Abstract: With the fierce competition in wind power industry，market's requirements for the product delivery，operation and maintenance service provided by machine manufacturers are increasingly demanding.A low-cost fault self-detecting method suitable for controller Input and Output（I/O）hardware was proposed based on BP neural network algorithm，in order to mitigate the negative impacts of certain defective I/O hardware of controllers′batch inventory on the operation and maintenance service of the project.The neural network model was trained with the random sequence of fault sample data set. The self-detecting circuit constructed with I/O hardware and relays is designed for collecting sampling signals which are processed into a characteristic matrix of normalized data.Putting the data into the self-detecting model，reference results will be output after fault identification and classification. The experiment preliminarily verified that the method can effectively identify the I/O hardware fault without specified equipment，which is of practical application values.

Key words: BP neural network, fault detecting, controller, I/O hardware, characteristic matrix, wind turbine controller, self