A Semi-Supervised Multi-Scale Deep Adversarial Model for Fan Anomaly Detection

Abstract

Failure of cooling fan will have a great impact on the operation of the air-conditioning cooling system. Traditional anomaly detection methods for fans rely on manual feature extraction, which is easy to lose effective information. Deep learning-based methods have potential to overcome such a problem, however, it is difficult to train the network without enough monitoring data. In operation, cooling usually worked at high speed, and its monitoring data contain much high-speed rotational noise. This will confuse the networks to focus on high-energy noise while lose the weak but useful information. To solve these problems, we propose a semi-supervised multi-scale deep adversarial model (SMDAM) for fan anomaly detection. In SMDAM, we propose the Linear Spectral Line Elimination (LSLE) technique to reduce the interference of rotational noise spectral lines on network training. Subsequently, a multi-scale feature extraction module is introduced to enrich diagnostic information, which extracts features from different scales without increasing the depth of the convolutional layer. Finally, two engineering data sets are used to verify the superiority of the proposed method. The results show that SMDAM is superior to other methods.