FMDPgram: An Improvement of FMD for Rotating Machinery Fault Diagnosis

Recently, feature mode decomposition (FMD) has been proposed and has demonstrated robust performance in the application of rotating machinery fault diagnosis. However, FMD does not have parameter adaptability, and its influencing parameters (i.e., segment number, mode number and filter length) need to be defined in advance. Inspired by the decomposition mode of wavelet packet transform, this article cleverly proposes a binary decomposition structure of FMD to form the concept of FMD packet (FMDP). Furthermore, the tiling structure of FMDP is constructed, thus forming the FMDPgram. In FMDPgram, the frequency band of the raw signal is evenly divided into 2 segments, and each FMD generates two submode components (SMCs), thus simplifying the hyperparameter optimization into a single-parameter optimization. Additionally, an indicator called narrowband peak-to-average ratio (NPAR) is proposed for optimal SMC positioning, and which is displayed in the FMDPgram tile map. First, performing FMDPgram with NPAR on the raw signal. Then, the optimal SMC is located based on the NPAR in the FMDPgram tile map. Finally, the envelope power spectrum of the optimal SMC is calculated to characterize the fault characteristic information. FMDPgram is tested on simulated and experimental data, and compared with FMD, spectral kurtosis, VMD and improved Kurtogram to evaluate its performance in rolling bearing diagnosis under low signal-to-noise ratio and non-Gaussian interference.