Bearing is one of the essential components of mechanical systems, bearing fault detection is of great importance in bearing production and system fault diagnosis. In this paper, a novel toolbox for bearing fault detection using the bearing vibration signals is proposed. Two baseline models are included: 1. Baseline for Feature Engineering Based Method, which consists of three steps: time-frequency feature extraction, Pearson Correlation Coefficient (PCC) reduction and classification. 2. Baseline for Deep Learning Based Methods: a powerful deep neural network model consists of Convolutional Blocks and Residual Blocks. In the paper, the experimental results show that the methods in our toolbox are sufficiently robust to produce results with accuracy between 98% and 100%.