[Purposes] This study aims at directing the condition-based maintenance of equipment by expounding the connotation of complex equipment fault prognostic. [Methods] In this study, the relevant research contents, status, and methods were investigated, summarized, and analyzed. The existing fault prognostic methods were divided into different categories and the corresponding application conditions, advantages, and drawbacks were discussed. [Findings] The knowledge-based methods can take full advantage of the experiential knowledge from experts, but the knowledge acquisition was a bottleneck problem. The model-based methods had the advantages of in-depth understanding of the nature of the target systems, but it was difficult to establish accurate models for complex equipments. The data-driven methods relied on a large amount of data. However, the cost of acquiring typical data of some complex equipments was very high. The hybrid methods can overcome the limitation of a single method, but designing an effective hybrid model was challenging. [Conclusions] The hybrid methods can improve the intelligence and performance of the fault prognostic system, which is an important development trend of complex equipment falt prognostic.