Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Abstract EN

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit.

In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering.

The suspicious sensor faults are indicated and categorized by denoting clusters.

Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD.

In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method.

Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.