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在实际的故障检测诊断过程中,由于故障产生原因的多样性和关联性,往往很难做出准确的判断。但是借助于专家的经验的模糊故障诊断方式可以通过专业人士的丰富经验和统计数据对实际的故障情形进行判断,进而做出准确的判断。这种做法综合模糊聚类和模糊模式识别等知识,能够对系统的故障发生时间和类型经行较理想的判别结果。根据以上理论提出模糊故障诊断的聚类-贴近度法,通过实验验证本方法的可行性。
In the actual process of fault diagnosis, it is often difficult to make accurate judgments due to the diversity and relevance of the causes of faults. However, with the help of the expert’s experience of fuzzy fault diagnosis, the expert’s rich experience and statistics can be used to judge the actual fault situation and make accurate judgment. This approach, combined with knowledge of fuzzy clustering and fuzzy pattern recognition, can identify the time and type of system failures better. According to the above theory, the clustering-closeness method of fuzzy fault diagnosis is proposed, and the feasibility of this method is verified through experiments.