A fault identification method for Fuel cell engine air supply subsystem pressure sensor based on Random forest

Abstract

To detect possible failures of the pressure sensor in the fuel cell engine air supply subsystem, this study proposes a fault identification method based on Random forest. To simulate faults in the sensor, we injected deliberate faults and constructed a fault dataset based on it. This dataset includes gradual fixed deviation and gradual complete deviation. The peRandom forestormance of training accuracy is observed on different fault datasets, aiming to identify fault types based on the deviation in accuracy caused by the presence of polluted or corrupted samples in the dataset. The results indicate that machine learning can effectively distinguish the data collected within 9 min. The validity of the residual law established by the root mean square error (RMSE) can be demonstrated through an example, wherein successful identification of intermittent faults 1.2/1.5 can be achieved a difference of 33%, which can effectively distinguish fault categories. In addition, this method does not rely on high-precision models and effectively utilizes abundant sensor data and the uneven distribution of data sets under faults, including sensors other than the target fault sensor, for machine learning to identify different types of sensor faults.