A combined prediction method for the life of product based on PSO with immunity algorithms

Abstract

As the rapid development of modern technology, industrial companies have to manufacture high-reliability and long-lifetime products to satisfy the changing needs of the customers. In such cases, it is difficult to assess the reliability and lifetime of the product with traditional life testing. Thus how to evaluate the lifetime and reliability index of these products is an urgent problem to be solved. For some products, it is possible to obtain the performance degradation information by the accelerated degradation testing (ADT). Therefore, to utilize the product degradation information may be an effective way to resolve this issue. However, most of lifetime prediction methods in use are mainly based on single prediction model with the disadvantage of low robustness and inaccuracy, so it cannot get the high credible lifetime of the product. This paper will adopt the prediction thought of combining two single models to establish a new prediction model, also propose a combined life prediction method based on the performance degradation data by using the particle swarm optimization (PSO) with immunity algorithms (IA). The author selects two better prediction models, which are time series model and the model of Brownian motion with drift, to predict the degradation path of the product separately. As the degradation process of the product is stochastic, and these two models are also well suited for this process. So each of them is good for long-term prediction. Then the objective function of the sum of the absolute errors is established with weight coefficients which are used to combine these prediction methods and integrate the prediction results. And the objective function is minimized with the IA-PSO, which has the characteristics of fast global optimization and uses to determine the weight coefficients for each of the prediction models. This approach can reduce the blindness of calculation and improve the accuracy of the prediction model. To verify this combined prediction method, the application of SLD's performance degradation data is demonstrated in this paper, the result shows that the combined prediction method based on the IA-PSO is effective and reasonable, and evidently has less prediction errors than each of single model. Therefore, this method plays an instructive way in engineering practice and research field.