Prediction and analysis of bearing vibration signal with a novel gray combination model

Abstract

Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

Enhancing Operational Efficiency in Industrial Systems: A DEA-Grey Integration

“…This method will serve as a model for China's high-tech industries. Li et al aim to propose a dynamic decision-making method to evaluate the enterprises' performance based on a GM(1,1) model and regret theory with Pythagorean fuzzy numbers (PFNs) [29] while the objective of Wu et al, 2019 is to develop a novel dynamic emergency decision-making method with probabilistic hesitant fuzzy information based on GM (1,1) for handling emergencies [30]. propose a new model that can reliably predict the vibration signal of bearings [31].…”

Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing

“…Rolling bearings are one of the most important components of mechanical equipment, and their safety and reliability are crucial for the operation of machines and industrial production. Due to long-term use and complex environmental factors, rolling bearing failures are inevitable and can lead to shortened remaining useful life or damage and casualties of property [1][2][3]. As a result, demand for bearing failure diagnosis is also increasing.…”

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

“…However, machine learning methods are used under the condition that the sufficient samples are available, which is difficult to meet in degradation stage prediction of rolling bearings [35]. Grey prediction theories [36] have been proposed concerning specialties for a smaller number of sample attempts to explore development laws utilizing the mining of the internal regulation of data series, but these are still subject to lower prediction accuracy and higher time complexity. In this work, a new approach called the grey bootstrap Markov chain (GBMC) for rolling bearing degradation stage prediction is proposed through the combination of grey prediction theory, bootstrap method [37] and Markov chains [38].…”