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In practical chiller systems, applying efficient fault diagnosis and Isolation (FDI) techniques can significantly reduce the energy consumption and keep the environment comfortable. The success of the existing methods for fault detection and diagnosis of chillers relies on the condition that sufficient labeled data are available for training. Generally, the number of labeled data is limited while abundant unlabeled normal data are available. To make effective use of the large number of unlabeled data to realize fault detection and isolation (FDI), a novel data driven FDI method based on the deep autoencoder (DAEFDI) is proposed. Specifically, DAEFDI method consists of two parts: fault detection (DAEFD) and fault isolation (DAEFI). For the DAEFD part, the unlabeled normal data is used to learn a DAE model to describe the chiller system. When the reconstruction error is higher than the threshold, it is considered that the system deviates from the normal state. For the DAEFI part, when it is detected that the system is in a fault state, the source variable caused the fault can be found according to their proportion in the reconstruction error. Experimental results demonstrate the effectiveness of the DAEFDI method.