Aiming at the problem that the existing fault prediction model does not have the ability to identify unknown faults， it is necessary to re-collect data to train the model or learn and identify unknown faults with the help of other components， an unknown fault prediction method based on the CapsNet model is proposed. This method can effectively process the multi-dimensional state sensing signals of complex equipment， realize the accurate sensing of equipment faults， and adaptively adjust the model and predict unknown faults when unknown faults occur. The conversion matrix is constructed to predict the existence and posture of the corresponding high-level features from the features of the low-level capsules. The process by which the dynamic routing algorithm integrates the prediction vector generated by the low-level capsule into the high-level capsule that agrees with it and forms the feature vector is described in detail. In the process of implementing fault feature classification in the last layer of CapsNets， a threshold judgment method is proposed. By reasonably selecting the value range of the threshold， the capsule network model can perfectly distinguish between known and unknown faults and realize accurate fault prediction. The proposed method is used to verify the performance of a series of well-trained CapsNets models. Through experiments， it can be found that the proposed method can better realize unknown fault prediction， which can prove the feasibility of the method.