基于CBM电子设备故障预测技术研究

为解决电子设备结构复杂，故障信息不足，故障预测困难，并且现有方法不能直接对电子设备进行状态预测等问题，本文提出了基于状态维修（CBM）的最小二乘支持向量机(LSSVM)和隐马尔科夫模型(HMM)组合故障预测方法。首先采取灵敏度分析法确定电路中要可能发生变化的元件，通过改变元件参数来设置电路的不同退化状态；其次建立组合故障预测模型；最后对该电路进行状态预测。结果表明，本文提出的方法能够直接预测电路的不同状态，进而实现直接预测电子设备的故障状态，预测精度可以达到93.3%。

Electronic Equipment Failure Prediction Technology based CBM

In order to solve the complex electronic equipment and hard to predict the fault, and existing methods cannot predict the state of the electronic equipment and other issues directly , we propose least square support vector machine and hidden Markov model portfolio fault prediction method. First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.