基于经验模态分解去噪改进主成分分析的冷水机组传感器故障检测

Chiller sensor fault detection using denoising-based improved principal component analysis

提出了一种基于经验模态分解(EMD)阈值去噪(TD)和主成分分析(PCA)相结合的冷水机组传感器故障检测方法(EMD-TD-PCA)。采用EMD阈值去噪法去除原始数据中的噪声来提高数据质量,针对去噪后的数据构建PCA模型。采集了武汉市某电子厂螺杆式冷水机组的实际运行数据,用于验证故障检测效果,并与传统PCA方法和小波阈值去噪(Wavelet-TD-PCA)方法的传感器故障检测结果进行了对比。结果表明:EMD-TD-PCA可以有效提高冷水机组传感器的故障检测效率,同等偏差条件下,故障检测效果优于传统PCA方法和Wavelet-TD-PCA方法。对于小偏差(-1~1℃)故障,故障检测效果提升尤为明显。

Presents a chiller sensor fault detection method based on empirical mode decomposition (EMD) threshold denoising (TD) and principal component analysis (PCA). Uses EMD and TD method to enhance the data quality by removing the noise factor in the raw data set, and builds a PCA model with the data processed. Monitors the operational data of a screw chiller system of an electric factory in Wuhan to evaluate fault detection effect. Compares the proposed method with the traditional PCA method and wavelet threshold denoising PCA (Wavelet-TD-PCA) method. The results show that the EMD-TD-PCA method can effectively improve the chiller fault detection efficiency, and its fault detection results are better than those of the PCA and Wavelet-TD-PCA methods at the same bias. Especially at -1 to 1 ℃ bias fault, the fault detection results are improved.