基于隐马尔科夫模型的DC/DC变换器故障检测

Research on the Failure Detection for DC/DC Converter Based on HMM

将语音信号处理领域的隐马尔科夫模型HMM引入DC/DC变换器故障检测。在简要介绍HMM及其优点的基础上,提出了一种基于HMM的DC/DC变换器故障检测方法。首先分析开关电源的失效机理,选择输出电压、电感电流作为特征参数;然后对每个状态的观察样本序列训练并建立HMM模型;最后以典型boost电路模型进行了仿真实验。实验结果表明该方法能可靠识别内部故障,效果明显,并且所需样本少,训练速度快。

Hidden Markov model （HMM）, which is initially employed in the field of speech signal processing, is introduced into DC/DC converter failure detection. HMM is depicted briefly, and a new method to detect convert failure based on HMM is present- ed. First, this paper analyzes the failure mechanism of DC/DC convert, selects the output ripple voltage and inductor current and out- put power as characteristic vectors. Then, it models for every state utilizing HMM, Extensive experiments have been conducted on a typical boost circuit model. The calculation result shows that this method is able to detect failure correctly.