As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations.

The vibration signal from the planetary gearbox exhibits nonlinear and impulsive characteristics amidst strong noise, impeding the effective extraction of fault information and compromising the accuracy of fault diagnosis. To address this challenge, a fault diagnosis method rooted in feature mode decomposition (FMD) is proposed. Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. Subsequently, a convolutional neural network integrated with the support vector machine model (CNN‐SVM) is established, leveraging the convolutional neural network for feature extraction. Ultimately, SVM iteratively optimized by the particle swarm optimization (PSO) algorithm, serves as the classification technique. Simulation and experiment results demonstrate the effectiveness of this method in extracting and identifying fault information within planetary gearboxes.

As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method.