As the core equipment of the urban lighting system, the regular operation of streetlights is of great significance to urban lighting. Currently, streetlight fault detection is limited to preliminary fault phenomenon. To detect the streetlight′s specific fault category, this paper takes the streetlight operation data of the streetlight monitoring and data acquisition system as the object. A streetlight fault detection model based on improved VMD and feature selection is proposed combining the traditional method, the datadriven method and the signal processing method. First, principal component analysis filters the main variable parameters of streetlight operation data, and variational mode decomposition is used to decompose the screened parameters. Similarly, the whale optimization algorithm is introduced to improve the adaptability of the variational mode decomposition. Secondly, the Pearson coefficient selects the relevant IMF components, and the sample entropy is used to construct the fault feature vector. Through the experimental verification of the streetlight fault statistical data of the urban lighting monitoring system in Chongzuo City, Guangxi, the results show that the proposed fault diagnosis method can effectively extract the fault feature information of different fault states of streetlights. The correct rate of fault diagnosis is 93.75%, which provides a new way for streetlight fault diagnosis.