Abstract: In order to construct a performance degradation index that can better characterize the bearing degradation process, a constrion method concerning improved one-dimensional deep convolutional neural network was proposed. Firstly, a one-dimensional deep convolutional neural network was built to extract features adaptively from original time domain signals, and the degradation characteristics of full-life time domain signals were described in detail. Secondly, a combined loss function was designed with combing the cumulative value of the positive and negative differential of degradation feature at adjacent points based on the mean square error function. Consequently, the positive differential value increases while the negative decreases in the training process, leading to the improved monotonicity of performance degradation index. Finally, the high-dimensional features are transformed into low-dimensional ones through the full-connection layer to realize the construction of performance degradation indicators. The effectiveness and feasibility of the proposed method were verified by conducting experiments on public and measured data sets.