With the rise of industrial big data and the Internet of Things, the research on bearing life and its health detection will be pushed to a new level. Research on the evaluation of its health status and the prediction of its operating status based on big data will help to improve the bearing The reliability of the equipment avoids the deterioration of the bearing health and extends its working life. This article reviews the achievements made in bearing health detection and life prediction at home and abroad, and prospects for its prospects. Although the existing research has achieved some noteworthy results, few researches have been carried out on key technologies such as the collection of operating data of bearing equipment, the extraction of information on degradation and fault characteristics, the assessment of health conditions, and the prediction of remaining life. The research has important theoretical significance and application value for further improving the service life of bearings. Keywords: rolling bearings; reliability; big data; health monitoring; life prediction Rolling bearings are one of the key components that determine the health and life of machinery. The research on the life and health of bearings has the characteristics of many influencing factors, large life dispersion, time-consuming testing, difficult data accumulation, and difficult theoretical modeling. With the accumulation of data in the process of manufacturing and operation, the development and popularization of technologies such as the Internet of Things, cloud computing and intelligent algorithms, the production environment has gradually acquired the basis of a big data environment, and on this basis, the health management and life of bearings Prediction can enable the bearing to achieve mutual comparison with its own state, the accumulation of data and experience models, and the collaborative diagnosis of faults, thereby becoming an intelligent system with self-learning and self-growth capabilities. In such a system, the bearing system is no longer an independently operating individual. Prognostic analysis of all equipment in the entire manufacturing operation system is carried out through the information network system, so that the control and decision-making end can see the status and operating capability of the bearing. At this stage, each type of bearing is managed by its professional sensors and analysis equipment, and a unified platform has not been established to integrate common features in bearing failures with their corresponding general and reconfigurable algorithms. On the other hand, although sensors and analyzers can complete basic monitoring and prediction tasks, they are not well adapted to the discovery of root failure causes, high-response prediction methods, large-scale equipment clusters, and collaborative management of multiple bearings. . Bearing health monitoring and life prediction will face demanding requirements. For this reason, this article will introduce in detail the results of bearing big data health monitoring and life prediction, and look forward to its development direction. 1 Research on the life and reliability of rolling bearings 1.1 Development and status quo of bearing life and reliability testing machines Bearing life refers to the total number of revolutions or working hours of a rolling element or raceway of a bearing before a fatigue peeling occurs. Bearing life testing is inseparable from bearing life testing machines. The development of bearing life testing machines has also witnessed the development of bearing life research. The test research on bearing life in my country is mainly led by two scientific research institutes, Luoyang Bearing Research Institute and Hangzhou Bearing Test and Research Center (United Nations Aid), supplemented by the life and reliability test bases of other related companies, to jointly undertake the bearing life of my country’s bearing industry , Reliability and performance test research work. At present, the design, R&D, and production of bearing life testing machines in my country have been completely independent, and some technical concepts have reached the international leading level. However, compared with SKF, Schaeffler, Timken, NT, etc. A large foreign bearing company started very late. In the early 20th century, the development of China’s bearing industry mainly relied on the technical support of the former Soviet Union’s big brother. The life test of bearings was mainly carried out on the basis of the ZS bearing life testing machine, and the evaluation quality of this testing machine has long been eliminated from the service performance of the bearing. Development requirements; and the “F&M 5″ new rolling bearing fatigue life testing machine introduced from the United States by the Hangzhou Bearing Test and Research Center (HBRC) through the United Nations aid project is not only expensive and technologically monopolized, but also uses a pneumatic high-voltage power source and 60Hz electrical frequency. Not suitable for China’s national conditions. Therefore, it is imperative for bearing life intensification testing machines to realize independent production. In the 1990s, Hangzhou Bearing Test and Research Center independently developed ABLT-1 automatic control rolling bearing fatigue life enhancement on the basis of foreign advanced life testing machines. The testing machine opened up new markets and new prospects for the domestic life testing machine, and it had reached the international advanced level at that time. With the birth of the ABLT-1 rolling bearing fatigue life intensified testing machine, the development of domestic bearing life testing machines has sprung up, but most of them are derived or improved on the basis of ABLT-1. Figure 1 ABLT-1A type rolling bearing fatigue life and reliability enhancement testing machine As bearing manufacturers and users pay more attention to their life test and reliability engineering, the types of bearings to be tested are gradually diversified, and the ABLT-1 testing machine can no longer meet the bearings with many different parameters. On the basis of continuous digestion and absorption and improvement of the ABLT-1 bearing life testing machine, the Hangzhou Bearing Test and Research Center has independently designed and developed Type 2, Type 3, Type 4, Type 5, Type 6, Type 7, Type 8, and Type 9 Type and other ABLT series of rolling bearing fatigue life and reliability enhancement test machines, with completely independent property rights of new bearing test technology and methods. The ABLT series fatigue life and reliability enhancement testing machine has absorbed the advantages of the previous testing technology, and further strengthened and improved the level of automation control. At present, many companies are working hard to approach the general direction of “Industry 4.0, Intelligent Manufacturing, Internet +”, and life testing machines are gradually developing and updating toward the trend of intelligence and automation. For different industries or different companies, there are different market demand patterns, different product production and processing technologies, and different demand priorities. The demand for individualization and intelligence is increasing. The rolling bearing life and reliability testing machine has also been innovated accordingly. The most core innovation is the drive system and the loading system, because the most important human control factors affecting the bearing life test are the test speed and load. The addition of servo motors and electro-hydraulic servo valves makes bearing life tests easier to meet customer’s personalized and automated requirements. The drive device adopts a servo motor, which can convert the voltage signal into the motor speed to drive the control object. In the automatic control system, it is used as an actuator and has the characteristics of small electromechanical time constant, high linearity, and starting voltage. The loading system uses an electro-hydraulic servo valve. After receiving the electrical analog signal, it can output the modulated flow and pressure accordingly, and then convert the low-power weak electrical input signal into high-power hydraulic energy (flow and pressure) output, load loading Faster and more precise. Through personalized design, it has basically been able to meet the needs of most rolling bearing fatigue life enhancement tests. The development of domestic bearing life testing machines has gone through more than 40 years, and has basically possessed skilled research and development technology, rich test experience, and accumulated a large amount of test data. Although certain research results have been achieved, there are too many and too complicated factors affecting bearing life. The data processing of bearing life experiment still needs to be further improved. It is now time to establish a database on bearing fatigue mechanism research, failure factor analysis, material smelting and processing technology, test data analysis and other related technologies, and propose as soon as possible the recommended value of each correction factor for domestic bearing life calculation, so that it can be applied to various types of bearings that are rapidly innovated. Service information. 1.2 Research on prediction of bearing life and reliability test During the operation of the bearing during the whole life cycle, it is likely to be affected by factors such as high temperature, poor lubrication, improper assembly, foreign matter intrusion, etc., resulting in damage to the bearing and failure of the bearing. Because the bearing life is very discrete, a batch of bearings with the same structure, the same material, the same heat treatment, and the same processing method under the same working conditions have a difference of dozens of times or more between the maximum life and the minimum life. Traditional mathematical statistics show the bearing life. The test data approximately conforms to the Weibull distribution or the lognormal distribution, but it is still difficult to predict in actual working conditions. Therefore, the effective processing of bearing life test data is extremely important, and domestic and foreign research institutions are also actively carrying out relevant research on bearing life test data. Saxena et al. used the power spectral density parameter as the performance degradation index of rolling bearing to predict the remaining service life of the bearing. The density parameter can diagnose the location and extent of the fault. Xiao Ting et al. used kurtosis and multi-domain feature sets as trend prediction indicators, which not only effectively reflect the running state of the bearing, but also predict the performance degradation trend of the bearing. Banjevic et al. used the proportional hazard model to predict the reliability function and remaining life of the equipment, and used the covariate at a certain moment as a benchmark to predict the remaining life. Based on previous studies, Kacpnynski proposed a prediction model combining monitoring data with material parameters, and used this model to predict the life of rolling bearings. Kimotho et al. proposed a hybrid differential evolution particle swarm optimization (DE-PSO) optimization algorithm to optimize the kernel function and penalty parameter prediction method of support vector machine, which improved the classification accuracy of support vector machine and the accuracy of remaining life prediction, and adopted NASA standard bearing failure data was verified. Orsagh et al. used the Yu-Harris model to predict the initial time of the rolling bearing fatigue spalling failure, and used the Kotzalas-Harris model to predict the failure time of the rolling bearing. Panigrahi proposed a diffusion particle swarm optimization algorithm (DPSO) to solve the problem of maximum likelihood function estimation in the research of bearing performance degradation, and achieved good prediction results. The current life model based on statistics still occupies a leading position in bearing life prediction. However, experiments and engineering applications have found that the life calculated by the statistical life model is usually conservative and the life span of the bearing is large. Therefore, how to study the mechanism of bearing performance degradation to improve Bearing life models are a major issue. The life prediction method based on condition monitoring has become a hot area of bearing life prediction research with the development of new information technology and artificial intelligence. With the help of big data, artificial intelligence information and other technologies, dynamic signals reflecting bearing service performance can be obtained, signal characteristic parameters characterizing bearing performance degradation, and the mapping relationship between signal characteristic parameters and remaining life can be established, so as to realize the prediction of remaining life. However, there is a lack of appropriate characteristic parameters to measure the evolutionary law of bearing performance gradual decline during operation. Compared with traditional life prediction models, artificial intelligence methods such as neural networks are not clear enough in physical meaning, and the parameters have greater influence factors. How to conduct in-depth research on the difficult points is very important to the bearing life prediction technology. 2 Research on Big Data Health Monitoring of Rolling Bearings Bearing health monitoring is based on the existing known data, and predicts the changes in the future running status trend of the bearing within a certain period of time, in order to accurately and quickly obtain fault development information. Carrying out condition monitoring and health monitoring of bearings can grasp the law of bearing degradation process, prevent larger failures from occurring, and prevent problems before they occur. Caesarendra et al. used correlation vector machine regression algorithm and logistic regression combination method to evaluate the degree of bearing degradation and predicted failure time. Yu et al. used the local protection projection method to extract the characteristics of bearing operation, and used Gaussian mixture model and statistical indicators to evaluate the health of the bearing. The study showed that the effect of the feature extraction was significantly better than the component analysis method. Li Xiuwen et al. used frequency domain morphological filtering to reduce the noise of low-speed rolling bearing acoustic emission signals, and compared simulation with actual bearing signals. The results showed that this method has good results. Rojas et al. proposed a SVM-based fault diagnosis method for rolling bearings. The time domain characteristics of rolling bearing vibration signals were identified by SVM. Jeong et al. used the methods of discrete wavelet transform and spectral kurtosis analysis to obtain the characteristic frequencies of the faults in each part of the rolling bearing, and completed the inner ring-outer ring, inner ring-rolling element, outer ring-rolling body, inner ring-outer Diagnosis of ring-rolling element compound fault form. Some of the indicators for bearing running status monitoring can be achieved by collecting physical parameters, such as temperature, vibration, noise amplitude, etc., and some require researchers to advance the data through signal processing methods, such as the degree of temperature change. , Vibration intensity, sound pressure intensity, etc. Through the characterization of these indicators, the health status of the bearing can be evaluated, the prediction and early warning of the running status of the bearing can be carried out, and the engineering staff can be guided to take corresponding protective measures to avoid the deterioration of the health status of the bearing. However, for bearing health monitoring and analysis at this stage, a unified platform has not been established to integrate common features in bearing failures, and it is still not very good in the mining of failure causes, high-response prediction methods, and collaborative management of multiple bearings. Complete. 3 Conclusion Although scholars at home and abroad have achieved some noteworthy research results in bearing health monitoring and life prediction, most of the research still stays on the routine fault diagnosis, and the research on bearings based on big data analysis and intelligent algorithm evaluation is more important. few. Bearing failure prediction and health management based on big data analysis can realize the independent guarantee of bearing equipment. With the development of technologies such as the Internet of Things and artificial intelligence, key technologies such as operating data collection, degradation and failure characteristic information extraction, and health assessment for bearing equipment will become the development trend of bearing health monitoring and life prediction.
Post time: Jul-23-2021