Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning. 

https://link.springer.com/content/pdf/10.1007/s11831-022-09804-w.pdf

Advances in Sparrow Search Algorithm: A Comprehensive Survey

2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale decomposition (PCHIP-LCD), and the effective ISCs are selected to reconstruct signals based on kurtosis-correlation coefficient (K-C) criteria. Secondly, the multi-dimensional degradation feature set of reconstructed signals is extracted, and then the sensitive degradation indicator IICAMD is calculated by fusing the improved independent component analysis (IICA) and Mahalanobis Distance (MD). Thirdly, the false fluctuation of the IICAMD is repaired by using the gray regression model (GM) to obtain the health indicator (HI) of the rolling bearing, and the start prediction time (SPT) of the rolling bearing is determined according to the time mutation point of HI. Finally, generalized regression neural network (GRNN) model based on HI is constructed to predict the RUL of rolling bearing. The experimental results of two groups of different rolling bearing data-sets show that the proposed method achieves better performance in prediction accuracy and reliability.

A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing.

Fengjie Fan

Papers

Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation

Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis

General synchroextracting chirplet transform: Application to the rotor rub-impact fault diagnosis

Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA

Classification and identification of epileptic EEG signals based on signal enhancement