Forecasting model for degradation path and parameter estimation based on neural network

C. Su, Y. Jiang
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引用次数: 4

Abstract

Traditional life evaluation theory established on the basis of mass failure data, the phenomena of little or naught failure put forward challenges for existed life evaluation theory. The performance degradation data provide useful information for products' reliability and gives feasible way for products' life evaluation. The limitations of existing degradation models are analyzed, a new forecasting model and parameter estimation method based on neural network is brought forward. By using back propagation neural network(BPNN), the nonlinear degradation path of product performance can be got, and the parameters can be estimated by self-adaptive neural network. An example is given out to validate the effectiveness of the method and compared with existing model.
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基于神经网络的退化路径预测模型及参数估计
传统的寿命评价理论是建立在大量失效数据基础上的,失效少或无失效现象对现有的寿命评价理论提出了挑战。性能退化数据为产品的可靠性提供了有用的信息,为产品的寿命评估提供了可行的方法。分析了现有退化模型的局限性,提出了一种新的基于神经网络的退化预测模型和参数估计方法。利用反向传播神经网络(BPNN)可以得到产品性能的非线性退化路径,并通过自适应神经网络对参数进行估计。通过算例验证了该方法的有效性,并与现有模型进行了比较。
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