Abstract: As a critical component of belt conveyors, idlers are prone to frequent failures, significantly impacting the efficiency and safety of coal mine operations. Extensive research has been conducted worldwide on idler fault diagnosis techniques and coordinated control strategies for belt conveyors. However, a universally accepted and effective monitoring and control framework is still lacking. This paper provides a comprehensive review of idler fault types and failure mechanisms, emphasizing the unique challenges associated with diagnosing faults in underground belt conveyors. The current state of research on idler fault diagnosis and post-failure coordinated control is systematically analyzed in four key areas: ① Fault State Perception Technologies: The principles and applications of vibration, acoustic, thermal, and image-based sensing technologies are discussed. ② Data Processing and Feature Extraction: Various signal processing methods and feature extraction strategies are examined. ③ Fault Identification Methods: The evolution of idler fault identification techniques is reviewed, ranging from traditional approaches to advanced machine learning, deep learning, and multi-source information fusion. ④ Post-Failure Coordinated Control: Challenges such as the high complexity of control systems, poor compatibility between different control strategies, and difficulties in ensuring the accuracy and real-time performance of condition monitoring data are highlighted. Despite notable advancements in idler fault diagnosis technologies, several challenges persist, including low fault identification accuracy, limited monitoring coverage, single-parameter detection, and the inability to classify faults or assess their severity. Furthermore, there is inadequate evaluation of potential fault-induced risks and a lack of comprehensive management strategies. Based on these findings, future research directions are proposed: advancing from single-parameter monitoring to multi-dimensional integrated monitoring, transitioning from sparse coverage to dense and comprehensive surveillance, shifting from surface-level diagnosis to in-depth analysis of fault evolution mechanisms, progressing from reactive responses to proactive fault prevention, and promoting the transformation from localized management to global coordinated control.