USING THE FAULT TREE AS A LOGICAL-PROBABILISTIC METHOD FOR ANALYSIS OF SHIP ELECTRIC MOTORS

S. Taranenko, S. Golubieva
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引用次数: 1

Abstract

Despite the progress in research and development of designs of modern electric motors for industrial enterprises for various purposes, including marine electric motors, reliable methods for determining the causes of their failures have not yet been created, and the application of well-known methods in practice, including diagnosing the technical condition of engines, not only requires a lot of labor and highly qualified personnel, but in many cases it is actually inefficient. This is especially true of severe operational damage arising from the destruction of parts and accompanied by a violation of the synchronization of their reciprocating and rotational motion. On the other hand, the performed study shows that the causes of malfunctions and failures of electric motors can also be determined by logical and probabilistic methods, including on the basis of fault tree analysis involving the results of research on various faults. By structuring the signs of failures, a failure tree was compiled that logically describes the cause-and-effect relationships between the failure event and the initial damage that caused it separately for each of the failure modes selected for analysis associated with severe damage to the electric motors of the type under study. As a result of using the fault tree in practice, it is possible to determine the causes of failure of electric motors and electric motors with automatic control systems with sufficient reliability and minimal time.
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用故障树作为船舶电机分析的逻辑概率方法
尽管在研究和开发用于各种目的的工业企业的现代电动机设计方面取得了进展,包括船用电动机,但尚未建立确定其故障原因的可靠方法,并且在实践中应用了众所周知的方法,包括诊断发动机的技术状况,这不仅需要大量的劳动力和高素质的人员,而且在许多情况下实际上效率低下。尤其是由于零件损坏而造成的严重操作损坏,并伴随着其往复和旋转运动的同步性被破坏。另一方面,所进行的研究表明,电机故障和失效的原因也可以通过逻辑和概率方法来确定,包括基于涉及各种故障研究结果的故障树分析。通过构建故障迹象,编制了一个故障树,该故障树从逻辑上描述了故障事件和初始损坏之间的因果关系,这些初始损坏分别针对所选的每种故障模式进行分析,这些故障模式与所研究类型的电动机的严重损坏有关。由于在实践中使用故障树,可以以足够的可靠性和最短的时间来确定电动机和具有自动控制系统的电动机的故障原因。
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来源期刊
自引率
0.00%
发文量
28
审稿时长
27 weeks
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