonov regularization is employed to estimate the rest nondominant modes. The effectiveness of the proposed approach is verified on a 2.5-stage aero-engine fan rig, where two cases with different rotational speeds are conducted. Advantages of the proposed approach over the classical CS-based one are demonstrated by the experimental results with improved mode accuracy and reduced numbers of microphones. Furthermore, the robustness of the ICSAMA approach is also indicated, which is apparently desired in practical engineering.
Keyword ：
Azimuthal mode analysis (AMA) compressive sampling (CS) generalized minimax-concave (GMC) regularization k-sparsity strategy Tikhonov regularization
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| GB/T 7714 | Li, Zepeng , Qiao, Baijie , Wen, Bi et al. Acoustic Mode Measuring Approach Developed on Generalized Minimax-Concave Regularization and Tikhonov Regularization [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Li, Zepeng et al. "Acoustic Mode Measuring Approach Developed on Generalized Minimax-Concave Regularization and Tikhonov Regularization" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Li, Zepeng , Qiao, Baijie , Wen, Bi , Wang, Yanan , Chen, Xuefeng . Acoustic Mode Measuring Approach Developed on Generalized Minimax-Concave Regularization and Tikhonov Regularization . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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Abstract ：
With the great advantages of hovering and vertical take-off and landing, helicopters play irreplaceable roles in the field of civil aviation, including transport, search and rescue, firefighting, medical transport, tourism, etc. Collected from public information, by the end of 2019, the total number of the global helicopters had exceeded 52 000, in which the number of helicopters in China was only about 2000, nearly 4% of the total. With the rapid development of the national economy and the opening of low-altitude airspace, there is an increasing demand for helicopters in China.
Keyword ：
China Demand forecasting Helicopters Market opportunities Market research Monitoring Performance evaluation
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| GB/T 7714 | Wang, Shibin , Tong, Chaowei , Tao, Zhiyu et al. Helicopter Health and Usage Monitoring System in China [J]. | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE , 2022 , 25 (2) : 45-52 . |
| MLA | Wang, Shibin et al. "Helicopter Health and Usage Monitoring System in China" . | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 25 . 2 (2022) : 45-52 . |
| APA | Wang, Shibin , Tong, Chaowei , Tao, Zhiyu , Yan, Ruqiang , Chen, Xuefeng . Helicopter Health and Usage Monitoring System in China . | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE , 2022 , 25 (2) , 45-52 . |
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Abstract ：
The planetary gearbox, widely used in many machinery fields, suffers from harmful vibration excited by bearings fault, which always causes machine breakdowns. Thus, fault diagnosis is the necessary approach to keeping machines safe, which often takes fault features, which are extracted from signals, as critical information. However, limited to various interferences caused by gear meshing and background noise, fault characteristic information is always weak and difficult to identify, bringing an increasing emphasis on feature enhancement. In this article, based on the feature enhancement problem under strong interferences, a sparse regular diagnosis algorithm is studied. First, a new fault sensitivity indicator is constructed to distinguish different periodic impulse signals and enhance the energy of the fault impulse. Combined with tunable Q-factor wavelet transform (TQWT) multiscale representation, the guided enhanced vector is introduced to guide the directional gathering of the fault frequency band. Then, a weighted regular term is constructed to establish the guided enhanced regular sparse (GERS) model. Iterative soft threshold algorithm is employed to solve the model. Compared with the state-of-the-art methods, through numerical simulation and fault experiment, the effectiveness and superiority of the proposed algorithm are verified successfully.
Keyword ：
Correlated envelope spectrum kurtosis (CESK) fault diagnosis algorithm feature enhancement planetary gear-box weighted sparse regularization
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| GB/T 7714 | Zhang, Xingwu , Ma, Rui , Li, Ming et al. Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Zhang, Xingwu et al. "Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Zhang, Xingwu , Ma, Rui , Li, Ming , Li, Xiaolong , Yang, Zhibo , Yan, Ruqiang et al. Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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Abstract ：
Mechanical equipment such as wind turbines often operates under time-varying working conditions (TVWC). The vibration signals collected from their key rotating components, such as bearings and gears, are often affected by TVWC. In this situation, eliminating the influence of working conditions is the key to effectively implementing end-to-end intelligent diagnosis of rotating machinery (RM). This paper first introduces the transformer encoder architecture to construct a diagnostic model. Then an improved self-attentive module based on a depth-separable convolution operation is built to encode better condition-independent depth diagnostic features and reduce the number of model parameters. Thus, a convolution enabled Transformer (Con-eT) is constructed as a deep encoder for the diagnostic model. Subsequently, a random contrastive regularization (RCR) method inspired by recently proposed contrastive learning is proposed to incentivize the model to learn working-condition-independent features and to improve the model's working condition generalization performance. A publicly available dataset of bearings at time-varying speeds is adopted to verify the effectiveness of the proposed method. Meanwhile, a specific experiment with a wind turbine gearbox designed explicitly for time-varying speed conditions further demonstrates the advantage of the proposed method over the comparative method in terms of the model's working condition generalization.
Keyword ：
Fault diagnosis Random contrastive regularization Rotating machinery Time-varying working condition Transformer network Working conditions generalization
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| GB/T 7714 | Zhou, Haoxuan , Huang, Xin , Wen, Guangrui et al. Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 173 . |
| MLA | Zhou, Haoxuan et al. "Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 173 (2022) . |
| APA | Zhou, Haoxuan , Huang, Xin , Wen, Guangrui , Dong, Shuzhi , Lei, Zihao , Zhang, Pin et al. Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 173 . |
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Abstract ：
Terahertz (THz) imaging has been widely used in industrial nondestructive testing (NDT) of nonpolar materials due to its unique property. Minor defect detection via THz NDT at high accuracy and fast speed is essential for industrial online detection systems. However, traditional defect detection algorithms cannot meet the demand of real-time high-precision detection of minor defects. Therefore, based on the you only look once x (YOLOX) algorithm and multiscale attention (MSA) mechanism, the modified YOLOX network called YOLOX-MSA is proposed as a one-stage minor defect detection framework to improve the detection accuracy while supporting the real-time operation. The proposed YOLOX-MSA network improves the mean average precision (mAP) by at least 11.65% on the printed circuit board (PCB) dataset with THz characteristics when the intersection over union (IoU) is 0.75. In addition, the proposed algorithm can reach the detection speed as 24-25 frames per second (FPS). Overall, our proposed method can be beneficial to generalize the THz NDT in the frequency domain on the minor defects of nonpolar material, which will fulfill the impending requirements of real-time defect detection for industrial applications.
Keyword ：
Detectors Feature extraction Head Imaging Industrial nondestructive testing (NDT) Merging minor defect detection printed circuit boards (PCBs) Real-time systems terahertz (THz) characteristics Transformers you only look once x-multiscale attention (YOLOX-MSA)
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| GB/T 7714 | Wang, Xingyu , Zhang, Zhen , Xu, Yafei et al. Real-Time Terahertz Characterization of Minor Defects by the YOLOX-MSA Network [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Wang, Xingyu et al. "Real-Time Terahertz Characterization of Minor Defects by the YOLOX-MSA Network" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Wang, Xingyu , Zhang, Zhen , Xu, Yafei , Zhang, Liuyang , Yan, Ruqiang , Chen, Xuefeng . Real-Time Terahertz Characterization of Minor Defects by the YOLOX-MSA Network . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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Abstract ：
Chatter is an unfavorable phenomenon that commonly occurs in the machining process, which results in various problems such as poorly finished surfaces, short tool life, and low machining efficiency. Thus, based on the intelligent manufacturing paradigm, an active chatter control strategy is proposed in this work. Different from the commonly used control methods, which strive to reduce the whole vibration (i.e., stable and chatter vibrations) during the machining process, the proposed strategy focuses on suppressing the chatter component by feeding back the displacement difference of the spindle-tool system at the current time and one tooth passing period before. On the basis of the proposed chatter control concept, a piezoelectric actuator-based active chatter control intelligent spindle-tool system as well as a proportional-differential (PD) controller and a fuzzy controller are designed to perform numerical simulations and milling experiments with different cutting parameters. The results prove that the developed strategies not only successfully control chatter and increase the maximum material removal rate (MRR) but also significantly decrease the required voltage of the actuator, which is conducive to saving control energy.
Keyword ：
Active control Displacement difference feedback Fuzzy control Milling chatter PD control
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| GB/T 7714 | Li, Denghui , Cao, Hongrui , Chen, Xuefeng . Displacement difference feedback control of chatter in milling processes [J]. | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2022 , 120 (9-10) : 6053-6066 . |
| MLA | Li, Denghui et al. "Displacement difference feedback control of chatter in milling processes" . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 120 . 9-10 (2022) : 6053-6066 . |
| APA | Li, Denghui , Cao, Hongrui , Chen, Xuefeng . Displacement difference feedback control of chatter in milling processes . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2022 , 120 (9-10) , 6053-6066 . |
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Abstract ：
Existing data-driven fault diagnosis methods assume that the label sets of the training data and test data are consistent, which is usually not applicable for real applications since the fault modes that occur in the test phase are unpredictable. To address this problem, open set fault diagnosis (OSFD), where the test label set consists of a portion of the training label set and some unknown classes, is studied in this article. Considering the changeable operating conditions of machinery, OSFD tasks are further divided into shared-domain open set fault diagnosis (SOSFD) and cross-domain open set fault diagnosis (COSFD) in this article. For SOSFD, 1-D convolutional neural networks are trained for learning discriminative features and recognizing fault modes. For COSFD, due to the distribution discrepancy between the source and target domains, the deep model needs to learn domain-invariant features of shared classes and separate features of outlier classes. Thus, by utilizing the output of an additional domain classifier, a model named bilateral weighted adversarial networks is proposed to assign large weights to shared classes and small weights to outlier classes during the feature alignment. In the test phase, samples are classified according to the outputs of the deep model and unknown-class samples are rejected by the extreme value theory model. Experimental results on two bearing datasets demonstrate the effectiveness and superiority of the proposed method. © 2005-2012 IEEE.
Keyword ：
Convolutional neural networks Deep learning Failure analysis Fault detection
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| GB/T 7714 | Yu, Xiaolei , Zhao, Zhibin , Zhang, Xingwu et al. Deep-learning-based open set fault diagnosis by extreme value theory [J]. | IEEE Transactions on Industrial Informatics , 2022 , 18 (1) : 185-196 . |
| MLA | Yu, Xiaolei et al. "Deep-learning-based open set fault diagnosis by extreme value theory" . | IEEE Transactions on Industrial Informatics 18 . 1 (2022) : 185-196 . |
| APA | Yu, Xiaolei , Zhao, Zhibin , Zhang, Xingwu , Zhang, Qiyang , Liu, Yilong , Sun, Chuang et al. Deep-learning-based open set fault diagnosis by extreme value theory . | IEEE Transactions on Industrial Informatics , 2022 , 18 (1) , 185-196 . |
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Abstract ：
The condition monitoring (CM) of rotating machinery (RM) is an essential operation for improving the reliability of mechanical systems. For this purpose, an efficient CM method that possesses simple and intuitive attributes is required for industrial applications. For condition monitoring that connects fault detection, degradation assessment, and prognosis applications, health indicators (HIs) have been developed in the past few decades. The construction of a HI is the decisive procedure for extracting informative fault information from the monitoring signal. From the initial statistical parameter-based construction methods to the introduction of data-oriented intelligent methods such as deep learning in recent years, HIs construction methods have ranged from fault mechanism-based approaches to a data-based approach, which involve two different technologies regardless of superiority or inferiority. This paper provides a systematic review of the HIs construction methods for rotating machinery proposed in the literature. It emphasizes the classical technical approaches and recent interesting research trends and analyzes the benefits and potential of efficient HIs for condition monitoring. The current challenges and future research opportunities are also presented in this paper. The Engineers and researchers interested in this research can be informed of current research ideas and directions in the field by reading this paper, as well as inspiring potentially excellent research work in the future.
Keyword ：
Condition monitoring Degradation assessment Fault detection Health indicator Rotating machinery
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| GB/T 7714 | Zhou, Haoxuan , Huang, Xin , Wen, Guangrui et al. Construction of health indicators for condition monitoring of rotating machinery: A review of the research [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 203 . |
| MLA | Zhou, Haoxuan et al. "Construction of health indicators for condition monitoring of rotating machinery: A review of the research" . | EXPERT SYSTEMS WITH APPLICATIONS 203 (2022) . |
| APA | Zhou, Haoxuan , Huang, Xin , Wen, Guangrui , Lei, Zihao , Dong, Shuzhi , Zhang, Ping et al. Construction of health indicators for condition monitoring of rotating machinery: A review of the research . | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 203 . |
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Abstract ：
Deep learning theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC). Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multisource information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problem, a novel multisource dense adaptation adversarial network is proposed, which leverages multisensor vibration information and classification label information. Specifically, the frequency spectrum of multisensor data is first employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state-of-the-art performances, which has shown great promise as an intelligent fault diagnosis method.
Keyword ：
Convolution Data models Dense convolutional network domain adaptation (DA) Employee welfare Fault diagnosis Feature extraction intelligent fault diagnosis Kernel multisource fusion Training transfer learning (TL)
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| GB/T 7714 | Huang, Ziling , Lei, Zihao , Wen, Guangrui et al. A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2022 , 69 (6) : 6298-6307 . |
| MLA | Huang, Ziling et al. "A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 69 . 6 (2022) : 6298-6307 . |
| APA | Huang, Ziling , Lei, Zihao , Wen, Guangrui , Huang, Xin , Zhou, Haoxuan , Yan, Ruqiang et al. A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2022 , 69 (6) , 6298-6307 . |
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