A fault diagnosis method for chillers based on a sparsely local embedding deep convolutional neural network (sparsely local embedding network

SLENet) is proposed. The sparsely local embedding filters are adopted for feature selection in the first two layers of SLENet

as a result complicated training and adjusting process are avoided. In addition

the spatial pyramid max pooling layer is employed to construct the output layer of SLENet to reduce the output dimensions of the proposed network

and then alleviate the computationally burden of classification. The experimental data from ASHRAE RP-1043 are used to validate the fault diagnosis method. The results demonstrate that the method achieves high diagnostic accuracy compared to convolutional neural network (CNN) and support vector machine (SVM) methods.