PSRONet: A Deep Reinforcement Learning-Based Sensor Configuration Framework in Railway Point Machines Fault Diagnosis

Despite rapid progress in data-driven fault diagnosis for Railway Point Machines (RPMs), most studies tacitly assume a fixed sensing setup: predefined sensor types, placements, channels, and sampling rates. This leaves a critical gap: the sensing layer (i.e., the sensor configuration of the monitoring system) itself is rarely optimized for the diagnostic task, cost constraints, or robustness to real-world disturbances. We address this gap by formulating sensor-configuration optimization as a Markov Decision Process (MDP), framing it as a task-aligned decision problem. To solve this, we introduce the Policy-guided Sensor Reduction and Optimization Network (PSRONet), a Deep Reinforcement Learning (DRL) framework…

• TS
  Tianjin Science and Technology Program

  Award: AB22035008