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.

故障诊断在建筑冷水机应用领域中具有重要意义，它可以减少能源浪费和维护成本，确保稳定的运行和更好的服务。为了提高诊断性能，本研究提出了一种基于数据自产生和深度卷积神经网络（SP-CNN）的新型故障诊断方法，其中提出了一种简单有效的数据增强技术，以充分利用优良的特征提取和深度CNN的模式识别功能可诊断典型的冷水机组故障。SP-CNN模型不是将故障数据转换为图片以供模型处理为数字类型，而是将数据直接转换为数字化图像，以避免由多重转换引起的错误。提出的数据自产生技术可以有效地增加诊断信息，并有助于提高性能。结果表明，该方法是有效的，SP量级为16的SP-CNN的诊断准确率达到97.03％，比无数据自生成的CNN的诊断准确率高1.63％。由于可能会出现过度拟合或拟合不足的情况，这不一定意味着SP比例越大，性能越好。提出的SP-CNN模型在总体诊断准确性和每个故障的个体准确性方面也表现出比反向传播（BP）网络和另一种CNN方法更好的性能。还发现，与在计算机视觉中的应用不同，对于故障诊断问题，特征序列对模型性能具有很大的影响。