Considering that the performance of a condition monitoring model directly determines the final execution effect of the CBM technology, this paper focused on the impact of the number of signals on the performance of a condition monitoring model. In the study, the influence factors except the number of signals used in the model are controlled by using the same training data, the same condition monitoring algorithm(AAKR) with optimal hyper-parameters determined according to the same standard, and the average performance of models formed by multiple random extractions of the same number of signals from the training data. The calculation results show that, overall, the performance of the model deteriorates as the number of selected signals increases. This may be because, as the number of signals increases, the dimensions of the space in which the training sample points are located in also increases. For a certain number of samples, the more dispersed in the higher dimensional space, and this is very disadvantageous for the AAKR method which essentially obtains the predicted value by interpolation. As a result, the performance of the model is degraded.