基于改进主元分析方法的空调系统传感器故障检测和诊断研究

传感器在空调系统中主要起着监测和控制的作用,影响空调系统的正常运行,从而带来能耗增加等不良影响。本文提出了结合小波变换的数据优化,以及基于神经网络的故障诊断优化的改进主元分析方法,用于空调系统传感器故障检测和诊断研究。通过对比数据优化前后主元分析的结果,发现同样0. 850 0累计贡献率原则上,采用小波变换去除噪声后,主元个数减少了两个,蒸发器进口温度传感器的固定偏差、漂移、精度下降等故障检测效果分别提升了0. 020 7、0. 020 8、0. 041 5,风量传感器固定偏差故障检测效果提升了0. 160 6。为了进一步找出故障源,在小波变换和主元分析的基础上,将求得的主元作为神经网络的输入,对5个传感器固定偏差故障进行测试,故障诊断结果分别为0. 766 7、0. 866 7、0. 900 0、1. 000 0、1. 000 0。

Sensor Fault Detection and Diagnosis of Air-conditioning System Based on Improved Principal Component Analysis Method

Sensors mainly play monitoring and controlling roles in air-conditioning systems and affect their normal operation, thereby causing adverse effects such as increased energy consumption if there are faults in sensors. In this study, an improved principal component analysis method combining wavelet transform data optimization and neural network-based fault diagnosis optimization is proposed for the sensor fault detection and diagnosis in an air-conditioning system. By comparing the results of principal component analysis before the data optimization and the results of the principal component analysis after the data optimization, it was found that in the principle of the same 0.8500 cumulative contribution rate after the wavelet transform used to remove noise, the number of principal components was reduced by two; the detection effect was improved by 0.0207, 0.0208, and 00415respectively; and the effect of the airflow sensor fixed deviation failure detection was improved by 0.1606. To find the source of the fault, the principal component analysis was used as the input of the neural network to test five sensor fixed deviation faultsbased on the wavelet transform and principal component analysis. The fault diagnosis results were 0.7667, 0.8667, 0.9000, 1.0000, and 1.0000, respectively.