World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

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    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level


    JUNE 2022 Issue has been successfully launched on 1 June 2022.

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    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level

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Karim Abdel-Hakam Mohamed*, Galal Ali Hassaan, Adel Abdel-Halim Hegazy


In general intelligent diagnosis is carried out when known inputs are fed into black boxes which subsequently produce outputs in accordance with machine faults. Neural networks (NN) are suitable for these tasks and have been widely researched as an artificial intelligence tool for machinery fault diagnosis. By employing such a tool, maintenance personnel need not understand or operate the internal mechanisms of a neural network. They will only be responsible for inputting the appropriate data to a neural network. The neural network will then be trained on this data so that it can diagnose faults. Artificial neural networks demonstrated to provide an effective method for fault diagnosis in rotating machinery in terms of reliability. In this paper, vibration frequency features of mechanical unbalance, angular misalignment, and mechanical looseness are discussed for rotating machinery fault diagnosis. This paper then presents an approach for rotating machinery fault diagnosis using pattern classification tool of neural networks and frequency domain vibration analysis. Finally this pattern recognition approach applied on a real world case with effective results.

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