As an emerging simulation technology in the field of system modeling and simulation, the equipment symbiotic simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life (RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders self-adaptive prediction of equipment RUL. Combined with parallel systems theory, the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model (SSM) is taken as the basic simulation model in the framework. Driven by the dynamic injected equipment degradation observation data, the model parameters are updated online by using expectation maximum (EM) algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter, so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy, proving the feasibility and effectiveness of symbiotic simulation method.
Equipment parallel simulation is an emerging simulation technology in recent years, and equipment remaining useful life (RUL) prediction oriented parallel simulation is an important branch of parallel simulation. An important concept in equipment parallel simulation is the model evolution driven by real-time data, including model selection and model parameter evolution. The current research on equipment RUL prediction oriented parallel simulation mainly focuses on a single continuous degradation mode, such as linear degradation and nonlinear degradation. Under this degradation condition, the model parameter evolution methods in parallel simulation can effectively predict equipment RUL. However, in practice, most of the equipment degradation processes exhibit a mixture of continuous degradation and discrete shock. So this requires adaptive selection of simulation models based on real-time degradation data. In this paper, the hybrid degradation equipment RUL prediction oriented parallel simulation considering model soft switch is studied. Firstly, under the modeling framework of the state space model (SSM), two kinds of degradation simulation models are established using the Wiener process and Poisson effect. Driven by the real-time degradation data, the model probability is calculated by using the forward interactive multiple model filtering algorithm to realize the model soft switch and data assimilation. On the basis of model soft switch, the expectation maximization algorithm is utilized to achieve model parameter evolution. Through the iteration between model soft switch and model parameter evolution, the simulation fidelity can be effectively improved and the actual equipment degradation state is continuously approached. According to the full probability theorem and the concept of first hitting time, the simulated degradation state distribution is integrated into the inverse Gaussian distribution. Then the analytical expression of the RUL probability density function is obtained to achieve RUL real-time prediction. Finally, a case study was conducted by using a bearing degradation data. The results show that the parallel simulation can effectively model the hybrid degradation process of the bearing. Compared with the single-model method that only considers the model parameter evolution, the RUL obtained by the method proposed in this paper has higher prediction accuracy and smaller uncertainty.
The latest demands for remaining useful life (RUL) prediction are online prediction, real-time prediction and adaptive prediction. This paper addresses the demands of RUL prediction and proposes a novel framework of parallel simulation based adaptive prediction for equipment RUL. In the framework, a Wiener state space model (WSSM) is developed to achieve the aim, which considers the whole historical data and monitoring noise. Driven by the online observation data, the degradation state is estimated by the Kalman filter based data assimilation and the WSSM parameters are updated by the expectation maximum algorithm. An analytical RUL distribution considering the distribution of the degradation state is obtained based on the concept of the first hitting time. A case study for GaAs laser device is provided to substantiate the superiority of the proposed method compared with the competing method of traditional Wiener process. The results show that the parallel simulation method can provide better RUL prognostic accuracy.
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