人工神经网络
领域(数学)
可靠性(半导体)
机器学习
断层(地质)
朴素贝叶斯分类器
人工智能
计算机科学
支持向量机
工程类
地质学
地震学
数学
物理
量子力学
功率(物理)
纯数学
作者
Ruonan Liu,Boyuan Yang,Enrico Zio,Xuefeng Chen
标识
DOI:10.1016/j.ymssp.2018.02.016
摘要
Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
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