The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately describe the health status of critical components. Therefore, it is widely used in the field of rotating equipment health state prediction. However, there are two major problems in predicting the health status of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and if only one feature in the time or frequency domain is selected for fault analysis, it will be susceptible to harsh operating environments and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time series data, which are difficult to accurately convert into the health status of key components. In order to solve the above problems, this paper proposes a combined prediction model combining a bidirectional long- and short-term memory network (BiLSTM), a self-organizing neural network (SOM) and particle swarm optimization (PSO). Firstly, the SOM is utilized to fuse the fault characteristics of multiple vibration signals of key components to obtain an indicator (HI) that can reflect the health status of rotating equipment and to also compensate for the vulnerability of single signal characteristics in the time or frequency domain to environmental influences. Secondly, the K-means clustering method is employed to cluster the health indicators and determine the health state, which solves the problem of determining the health of a component from unsupervised vibration signal data which is quite difficult. Finally, the particle swarm optimized BiLSTM model is used to predict the health state of key components and the bearing dataset from the IEEE PHM 2012 Data Challenge verifies the method’s effectiveness and validity.