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Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System
ALAM, TAJ; DUBEY, PARITOSH; KUMAR, ANKIT - In: International Journal of Distributed Systems and … 9 (2018) 4, pp. 20-39
Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous...
Persistent link: https://www.econbiz.de/10012044203
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An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices
Peng, Jun; Feng, Lixiao; Chen, Guorong - In: International Journal of Cognitive Informatics and … 12 (2018) 4, pp. 101-114
Due to high temperature, high pressure, high corrosion, and many other factors, the hazardous chemical device is facing more severe security challenges than other industries. Now, the monitoring methods have been very mature, which play a basic monitoring role, not a predictive fault diagnosis....
Persistent link: https://www.econbiz.de/10012043718
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Three Phase Induction Motor's Stator Turns Fault Analysis Based on Artificial Intelligence
Hussein, H. A. Taha; Ammar, M. E.; Hassan, M. A. Moustafa - In: International Journal of System Dynamics Applications … 6 (2017) 3, pp. 1-19
This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on modelling the motor in the dq frame for both health and fault cases to facilitate recognition of motor current. Using an Adaptive...
Persistent link: https://www.econbiz.de/10012047452
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Colored Petri Nets Based Fault Diagnosis in Service Oriented Architecture
Bhandari, Guru Prasad; Gupta, Ratneshwer; Upadhyay, … - In: International Journal of Web Services Research (IJWSR) 15 (2018) 4, pp. 1-28
Diagnosing faults in a service-oriented architecture (SOA) is a difficult task due to limited accessibility of software services. Probabilistic approaches of diagnostic faults may be insufficient due to the black-box nature of services. In SOA, software services may be obtained by different...
Persistent link: https://www.econbiz.de/10012048778
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Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey
Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A. - In: Journal of Industrial Engineering and Management (JIEM) 15 (2022) 1, pp. 31-57
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D...
Persistent link: https://www.econbiz.de/10013326270
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Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks
Alblawi, Adel - In: Energy Reports 6 (2020), pp. 1083-1096
In the study presented in this paper, the deterioration in the performance of an industrial gas turbine during the operation design point was simulated by using the thermodynamic principle and a multi feedforward artificial neural networks (MFANN) system. Initially the thermodynamic model was...
Persistent link: https://www.econbiz.de/10012652465
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A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process
Huda, Shamsul; Abdollahian, Malihe Akhavan; Mammadov, Musa - In: European journal of operational research : EJOR 237 (2014) 3, pp. 857-870
Persistent link: https://www.econbiz.de/10010384683
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A Bayesian network approach for fixture fault diagnosis in launch of the assembly process
Jin, Sun; Liu, Yunhua; Zhongqin, Lin - In: International journal of production research 50 (2012) 23, pp. 6655-6666
Persistent link: https://www.econbiz.de/10009673529
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A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions
Wang, Zheng; Liu, Qingxiu; Chen, Hansi; Chu, Xuening - In: International journal of production research 59 (2021) 16, pp. 4811-4825
Persistent link: https://www.econbiz.de/10012625725
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Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors : state of the art survey
Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A. - In: Journal of industrial engineering and management : JIEM 15 (2022) 1, pp. 31-57
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D...
Persistent link: https://www.econbiz.de/10012815091