断层(地质)
柴油发电机
发电机(电路理论)
保险丝(电气)
可靠性工程
可靠性(半导体)
计算机科学
柴油
断层模型
故障指示器
工程类
人工智能
故障检测与隔离
汽车工程
电气工程
电子线路
地质学
功率(物理)
地震学
执行机构
物理
量子力学
作者
Shuai Yu,Feng Leng,Wei Xie,Yongmao Wang
标识
DOI:10.1109/wcmeim56910.2022.10021381
摘要
The effective fault diagnosis of the diesel generator sets can enable maintenance personnel to locate the fault location timely and accurately, and find the cause of the fault, which plays a vital role in ensuring the stable and reliable operation of the diesel generator sets. In order to fuse multiple evidence bodies and solve the problems such as uncertainty in fault diagnosis caused by single information, this paper combines the Deep Belief Network with D-S evidence theory to build a multi-level decision fusion model to realize the deep diagnosis of diesel generator sets fault, which can not only locate the fault location but also get the fault cause. The calculation results show that the model is effective in improving the accuracy of fault diagnosis for diesel generator sets, and improves the reliability of fault diagnosis.
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