提升小波阈值去噪的故障检测与诊断方法研究

The Research of Fault Detection and Diagnosis Based on Lifting Scheme Wavelet and Threshold Denosing

针对提升小波阈值去噪方法中软、硬阈值去噪效果不太好和故障检测与诊断准确率不高的缺点,提出了一种双变量阈值函数与提升小波相结合的去噪方法,并将其应用到故障检测与诊断中。利用所提方法对数据进行去噪处理,通过主元分析(PCA)方法对去噪后的数据进行故障检测与诊断。为验证该方法的有效性,将该方法运用到化工TE过程,并将双变量阈值函数与软、硬阈值函数进行对比。实验结果证明,双变量阈值函数与提升小波结合的方法具有更好的去噪效果,同时也提高了PCA方法对故障检测与诊断的准确率。

In order to improve the wavelet threshold denosing effect and overcome the low efficiency and accuracy problem of conventional fault detection and diagnosis （FDD） meth ods, an novel approach based on threshold denosing function with double variable parameters and lifting scheme wavelet is proposed. Firstly, the proposed method is applied to denose the data of TE process. Then, the preprocessed data is classified by Principle Component Analysis （PCA） to detection and diagnose the faults. To certify the characteristic of the method, the proposed method is applied to detect and diagnose the faults in TE process, and compare with the soft and hard threshold methods which are used with lifting wavelet and PCA. Simulation results show that, the ensemble denosing method based on threshold denosing function with double variable parameters and lifting scheme wavelet is better than conventional denosing methods, meanwhile, the accuracy of fault detection and diagnosis with PCA is improved.