Subway fault diagnosis method based on data mining

Abstract

The invention discloses a subway fault diagnosis method based on data mining. The method comprises the steps that history fault data of a subway is disposed to form an original decision table; the original decision table is pre-processed by a rough set, redundant attributes are reduced to obtain a new attribute set, and the original decision table is transformed to a new decision table with a boolean attribute; association rules between an attribute set of the new decision table and faults are mined to store in a rule base; state data and the rules in the rule base are compared in real time to make fault diagnosis. According to the subway fault diagnosis method based on data mining, the rough set is adopted to perform data pre-processing, data mining is used in fault diagnosis, the faults occurring on the subway are judged, and not only difficulties brought by the fact that subway working personnel judges the faults according to experiences are solved, but also the speed and accuracy of fault solving are greatly improved.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a method for diagnosing subway faults based on data mining. Background technique [0002] With the rapid development of society, subways have been built in major and medium-sized cities, and have gradually become the main means of transportation in the city. People have put forward higher requirements for the reliability and safety of subways. The relevant departments of the subway must take effective measures to ensure the stable and reliable operation of the subway while continuously improving the quality of the subway itself. However, during the long-term operation of the subway, the probability of equipment failure is very high. If it cannot be dealt with in time, it will cause great losses. , so it is increasingly important to carry out fault diagnosis on the subway in time and effectively. [0003] In fault diagnosis, the selection of fault diagnosis method is the key ...

AI Technical Summary

Problems solved by technology

However, the expert system has the following disadvantages, such as the "bottleneck" problem of expert knowledge acquisition, does not have self-learning ability, can only solve fixed special problems, and cannot expand the knowledge base

However, the subway system is complex, and each system involves many faults. These faults are characterized by diversity, complexity, and concealment. In the process of obtaining data during operation, it is very difficult to quickly and timely determine the fault and its cause.

Therefore, such traditional fault diagnosis methods are no longer applicable

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