Automatic multi-fault recognition in TFDS based on convolutional neural network

In recent years, more than 300 sets of Trouble of Running Freight Train Detection Systems (TFDSs) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically. Motivated by the success of convolutional neural networks (CNNs) in the tasks of image recognition, this paper makes essential use of CNN models and proposes an automatic fault recognition system (AFRS) for recognizing four typical faults in TFDS simultaneously. AFRS is a two-stage system: In the first stage, a coarse-to-fine scheme based on CNN model is adopted to detect the target regions of side frame keys (SFKs) and shaft bolts (SBs) simultaneously. In the second stage, we establish another CNN model for multi-fault determination to recognize four typical faults emerged in the target regions of SFKs and SBs. The experimental results show that this system has an excellent performance of multi-fault recognition in TFDS. High recognition accuracy rates, low false ratios and low omission ratios are obtained for all the four typical faults, demonstrating the high recognition ability and robustness against various low quality imaging situations.