基于深度学习算法的船用高频电路工作状态检测研究

Research on working state detection of marine high frequency circuit based on deep learning algorithm

为了提升船用高频开关电源的运行可靠性,提出基于深度学习算法的船用高频电路工作状态检测方法。采集船用高频电路工作状态信号,作为深度受限波尔兹曼机的输入,深度受限波尔兹曼机利用2层受限玻尔兹曼机,通过2次非线性映射,提取船用高频电路工作状态特征。设置所提取的高频电路工作状态特征,作为支持向量数据描述方法的输入,将输入样本映射至高维内积空间,判定样本是否存在于高维内积空间的最优超球体内,检测船用高频电路工作状态为正常或异常状态。实验结果表明,该方法可以精准检测船用高频电路工作状态,满足船舶高频开关电源的运行可靠性需求。

A deep learning algorithm based working state detection method for marine high frequency circuit is studied to improve the operational reliability of marine high frequency switching power supply.The working state signals of Marine high frequency circuits are collected and used as input of the depth limited Boltzmann machine.The depth limited Boltzmann machine uses the two-layer limited Boltzmann machine to extract the working state characteristics of marine high frequency circuits through two nonlinear mapping.The extracted working state characteristics of the high-frequency circuit are set as the input of the support vector data description method,which maps the input sample to the high-dimensional inner product space,determines whether the sample exists in the optimal hypersphere of the high-dimensional inner product space,and detects whether the working state of the marine high-frequency circuit is normal or abnormal.Experimental results show that the proposed method can accurately detect the working state of marine high-frequency circuit and meet the operational reliability requirements of marine high-frequency switching power supply.