Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA

•Parallel dual-parameter optimized RSSD is proposed to decouple compound fault signal.•The improved MOMEDA is presented to enhance the weak periodic fault impulse features.•Proposed method can accurately diagnose the weak compound fault of planetary gearbox.•Experiment and applications validate the proposed method and shown its superiority. Due to the complexity and harsh operating environment of planetary gear transmission system, compound fault may co-exist in the planetary gearbox, which results in different type faults coupling together and the weak fault impulse features being completely submerged by strong ambient noise, thus making it a great challenge to extract fault-related information from planetary gearbox. To address these issues, a novel compound fault features extraction technique based on parallel dual-parameter optimized resonance-based sparse signal decomposition (RSSD) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed in this contribution. Firstly, according to the oscillation properties of different type faults, the parallel dual-parameter optimized RSSD constructs wavelet basis functions matching with fault features to adaptively decouple compound fault signal into high and low resonance components. Then, the improved MOMEDA is applied to deconvolute the resonance components to eliminate the interference of complex transmission path and strong ambient noise, thereby enhancing the weak periodic fault impulses. Finally, envelope demodulation processing for the enhanced signals is carried out to extract fault characteristic frequencies and identify different type faults. The effectiveness and feasibility of the proposed method are validated using both the numerical simulated signals and practical experimental dates from two different types of planetary gearbox compound fault. Moreover, comparisons with some existing methods illustrate the superiority of the proposed method to identify weak compound fault under strong ambient noise.