Fault diagnosis for building chillers based on data self-production and deep convolutional neural network

Abstract

Fault diagnosis is of great importance in the field of building chiller application, which can reduce energy waste and maintenance cost, ensure stable operation and better service. To improve the diagnosis performance, this study presents a novel fault diagnosis method based on data self-production and deep convolutional neural network (SP-CNN), in which a simple, effective data augmentation technology is proposed to well utilize the excellent feature extraction and pattern recognition capability of deep CNN to diagnose typical chiller faults. Instead of transforming the fault data into a picture for the model to process into digital type, the SP-CNN model directly transforms the data into a digitized image to avoid possible errors caused by multi-transformation. The proposed data self-production technique can effectively augment the diagnosis information and help improve the performance. The results show that the proposed method is effective and the diagnosis accuracy of the SP-CNN with SP scale of 16 reaches 97.03%, higher than that of CNN without data self-production by about 1.63%. Due to the possible occurrence of over-fitting or under-fitting, it does not necessarily mean that the larger the SP scale, the better the performance. The proposed SP-CNN model also shows a better performance than back propagation (BP) network and another CNN method in terms of the overall diagnosis accuracy and the individual accuracy for each fault. It is also found that unlike the application in computer vision, for a fault diagnosis problem, the feature sequence has a great influence on the model performance. The accuracy is further improved to 98.02% by re-arrangement of features.