We propose a human–machine graphic control fault diagnosis system based on an extension neural network (ENN) and the chaos synchronization detection method. A high-frequency signal is injected into a photovoltaic (PV) module to observe the voltage variation under different fault conditions, and the fault type is diagnosed using the proposed algorithm. Firstly, defects are introduced into the PV module and a high-frequency signal is injected by a signal generator. The high-frequency oscilloscope through the high-frequency sensor captures the voltage signal. The feature of the signal is calculated by the chaos synchronization detection method and a chaotic error scatter map is established. The chaos eye coordinates of the scatter diagram are used as eigenvalues for fault diagnosis. Finally, the ENN is used for fault diagnosis of the PV module. Also, from a comparison of analysis results with those of a traditional neural network, the ENN can identify the type of PV module fault rapidly and the recognition accuracy is as high as 87.5%. Small changes in the voltage signal can be detected effectively by using the chaos synchronization detection method, and the preprocessing of big data is reduced. The PV module fault state is identified accurately to demonstrate the applicability of the method to PV module fault diagnosis.