Fault diagnosis methods based on a time-series convolution and the comparison of multiple methods

Valves and other actuators may fail and cause economic losses or safety accidents. To ensure the stable operation of a control system, it is necessary to identify the failures of various valves and carry out the corresponding maintenance. Several methods are designed and implemented for valve fault diagnosis in this paper. In particular, a novel fault diagnosis method based on a time-series convolution network (FDM-TSCN) is proposed, which is built on a time-series data feature extracting and convolutional neural network. FDM-TSCN can classify 18 out of 19 types of fault, while many other methods cannot. This algorithm is presented in detail and implemented as a prototype system. Comprehensive simulations are performed on valve fault datasets that are generated by the development and application of methods for actuator fault diagnosis in industrial systems (DAMADICS). The simulation results prove the effectiveness and superiority of the proposed FDM-TSCN method. All of the source codes and related data in the paper are made available, which enables other researchers to verify the work easily and may inspire them to carry out more informed research.