Results 51 to 60 of about 73,405 (211)

Evolvable Transformer Fault Diagnosis Model Combining Feature Analysis and Machine Learning [PDF]

Jisuanji gongcheng
Transformers, which possess complex mechanisms and exert an extensive influence, are important equipment in large power systems. The state detection and fault diagnosis of transformers are hence a key challenge in traditional power systems; they also ...
Yedong MAO, Chunhui ZHANG, Jie CHEN
doaj   +1 more source

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Chinese Journal of Mechanical Engineering, 2019
Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge.
Juying Dai, Jian Tang, Shuzhan Huang, Yangyang Wang   +3 more
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Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

IEEE Access, 2021
Research on the intelligent fault diagnosis method of rolling bearing based on laboratory data has made some achievements. However, due to the change of working conditions and the lack of historical data of the same equipment in the actual diagnosis ...
Qingfeng Wang, Shuai Wang, Bingkun Wei, Wenwu Chen, Yufei Zhang   +4 more
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Study on the fault Petri nets of the unmanned roadheader based on the transition memory

矿业科学学报, 2017
Unmanned roadheader is the realization of the concept of unmanned mining technology, coal mine intelligent mining requires roadheader with intelligent diagnosis technology.
Yang Jianjian, Tang Zhiwei, Liu Qiang, Wang Fengdong, Yin Tongzhou, Wu Miao   +5 more
doaj  

Design methodology for smart actuator services for machine tool and machining control and monitoring [PDF]

, 2011
This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and ...
Archimède, Bernard, Desforges, Xavier, Habbadi, Abdallah   +2 more
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A Deep Multi-Label Learning Framework for the Intelligent Fault Diagnosis of Machines

IEEE Access, 2020
Deep learning has been applied in intelligent fault diagnosis of machines since it trains deep neural networks to simultaneously learn features and recognize faults.
Jianjun Shen, Shihao Li, Feng Jia, Hao Zuo, Junxing Ma   +4 more
doaj   +1 more source

Self-tuning routine alarm analysis of vibration signals in steam turbine generators [PDF]

, 2012
This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated ...
Campbell, Graeme, Costello, Jason, McArthur, Stephen, West, Graeme   +3 more
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Fault detection and diagnosis for process control rig using artificial intelligent [PDF]

, 2010
This paper focuses on the application of artificial intelligent techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a process control rig.
Abdul Rahman, Ribhan Zafira, Khalid, Marzuki, Yusof, Rubiyah   +2 more
core  

Data-driven Soft Sensors in the Process Industry [PDF]

, 2009
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control.
Abdi, Alhoniemi, Angelov, Angelov, Angelov, Arazo-Bravo, Atkeson, Bastin, Bauer, Bishop, Bogdan Gabrys, Bonne, Breiman, Bro, Casali, Chen, Chen, Chen, Chen, Chen, Choi, Chruy, Davies, Dayal, De Wolf, Desai, Devogelaere, Ding, Dong, Dong, Dote, Doyle, Dunia, Dunia, Dunia, Eriksson, Fellner, Fortuna, Fortuna, Frank, Freund, Funahashi, Gabrielsson, Gabrys, Gabrys, Gabrys, Gabrys, Gama, Geladi, Gomez, Gonzalez, Gonzalez, Goodwin, Gosset, Guyon, Han, Hastie, He, Hodge, Hotelling, Jackson, James, Jang, Jiang, Jolliffe, Jordaan, Jos de Assis, Kadlec, Kadlec, Kalos, Kampjarvi, Kittler, Kohavi, Kohonen, Kordon, Kourti, Kourti, Krogh, Kuncheva, Lee, Lee, Lee, Lee, Li, Li, Lin, Lin, Luo, Macias, Mandic, Marjanovic, Meleiro, Menold, Nauck, Neogi, Nomikos, Nomikos, Nomikos, Opitz, Park, Pearson, Pearson, Petr Kadlec, Poggio, Prasad, Principe, Qin, Qin, Qin, Qin, Radhakrishnan, Rnnar, Rong, Rotem, Ruta, Ruta, Schafer, Scheffer, Serneels, Sibylle Strandt, Stanimirova, Su, Tzanakou, van Sprang, van Sprang, Vapnik, Venkatasubramanian, Venkatasubramanian, Venkatasubramanian, Vilalta, Walczak, Walczak, Walczak, Wang, Wang, Wang, Wang, Warne, Weiss, Widmer, Wold, Wold, Wold, Wolpert, Yan, Yang, Zadeh, Zamprogna, Zamprogna, Zhang   +149 more
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Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest

Nuclear Engineering and Technology
Data-driven fault diagnosis techniques are significant for the stable operation of nuclear power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually faces imbalance data problems with small fault samples and much ...
Jun Guo, Yulong Wang, Xiang Sun, Shiqiao Liu, Baigang Du   +4 more
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