fault type
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2022 ◽  
Vol 12 (2) ◽  
pp. 650
Author(s):  
Meng-Hui Wang ◽  
Shiue-Der Lu ◽  
Chun-Chun Hung

Surge arresters primarily restrain lightning and switch surges in the power system to avoid damaging power equipment. When a surge arrester fails, it leads to huge damage to the power equipment. Therefore, this study proposed the application of a convolutional neural network (CNN) combined with a symmetrized dot pattern (SDP) to detect the state of the surge arrester. First, four typical fault types were constructed for the 18 kV surge arrester, including its normal state, aging of the internal valve, internal humidity, and salt damage to the insulation. Then, the partial discharge signal was measured and extracted using a high-speed data acquisition (DAQ) card, while a snowflake map was established by SDP for the features of each fault type. Finally, CNN was used to detect the status of the surge arrester. This study also used a histogram of oriented gradient (HOG) with support vendor machine (SVM), backpropagation neural network (BPNN), and k-nearest neighbors (KNN) for image feature extraction and identification. The result shows that the proposed method had the highest accuracy at 97.9%, followed by 95% for HOG + SVM, 94.6% for HOG + BPNN, and 91.2% for HOG + KNN. Therefore, the proposed method can effectively detect the fault status of surge arresters.


Author(s):  
Suliang Ma ◽  
Jianlin Li ◽  
Yiwen Wu ◽  
Chao Xin ◽  
Yaxin Li ◽  
...  

Abstract Evaluating the mechanical state of high-voltage circuit breakers (HVCBs) based on vibration information has currently become an important research direction. In contrast to the unicity of the travel–time and current–time curves, the vibration information from the different positions is diverse. These differences are often overlooked in HVCB fault identification applications. Additionally, the fault recognition results based on different location information often vary, and conflicting diagnosis results directly cause the accurate identification of the fault type to fail. Therefore, in this paper, a novel multi-information decision fusion approach is proposed based on the improved random forest (RF) and Dempster-Shafer evidence theory. In the proposed method, the diagnostic distribution of all classification regression trees (CART) in the RF is considered to solve the conflicts among the multi-information diagnosis results. Experimental results show that the proposed method eases the contradiction of multi-position diagnostic results and improves the accuracy of fault identification. Furthermore, compared to the common classifiers and probability generation methods, the effectiveness and superiority of the proposed method are verified.


2022 ◽  
Vol 74 (1) ◽  
Author(s):  
Masanao Shinohara ◽  
Shin’ichi Sakai ◽  
Tomomi Okada ◽  
Hiroshi Sato ◽  
Yusuke Yamashita ◽  
...  

AbstractAn earthquake with a magnitude of 6.7 occurred in the Japan Sea off Yamagata on June 18, 2019. The mainshock had a source mechanism of reverse-fault type with a compression axis of WNW–ESE direction. Since the source area is positioned in a marine area, seafloor seismic observation is indispensable for obtaining the precise distribution of the aftershocks. The source area has a water depth of less than 100 m, and fishing activity is high. It is difficult to perform aftershock observation using ordinary free-fall pop-up type ocean bottom seismometers (OBSs). We developed a simple anchored-buoy type OBS for shallow water depths and performed the seafloor observation using this. The seafloor seismic unit had three-component seismometers and a hydrophone. Two orthogonal tiltmeters and an azimuth meter monitored the attitude of the package. For seismic observation at shallow water depth, we concluded that an anchored-buoy system would have the advantage of avoiding accidents. Our anchored-buoy OBS was based on a system used in fisheries. We deployed three anchored-buoy OBSs in the source region where the water depth was approximately 80 m on July 5, 2019, and two of the OBSs were recovered on July 13, 2019. Temporary land seismic stations with a three-component seismometer were also installed. The arrival times of P- and S-waves were read from the records of the OBSs and land stations, and we located hypocenters with correction for travel time. A preliminary location was performed using absolute travel time and final hypocenters were obtained using the double-difference method. The aftershocks were distributed at a depth range of 2.5 km to 10 km and along a plane dipping to the southeast. The plane formed by the aftershocks is consistent with the focal mechanism of the mainshock. The activity region of the aftershocks was positioned in the upper part of the upper crust. Focal mechanisms were estimated using the polarity of the first arrivals. Although many aftershocks had a reverse-fault focal mechanism similar to the focal solution of the mainshock, normal-fault type and strike–slip fault type focal mechanisms were also estimated. Graphical Abstract


2022 ◽  
Vol 13 (1) ◽  
pp. 0-0

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.


2021 ◽  
Vol 2137 (1) ◽  
pp. 012025
Author(s):  
Zhiyong Yang ◽  
Bingyuan Yang

Abstract MMC-HVDC has the characteristics of limited amplitude, controlled phase angle and unequal positive and negative sequence impedance. Therefore, the fault characteristics of flexible direct system and AC power grid tie line are quite different from traditional synchronous power supply, which may affect the performance of AC power grid sudden variable protection. Therefore, a new AC protection method based on positive sequence current sudden variable impedance is proposed. The results show that when an in zone fault occurs, the direction of current sudden changes at both ends is the same, and the positive sequence impedance may approach the line impedance; When an out of area fault occurs, the current abrupt variables at both ends have opposite directions and equal sizes, and the positive sequence impedance is much greater than the line impedance. Based on the above characteristics, the criteria of fault start up and fault type are constructed. The simulation results show that the protection method can realize fault discrimination quickly and reliably.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Somayeh Bakkhtiari Ramezani ◽  
Amin Amirlatifi ◽  
Thomas Kirby ◽  
Shahram Rahimi ◽  
Maria Seale

One of the main goals of predictive maintenance is to accurately classify the temporal trends as early as possible, detect faulty states and pinpoint the root cause of the fault. Undoubtedly, neither late nor early maintenance is desirable and incurs additional operating costs; however, early identification of faulty trends and scheduling on-time maintenance is crucial to smooth machinery operation. Various data-driven techniques have been used to identify faults; nevertheless, many of these techniques fail to perform when faced with missing values at run time or lack any explanation on the root cause of the fault. The present work offers a comprehensive study on different techniques used for fault type classification and compares their performance in identifying the mode of operation for the PHME21 dataset. We also evaluate the robustness of such classifiers against missing values. This study shows that tree-based techniques are best suited to perform root cause analysis for each faulty state and establish rules for faulty conditions.


2021 ◽  
Vol 2095 (1) ◽  
pp. 012058
Author(s):  
Xiaoyu Xian ◽  
Haichuan Tang ◽  
Yin Tian ◽  
Qi Liu ◽  
Yuming Fan

Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.


2021 ◽  
Vol 1207 (1) ◽  
pp. 012020
Author(s):  
L J Kong ◽  
Y W Huang ◽  
Q B Yu ◽  
J Y Long ◽  
S Yang

Abstract Complicated industrial robot structure and harsh working conditions may cause signal features collected in the condition monitoring process to be seriously disturbed. In this paper, a joint feature enhancement mapping and reservoir computing (FEM-RC) method is presented to handle the industrial robot fault diagnosis problem. Firstly, a feature enhancement mapping (FEM) method is proposed to achieve intraclass distance minimization and interclass distance equalization to obtain an enhanced feature matrix. Then, the first reservoir computing (RC) network is adopted to map the original feature matrix to the feature enhancement matrix, and the second RC network is for fault type classification. The results of the experiment carried out on a six-axial industrial robot demonstrate that compared with other peer models, the present FEM-RC has better fault diagnosis performance and robustness.


2021 ◽  
Author(s):  
Andrew Sabate ◽  
Rommel Estores

Abstract Unique single failing device is common for customer returns and reliability failures. When the initial and iterative Automatic Test Pattern Generator (ATPG) could not provide a sufficient diagnostic resolution, it can become quite challenging for the analyst to determine the failure mechanism in an efficient and effective way. Fault isolation could be performed in combination with the diagnosis results but there are cases with mismatch between the results (location, fault type, suspect nets). When the diagnostic resolution is low, the probability for such mismatches are high. This paper proposes an approach to increase the diagnostic resolution by utilizing a high-resolution targeted pattern (HRT) and single shot logic (SSL) patterns. Two cases will be discussed in the paper to highlight this approach and show in detail how it was utilized on actual failing units.


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