This study presents a fault diagnosis and isolation framework for cable wear in cable-driven manipulators, aiming to improve the system reliability and safety during operation. During the fault diagnosis phase, by integrating the mapping relationship between manipulator joint angles and cable tension, a cable tension model is created to accurately predict tension changes under various working conditions. Based on this, a physics-informed fault diagnosis network is developed. By leveraging the dynamic features of tension signals and joint angle data and optimizing with a multi-objective loss function, accurate fault diagnosis of faulty cables is achieved. In the fault isolation phase, this research designs a fiber Bragg grating sensor based on a synchronous compensation method to stably and precisely collect joint movement information and introduces a three-driven cable synchronous compensation strategy. This strategy sequentially performs multiple equidistant synchronous contraction operations on the three driving cables of each joint, thereby reducing the impact of dynamic errors on isolation accuracy. The optical fiber sensor is used to collect the dynamic responses of the manipulator′s pitch and yaw angles, and two-dimensional feature maps are generated using the Gram Angle Difference Field algorithm. An ensemble classification model is then constructed to achieve precise isolation of manipulator cable damage. Experimental results show that the proposed method achieves a fault diagnosis accuracy of 98.48% in the cable fault diagnosis experiments and a fault isolation accuracy of 94.89% in the cable fault isolation experiments. The approach demonstrates significant advantages in cable tension prediction, fault diagnosis, and localization, providing robust support for the safe operation of cable-driven manipulators.