Detection of particles with or without charge arouses enormous interest in energy, medical service, industrial processing, and equipment monitoring fields. In this paper, a self-powered particles sensor based on a triboelectric nanogenerator (PS-TENG) is designed to characterize particles by detecting the outputs induced by the triboelectrification between the particles and the PTFE surface. Ascribed to the cumulative triboelectric charging effect on the PTFE surface, the output voltage of PS-TENG is significantly amplified. Then the relationships between the output voltage and the material and size of particles are systematically studied, based on which the characteristics of the random particles are accurately detected. The detection principle and characterization method using PS-TENG for charged particles are also proposed and further demonstrated by systematical comparison with detection of Faraday cup method. Furthermore, based on a PS-TENG with double electrodes, the average velocity of particles and particle mass flow can also be measured. This work promotes the development and application of triboelectric nanogenerator in self-powered particle characterization, mass flow monitoring, equipment fault diagnosis, and accident prediction. This paper propose a sensitive self-powered particle sensor based on cumulative triboelectric charge, using which the size, material and other features of particles can be accurately detected. This type of TENG has promising potential applications in self-powered particles characterization, mass flow monitoring, equipment fault diagnosis and accident prediction. • A TENG-based particles sensor is proposed, which exhibits high sensitivity due to the cumulative tribocharge. • Accurate particle detections are realized based on the relationships between the outputs and features of particles. • By comparing with Faraday cup test, the detection of both particles with and without charge is verified. • Methods for measuring velocity and mass flow of particles are developed based on TENG-based sensors.