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Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Cyprian F. Ngolah, Ed Morden, Yingxu Wang
Copyright: © 2013 |Pages: 15
ISBN13: 9781466626515|ISBN10: 1466626518|EISBN13: 9781466626829
DOI: 10.4018/978-1-4666-2651-5.ch012
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MLA

Ngolah, Cyprian F., et al. "Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks." Advances in Abstract Intelligence and Soft Computing, edited by Yingxu Wang, IGI Global, 2013, pp. 158-172. https://doi.org/10.4018/978-1-4666-2651-5.ch012

APA

Ngolah, C. F., Morden, E., & Wang, Y. (2013). Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks. In Y. Wang (Ed.), Advances in Abstract Intelligence and Soft Computing (pp. 158-172). IGI Global. https://doi.org/10.4018/978-1-4666-2651-5.ch012

Chicago

Ngolah, Cyprian F., Ed Morden, and Yingxu Wang. "Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks." In Advances in Abstract Intelligence and Soft Computing, edited by Yingxu Wang, 158-172. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2651-5.ch012

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Abstract

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

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