School of Electro-Mechanical Engineering, Xidian University, Xi'an 710071, China.
School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China.
Comput Intell Neurosci. 2019 Sep 26;2019:5367217. doi: 10.1155/2019/5367217. eCollection 2019.
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 ( ) 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 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 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.
本文提出了一种故障检测与诊断（FDD）方法，该方法使用一维卷积神经网络（1-D CNN）和 WaveCluster 聚类分析来检测和诊断空气处理单元送风温度（ ）控制回路中的传感器故障。在该方法中，1-D CNN 用于从原始数据中提取人工引导特征，然后通过 WaveCluster 聚类对提取的特征进行分析。可疑的传感器故障通过表示聚类来指示和分类。此外，还引入了无罪程序以进一步提高 FDD 的准确性。在验证中，主要使用误报率和漏报率来证明所提出的 FDD 方法的效率。结果表明，该方法可以有效地检测和诊断控制回路中的突发传感器故障，并且在噪声范围为 6 dBm∼13 dBm 内具有良好的鲁棒性。
