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Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery

In this paper, we focus on the diagnostic algorithm of the diagnosis system for plant maintenance. In the current plant, the reduction of maintenance costs and the avoidance of sudden fault of equipment are required to improve the operation rate of the plant. The fault diagnosis is performed for this requirement, and the collection and analysis of actual fault data are indispensable to diagnose machinery conditions. For some reasons, however, there are many kinds of equipment whose fault data collection are difficult. In this paper, we select the motor in plant as such kind of equipment and propose a new diagnostic algorithm.