Abstract: This paper designed and implemented a Fault Prognostics and Health Management (PHM) System for track equipment on ordinary-speed railways to address the high latency and excessive costs inherent in traditional maintenance modes. It integrated and analyzed multi-source full-life-cycle operational data of key track assets, including rails, turnouts, and ballasted track beds, by leveraging data middle platforms and big data technologies. It also introduced specialized models for rail wear prediction, track bed degradation analysis, and tamping effectiveness evaluation, thereby achieving full-life-cycle management and precise quality condition assessment of ordinary-speed railway track equipment. Application results show that the system breaks down data silos between different systems, enables cross-departmental data sharing and collaborative work, and provides robust technical support for the transformation of ordinary-speed railways toward predictive maintenance.