To address the problems of scarce fault samples, high risk in reproducing faults on real equipment, and high cost, this paper proposes a marine elevator modelling method and a fault injection method based on multi-physics causal modelling for a fault sample generation method. Different from the traditional measurement-bias fault injection method that only superimposes deviations at the level of measurable signals, the proposed method establishes a fault injection model based on the mechanical-electrical-hydraulic coupling characteristics of marine elevators, and by dynamically modifying key component-level physical parameters, realises the evolution of the elevator from component-level performance changes to system-level functional failure. Based on the marine elevator fault-injection method proposed in this paper, the method’s effectiveness in fault propagation across multiple components is verified through fault-physical-quantity characterisation behaviour analysis and comparative experiments on performance with measurement-bias injection. Meanwhile, the effectiveness of the proposed method as a verification benchmark for fault diagnosis is demonstrated through the consistency of the effects of multiple typical fault diagnosis algorithms. The proposed method can generate fault samples with strong physical causality, providing reliable fault data support for the training of intelligent operation and maintenance and fault diagnosis algorithms for marine elevators.