| 基于DBN的故障特征提取及诊断方法研究 |
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| 引用本文： | 赵光权,葛强强,刘小勇,彭喜元.基于DBN的故障特征提取及诊断方法研究[J].仪器仪表学报,2016,37(9):1946-1953. |
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| 作者姓名： | 赵光权 葛强强 刘小勇 彭喜元 |
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| 作者单位： | 哈尔滨工业大学自动化测试与控制系哈尔滨150080,哈尔滨工业大学自动化测试与控制系哈尔滨150080,哈尔滨工业大学自动化测试与控制系哈尔滨150080,哈尔滨工业大学自动化测试与控制系哈尔滨150080 |
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| 基金项目： | 国家自然科学基金（61571160）项目资助 |
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| 摘 要： | 随着装备日趋复杂化,依靠专家经验或信号处理技术人工提取和选择故障特征变得越来越困难。此外,以BP神经网络、SVM为代表的浅层模型难以表征被测信号与装备健康状况之间复杂的映射关系,且面临维数灾难等问题。结合深度置信网络(DBN)在提取特征和处理高维、非线性数据等方面的优势,提出一种基于深度置信网络的故障特征提取及诊断方法。该方法通过深度学习利用原始时域信号训练深度置信网络并完成智能诊断,其优势在于能够摆脱对大量信号处理技术与诊断经验的依赖,完成故障特征的自适应提取与健康状况的智能诊断,该方法对时域信号没有周期性要求,具有较强的通用性和适应性。在仿真数据集和轴承数据集上进行了故障特征提取和诊断实验,实验结果表明:本文提出的方法能够有效地从原始信号中进行多种工况、多种故障位置和多种故障程度的故障特征提取和诊断,并且具有较高的故障识别精度。
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| 关 键 词： | 深度置信网络 特征提取 故障诊断 原始数据 |
Fault feature extraction and diagnosis method based on deep belief network |
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| Zhao Guangquan,Ge Qiangqiang,Liu Xiaoyong and Peng Xiyuan.Fault feature extraction and diagnosis method based on deep belief network[J].Chinese Journal of Scientific Instrument,2016,37(9):1946-1953. |
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| Authors: | Zhao Guangquan Ge Qiangqiang Liu Xiaoyong and Peng Xiyuan |
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| Affiliation: | Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China,Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China,Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China and Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China |
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| Abstract: | As the equipment is increasingly complicated, it is much more challenging to artificially extract and select fault features using expertise and signal processing technology. In addition, traditional shallow architectures, i.e., BP neural network, SVM, are not capable enough at learning the complex nonlinear relationships between equipment health status and its represented signals. Deep belief network (DBN) has unique advantages in feature extraction, high-dimensional and nonlinear data processing. Hence, a novel fault feature extraction and diagnosis method are proposed based on deep belief network. Deep neural network can be directly trained using original time domain signal and utilized for smart fault diagnosis. This proposed method can adaptively extract the fault features and automatically identify machinery health conditions, overcoming the dependence on massive signal processing technologies and expertise. Moreover, periodic time domain signals are not required, which makes this method with high applicability and generality. The effectiveness of the proposed method is validated using datasets from simulations and bearings. The diagnostic results prove that the proposed method is able to conduct fault feature extraction and diagnosis effectively under various operating conditions, fault locations and levels from the raw signals to obtain superior diagnosis accuracy. |
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| Keywords: | deep belief network feature extraction fault diagnosis raw data |
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