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 presented. First, this paper analyzes the failure mechanism of DC/DC convert, selects the output ripple voltage and inductor current and output 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