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学者姓名:陈雪峰

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< Page ,Total 66 >
IMS Technical Committee TC-3: Condition Monitoring and Fault Diagnosis Instrument EI SCIE Scopus
期刊论文 | 2022 , 25 (8) , 5-9 | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE
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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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Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions SCIE Scopus
期刊论文 | 2022 , 173 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
SCOPUS Cited Count: 13
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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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Helicopter Health and Usage Monitoring System in China EI SCIE Scopus
期刊论文 | 2022 , 25 (2) , 45-52 | IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE
SCOPUS Cited Count: 1
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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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Real-Time Terahertz Characterization of Minor Defects by the YOLOX-MSA Network EI SCIE Scopus
期刊论文 | 2022 , 71 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
SCOPUS Cited Count: 16
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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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Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis EI SCIE Scopus
期刊论文 | 2022 , 71 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
SCOPUS Cited Count: 15
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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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Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning EI SCIE Scopus
期刊论文 | 2022 , 27 (2) , 985-998 | IEEE-ASME TRANSACTIONS ON MECHATRONICS
WoS CC Cited Count: 2 SCOPUS Cited Count: 57
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Abstract :

Accurately and intelligently identifying faults of the planetary gearbox is essential in the safe and reliable operation and maintenance of the mechanical drive system. Recently, fault diagnosis of planetary gearbox has acquired tremendous progress, especially with the rising popularity of deep learning (DL). However, most methods are standard supervised learning where the input is directly mapped to a fault type, and with strong feedback. Also, their learning ways are static and unlike human learning that gradually acquires knowledge by interaction with the environment. To a certain extent, these deficiencies reduce the generalization and intelligence level of DL-based fault diagnosis methods. Besides, due to harsh working conditions, signals acquired often have strong noise and nonlinear features, leading to relatively low accuracy if raw signals are used as the input directly. Thus, this article proposes a new fault diagnosis method based on time-frequency representation and deep reinforcement learning (DRL). We first define fault diagnosis as a sequential decision-making problem in the classification Markov decision process. Next, the vibration signals are converted to uniform-sized TF maps by synchro-extracting transform to enhance the robustness of feature representation. Finally, a diagnosis agent is built and trained in the framework of DRL to learn the optimal classification policy automatically. Experimental results show that this method not only achieves better generalization and stability with an overall accuracy of over 99.5% in single-speed load cases but also outperforms others in multiwork conditions.

Keyword :

Classification policy Decision making deep learning (DL) deep reinforcement learning (DRL) fault diagnosis Fault diagnosis Feature extraction Games IEEE transactions Mechatronics planetary gearbox Vibrations

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GB/T 7714 Wang, Hui , Xu, Jiawen , Sun, Chuang et al. Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2022 , 27 (2) : 985-998 .
MLA Wang, Hui et al. "Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS 27 . 2 (2022) : 985-998 .
APA Wang, Hui , Xu, Jiawen , Sun, Chuang , Yan, Ruqiang , Chen, Xuefeng . Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2022 , 27 (2) , 985-998 .
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Displacement difference feedback control of chatter in milling processes SCIE Scopus
期刊论文 | 2022 , 120 (9-10) , 6053-6066 | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
SCOPUS Cited Count: 5
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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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Deep-learning-based open set fault diagnosis by extreme value theory EI SCIE Scopus
期刊论文 | 2022 , 18 (1) , 185-196 | IEEE Transactions on Industrial Informatics
WoS CC Cited Count: 8 SCOPUS Cited Count: 90
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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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Acoustic Mode Measuring Approach Developed on Generalized Minimax-Concave Regularization and Tikhonov Regularization EI SCIE Scopus
期刊论文 | 2022 , 71 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
SCOPUS Cited Count: 7
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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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A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery EI SCIE Scopus
期刊论文 | 2022 , 69 (6) , 6298-6307 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
WoS CC Cited Count: 5 SCOPUS Cited Count: 51
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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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