云模型方法在选煤厂跳汰系统中的故障检测与诊断

针对传统的故障检测与诊断方法的局限性,笔者结合信息融合思想和云模型算法,提出了用于选煤厂跳汰系统故障检测与诊断的云模型方法。采用一维云模型推理映射算法,代替传统神经网络方法的训练过程,融合多源信息合并处理,保证检测和诊断的正确性,并进行实时检测仿真。结果表明：系统辨识精度较高,能很好地反应跳汰系统工作情况,并能及时判断。该方法用于选煤厂跳汰系统故障检测与诊断可行。

Fault detection and diagnosis for coal preparation plant jigging system based on cloud model and information fusion

Aimed at eliminating negative features of the conventional fault detection and diagnosis,this paper proposes the cloud model method designed for fault detection and diagnosis for coal preparation plant jigging system,combined with the cloud model ideas and information fusion algorithm.The method involves substituting reasoning mapping algorithm based on one-dimensional cloud model for conventional neural network training process for the consolidation of multi-source information integration to ensure the accuracy of detection and diagnosis and real-time detection simulation.The results show that the system,capable of a higher accuracy identification,a better response to jigging system work,and timely judgments,promises to come into feasible use in fault detection and diagnosis of coal preparation plant jigging system.