Application of Mallat wavelet fast transforms and IDRNN in real-time fault detection and identification for satellites

A real-time fault detection and identification (FDI) scheme of time-variant signals for a complex system was studied. A sliding-window Mallat wavelet fast transform was first introduced to avoid depending on the signals in all periods for the classical wavelet transform, and the computing effect was improved, which makes sense that the real-time fault detection is effective. Secondly, aimed at the problem that it is difficult to identify the fault by using time-variant signals, an improved dynamic recurrent neural network (IDRNN) was utilized to identify the fault intelligently after detecting the fault. Finally, the scheme, including fault detection based on the sliding-window Mallat wavelet and fault isolation based on the optimized IDRNN, was applied into a satellite attitude control simulation platform to verify the online diagnosis result. Experimental results show that the sliding-window Mallat wavelet fast transform is consistent with the classical wavelet transform in real-time scenarios, IDRNN has a better generalization ability for identifying time-variant signals, and the scheme including the sliding-window Mallat wavelet and IDRNN can implement detecting the faults and classifying the multiple faults based on real-time monitoring signals for the complex system.