基于小波分析的水力机组故障诊断奇异数据还原研究

Research on the Singularity Data Reduction of Fault Diagnosis of Hydraulic Generator Sets Based on Wavelet Analysis

采用小波变换中奇异信号检测的基本原理,首先检测出信号中的奇异点,将奇异点剔除后再通过处理过的细节系数和近似系数一起重构信号,根据重构信号再对机组进行重新分析。实例仿真结果表明,小波能够正确识别水力机组奇异信号,并对机组信号进行准确重构,基于此可正确认识机组故障并准确描述机组状态。

In this paper, the fundamental principle for detecting signal singularity in wavelet transformation is applied. First, the singularity in signals is detected. Secondly the singularity points are removed and the signals are reconstructed by processed detail coefficients and approximate coefficients. Then, the reconstructed signals are used to analyze the sets once again. The example simulation shows that wavelet can discern singularity signals correctly and reconstruct them properly, on the basis of which, the faults in generating sets can be correctly recognized and the state of generating sets can be accurately described.