Data-driven fault diagnosis models for specific mechanical equipment lack generalization capabilities. As a core component of various types of machinery, the health status of bearings makes sense in analyzing derivative failures of different machinery. This study proposes a bearing health monitoring and fault diagnosis algorithm based on 1D-CNN (one-dimensional convolution neural network) joint feature extraction. The algorithm first partitions the original vibration signal of the bearing in segmentations. The signal segmentations are used as feature learning spaces and input into the 1D-CNN in parallel to extract the representative feature domain under each working condition. To avoid processing overlapping information generated by faults, a bearing health status discriminant model is built in advance based on the feature domain sensitive to health status. If the health model recognizes that the bearing is not in a healthy state, the feature domain will be reconstructed jointly with the original signal and coupled with an automatic encoder for failure mode classification. Bearing data provided by Case Western Reserve University are used to carry out experiments. Experimental results demonstrate that the proposed algorithm inherits the accuracy and robustness of the deep learning model, and has higher diagnosis accuracy and lower time delay.