Abstract
In industrial applications, the vibration and temperature measurements of rolling element bearings are known as two popular condition monitoring methods. The previously published method for remaining useful life (RUL) prediction has been limited to using the vibration signal. However, a single signal source cannot fully reflect the degradation trend of bearings, influencing the RUL prediction precision. In this paper, a novel general log-linear Weibull (GLL-Weibull) model is constructed by considering vibration and temperature condition monitoring signals to estimate the model parameters. During the feature extraction stage, the relative root mean square (RRMS) is derived from the monitored vibration signal, and the relative temperature trend value is extracted from the monitored temperature signal to eliminate individual differences in bearings and random signal fluctuations. Then, a fuzzy operator is introduced to describe the degree of an “overheated bearing” and “excessive bearing vibrations.” During the RUL prediction stage, both the extracted vibration and temperature characteristics are used to create the GLL-Weibull model. The best parameters are attained by employing the maximum likelihood estimation approach. The algorithm performance is checked with criteria like the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The effectiveness and superiority of the presented approach are validated by two real-life prognosis cases. According to the experimental results, the presented approach provides superior prediction precision and lower computational cost than other approaches for bearings under constant or variable operating conditions.
Highlights
Rolling element bearings are known as important parts in various machines, and bearing failure can lead to considerable losses in production and human casualties. erefore, a higher reliability and readiness state for bearings is urgently required, especially for complex and high-maintenance-cost machines such as wind turbines [1]
In light of the above problems, we propose a novel GLLWeibull model for the RUL prediction of bearings that considers both vibration and temperature characteristics by taking full advantage of the high sensitivity of vibration characteristics and the powerful anti-interference capability of temperature characteristics
In other words, when external covariates are uncertain or not easy to identify analytically and experimentally, the utilization of various measurable parameters can guarantee the reduction of various uncertainties that originate from environmental conditions, loading levels, and measurement equipment. erefore, this paper introduces the internal covariates into the Weibull distribution model to establish a general log-linear Weibull (GLL-Weibull) model, which can be called the Weibull accelerated failure model. e cumulative density function (CDF) for the GLL-Weibull model is given by the following: β
Summary
Rolling element bearings are known as important parts in various machines, and bearing failure can lead to considerable losses in production and human casualties. erefore, a higher reliability and readiness state for bearings is urgently required, especially for complex and high-maintenance-cost machines such as wind turbines [1]. In the can be described in the following equation: presented method, multiple covariates (through measured vibration signals and temperature signals) have been considered to enhance the modeling accuracy and prediction precision. The vibration and temperature trend values can reflect the evolution of the bearing from the degradation state to final failure, the sensitivity of the two signals to different types of faults is inconsistent. Based on the vibration and temperature fuzzy operators proposed in this paper, the reliability function at time t for the bearing issue with vibration and temperature coordination is described as follows: R(t, Vib, Tem) exp⎛⎝−. En, according to the allowable variation range of vibration and temperature, the two feature values are fuzzified, and the fuzzy operators are used as two internal covariates to estimate the values of unknown parameters of the GLL-Weibull model. The RUL estimation is performed based on the current time. en, the maintenance decision is made by the user
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