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CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things [PDF]
Sensors, 2023 Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle.
Zhenwen Gui +5 more
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Fault prediction of aircraft engine based on adaptive hybrid sampling and BiLSTM [PDF]
Scientific ReportsTo address the class imbalance problem in aero-engine fault prediction, we propose a novel framework integrating adaptive hybrid sampling and bidirectional LSTM (BiLSTM).
Junying Hu, Xu Jiang, Huan Xu, Ke Zhang
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Benchmark for welding gun fault prediction with multivariate time series data [PDF]
Scientific DataIn the automotive industry, machinery failures of the resistance spot welding (RSW) guns would interrupt the manufacturing lines and cause unplanned downtime, potentially resulting in a significant loss of production and reliability.
Xiaoye Wang, Changsheng Zhang, Tao Wang
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Research on Fault Prediction Method of Electronic Equipment Based on Bi-LSTM [PDF]
Hangkong bingqi, 2022 In order to improve the accuracy of electronic equipment fault prediction results, a fault prediction method based on bi-directional long short term memory (Bi-LSTM) is proposed.
Ni Xianglong, Shi Chang’an, Ma Yueliang, Liu Lei, He Jian
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Overview of Intelligent Radar Fault Prediction and Detection Technology [PDF]
Jisuanji kexue, 2023 Radar fault prediction and fault detection technology is the key technology for the transformation of radar equipment maintenance from traditional regular maintenance to intelligent condition-based maintenance.To ensure the performance of radar combat ...
ZHAI Yuting, CHENG Zhanxin, FANG Shaojun
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Predicting Fault Slip via Transfer Learning [PDF]
Nature Communications, 2021 Abstract Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly due to large training data sets. In Earth however, earthquake interevent times range from 10's-100's of years and geophysical data typically exist for only a portion of an earthquake cycle.
Kun Wang +3 more
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Stereo Matching Algorithm Based on Global Error Energy Function [PDF]
Jisuanji gongcheng, 2017 In order to solve the problem of large amounts of computation in the global stereo matching algorithm,this paper proposes an improved algorithm by introducing global error energy function.It considers global error energy function as the cost of stereo ...
WANG Xinyan,PAN Wei,WANG Yuelian,LIU Xinyue
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Research on Maintenance Fault Diagnosis System for E-class High-Performance Computer [PDF]
Jisuanji gongcheng, 2022 E-class computer systems typically have huge scales.Consequently, the total number of abnormal faults is bound to increase, resulting in difficulty in fault diagnosis.Thus, there is an urgent need for the development of a more accurate and efficient real-
JIAN Lantao, REN Xiujiang, ZHANG Zhen, SHI Song, HUANG Yiming, ZHANG Chunlin
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Research on fault prediction of working face equipment based on time series data
Gong-kuang zidonghua, 2021 Coal mine working face equipment are usually consists of several complex system modules that have strong coupling among each other. Moreover, the equipment fault mechanism is complex.
ZHENG Lei
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Predicting faults from cached history [PDF]
Proceedings of the 1st India software engineering conference, 2007 We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location ...
Sunghun Kim +3 more
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