High accuracy prediction of degradation trend provides valuable information in establishing reasonable maintenance decision-making with the goal of improving the maintenance efficiency and avoiding sudden downtime. The extraction of degradation features and the prediction algorithm are the key factors in degradation trend prediction. In this work, based on composite multiscale grey entropy (CMGE) and dynamic particle filter (PF), a novel prediction architecture is proposed to improve accuracy under different working conditions. The CMGE is proposed as the degradation feature indicator extracted from rolling bearing vibration signal. The dynamic PF is proposed to predict the degradation trend of rolling bearing. Three rolling bearing accelerated life tests were conducted to evaluate the performance of the proposed method for rolling bearing degradation trend prediction. Experimental results demonstrate CMGE has good monotonicity and weak data length dependence, which can effectively describe the degradation trend of rolling bearing, and the proposed dynamic PF achieves higher prediction accuracy than the traditional PF and GM model, respectively.

Entropy-based nonlinear dynamic techniques have been considered to be a powerful tool and widely and successfully utilized to analyze the vibration signals in rolling bearing. This paper presents a new entropy-based nonlinear dynamic method, called the Grey entropy (GreyEn) for vibration performance degradation evaluation of rolling bearing. Similar to the existing measures, GreyEn is the negative natural logarithm of the conditional probability that two vectors similar for m points remain similar for the next m+1 points. Importing the concept of grey system theory, vectors’ similarity is measured by the grey relational degree (GRD). Simulation signals are employed to evaluate the effectiveness of the proposed method. The results suggest that GreyEn, compared with sample entropy (SampEn) and fuzzy entropy (FuzzyEn), leads to more efficiently measure the complexity. The validity of GreyEn is also assessed through three experimental tests. The experimental results show that GreyEn, compared with SampEn and FuzzyEn, can effectively evaluate the vibration performance degradation of the rolling bearing.

Rolling bearing reliability is often affected by the processing quality, however the life reliability or operating reliability do not involve the study of rolling bearing processing quality assessment. In order to avoid bearing failure caused by bearing processing quality, it is necessary to evaluate the reliability of the bearing quality assurance capability. The difficulty in reliable evaluation of bearing's quality assurance capability is how to quantify the uncertain relationships between the bearing quality and its influence factors, and then determine the weight of different influencing factors. Therefore, in this paper, a novel method for determining the weight of bearing quality-influencing factors based on bootstrap maximum entropy method and similarity method is proposed, and then the quality-achieving reliability model is established. The experimental results show that the proposed method can effectively quantify the relationship between the bearing quality and its influencing factors, and accurately assess the bearing quality assurance capability and bearing quality processing level under the condition of a small number of experimental bearings. Compared with other quality-achieving reliability method, the proposed method is more effective.

Rolling element bearings are used in all rotating machinery, and the degradation performance of rolling element bearings directly affects the performance of the machine. Therefore, high reliability prediction of the performance degradation trend of rolling element bearings has become an urgent research problem. However, the degradation characteristics of the rolling element bearings vibration time series are difficult to extract, and the mechanism of performance degradation is very complicated. The accurate physical model is difficult to establish. In view of the above reasons, based on the vibration performance data of rolling element bearings, a model of bearing performance degradation trend parameter based on wavelet denoising and Weibull distribution is established. Then, the phase space reconstruction of the series of bearing performance degradation trend parameter is carried out, and the prognosis is obtained by the improved adding weighted first-order local prediction method. The experimental results show that the bearing vibration performance degradation parameter can accurately depict the degradation trend of the bearing, and the reliability level is 91.55%; and the prediction of bearing performance degradation trend parameter is satisfactory: the mean relative error is only 0.0053% and the maximum relative error is less than 0.03%.

Degradation stage prediction, which is crucial to monitoring the health condition of rolling bearings, can improve safety and reduce maintenance costs. In this paper, a novel degradation stage prediction method based on hierarchical grey entropy (HGE) and a grey bootstrap Markov chain (GBMC) is presented. Firstly, HGE is proposed as a new entropy that measures complexity, considers the degradation information embedded in both lower- and higher-frequency components and extracts the degradation features of rolling bearings. Then, the HGE values containing degradation information are fed to the prediction model, based on the GBMC, to obtain degradation stage prediction results more accurately. Meanwhile, three parameter indicators, namely the dynamic estimated interval, the reliability of the prediction result and dynamic uncertainty, are employed to evaluate the prediction results from different perspectives. The estimated interval reflects the upper and lower boundaries of the prediction results, the reliability reflects the credibility of the prediction results and the uncertainty reflects the dynamic fluctuation range of the prediction results. Finally, three rolling bearing run-to-failure experiments were conducted consecutively to validate the effectiveness of the proposed method, whose results indicate that HGE is superior to other entropies and the GBMC surpasses other existing rolling bearing degradation prediction methods; the prediction reliabilities are 90.91%, 90% and 83.87%, respectively.