使用机器学习算法和振动数据预测强制鼓风机故障，以实现有效的维护策略

IF 0.9 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Failure Analysis and Prevention Pub Date : 2023-09-19 DOI:10.1007/s11668-023-01765-x

Khaled Salem, Ebtisam AbdelGwad, Hanan Kouta

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting Forced Blower Failures Using Machine Learning Algorithms and Vibration Data for Effective Maintenance Strategies

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来源期刊

Journal of Failure Analysis and Prevention ENGINEERING, MULTIDISCIPLINARY-

CiteScore

2.00

自引率

8.30%

发文量

200

期刊介绍： The Journal of Failure Analysis and Prevention (JFAP) is written for and read by individuals involved in failure analysis, materials scientists, and mechanical, manufacturing, aeronautical, civil, chemical, corrosion, and design engineers. The publication is unique in that it contains current news and technical articles, as well as archival peer-reviewed papers and reviews. JFAP provides information gathering techniques, technical analysis, and emerging tools that will assist failure analysis professionals in determining the cause of failures and eliminating failures in the future. Articles demonstrate the importance of failure analysis to product/performance improvements and industrial problem solving. JFAP is intended to be of interest to both the experienced and less experienced failure analysis practitioner with a focus on shared interest across the industries.Bi-monthly periodical for the failure analyst. Provides practical information for determining the cause of failures and eliminating future failures. Highlights information-gathering techniques, technical analysis and emerging tools to assist the future analyst. Peer-reviewed articles demonstrate the important of failure analysis, product/performance improvement and industrial problem solving. Written for the experienced and less experienced failure analysis practitioner.