中央空调传感器双重降噪模糊故障检测方法

针对现有降噪方法存在噪声残留以及异常检测指标受噪声影响较大的问题,提出中央空调传感器双重降噪和模糊指标的故障检测方法。 自适应噪声的完整经验模态分解(complete EEMD with adaptive noise,CEEMDAN)所具有的噪声残余等问题,用局部均值估计提取 k 阶模态替换模态估计完成初次降噪;而早期出现的虚假模式,先通过相关系数准则筛选含噪分量尽可能保留有效信息,然后计算奇异值差分谱确定降噪阶次进行奇异值分解(singular value decomposition,SVD)完成二次降噪。 最后,结合能量和峭度系数提出模糊指标作为异常信号控制限进行故障检测。 采用中央空调实验系统运行数据对所提方法进行验证,结果表明,该方法具有良好的降噪及敏感特征筛选能力,信噪比提升 20. 203 7 dB,均方误差平均减小 48. 75%,故障检测准确率平均提升 8. 67%,响应速度提升 33. 3%,抗噪性及检测效果提升明显。

Double noise reduction fuzzy fault detection method forsensors in central air conditioning

The current noise reduction methods have noise residue and inadequate adaptability, so that the abnormal detection index isgreatly affected by noise, a sensor fault detection method based on double noise reduction and fuzzy index for central air conditioning isproposed. Complete EEMD with adaptive noise (CEEMDAN) is used to extract k-order modes and replace modal estimation to achieveinitial noise reduction. For the false mode appearing in the early stage, firstly, the noise-containing components are screened by thecorrelation coefficient criterion to retain the effective information as much as possible. Then, singular value difference spectrum iscalculated to determine the order of denoising and singular value decomposition ( SVD) to complete the secondary denoising. Theexperimental data of central air conditioning system are used to verify the proposed method, this method has good ability of noisereduction and sensitive feature screening, the SNR was improved by 20. 203 7 dB, the mean square error was reduced by 48. 75% onaverage, the fault detection accuracy was improved by 8. 67% on average, and the response speed was improved by 33. 3%.