Hybrid Degradation Equipment Remaining Useful Life Prediction Oriented Parallel Simulation considering Model Soft Switch

Abstract EN

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.