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传感器是空调系统的重要组成部分,它对空调系统的运行状态进行实时监控,并将运行数据传输到控制系统中。传感器发生故障将使得空调系统偏离正常运行状态,导致系统能耗增加。主元分析法(Principal Component Analysis,PCA)是传感器故障检测与诊断中常用的数据分析方法。本文采集了多联机系统传感器正常运行数据,通过人为调整得到故障运行数据;采用正常运行数据对PCA进行建模,用训练好的模型对故障运行数据进行检测与诊断,分析检测与诊断结果。本文中传感器故障工况包含压缩机排气感温包漂移、脱落、精度下降以及完全失效故障。检测与诊断的结果为:基于PCA的多联机系统压缩机排气温度传感器故障检测与诊断结果良好。
Sensor is an important part of the air conditioning system, which monitors the operating status of the air conditioning system in real time and transmits the operating data to the control system. Sensor failure will make the air conditioning system deviate from the normal operation, resulting in increased system energy consumption. Principal component analysis (PCA) is a commonly used data analysis method in sensor fault detection and diagnosis. In this paper, we collect the data of normal operation of multi-line system sensors and obtain the fault operation data through manual adjustment. We use the normal operation data to model the PCA, detect and diagnose the fault operation data with the trained model, and analyze the detection and diagnosis results. In this paper, the sensor failure conditions include the compressor exhaust temperature package drift, fall off, decline in accuracy and complete failure. The results of testing and diagnosis are as follows: PCA-based multi-line system compressor exhaust temperature sensor fault detection and diagnosis results are good.