NDWCM-PSO based energy management strategy for passive sensing

To ensure that train passive sensors achieve long-term uninterrupted collection and transmission of state data under complex operating conditions, a passive sensing energy management strategy based on an improved particle swarm optimization (PSO) algorithm was proposed. Firstly, the passive sensing energy conversion relationship was analyzed, analytical equations among energy characteristic parameters, operating modes, and train operating conditions were obtained, and a passive sensing energy model was constructed. Secondly, to address the deficiency that the original PSO algorithm is prone to falling into a local optimum in the later stage of solving complex problems, a nonlinear decreasing weights and chaotic mapping - particle swarm optimization (NDWCM-PSO) was proposed. By establishing a solution model based on the NDWCM-PSO algorithm, energy matching among system energy characteristic parameters, operating modes, and trains operating conditions was realized, and passive sensing energy management was completed. Finally, the effectiveness of the algorithm was verified by building a passive experimental system for train traction motor bearing condition. The results indicate that under speeds of 20 km/h and 80 km/h, the system realizes long-term uninterrupted data acquisition and transmission functions with a sampling frequency of 10 kHz and a sampling interval of 60 s.