| A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes |
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| 作者姓名： | 徐国平 田蔚风 金志华 钱莉 |
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| 作者单位： | Dept.of Information Measurement Technology and Instrument Shanghai Jiaotong Univ.，Dept.of Information Measurement Technology and Instrument，Shanghai Jiaotong Univ.，Dept.of Information Measurement Technology and Instrument，Shanghai Jiaotong Univ.，Research Inst.of Micro-Nano Science and Technology，Shanghai Jiaotong Univ.，Shanghai 200030，China，Shanghai 200030，China，Shanghai 200030，China，Shanghai 200030，China |
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| 摘 要： | Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.
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| 关 键 词： | 统计参数分析 矢量机械 基础功能 自动化 |
| 文章编号： | 1007-1172（2007）05-0592-05 |
| 修稿时间： | 2006-09-19 |
A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes |
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| XU Guo-ping,TIAN Wei-feng,JIN Zhi-hua,QIAN Li.A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes[J].Journal of Shanghai Jiaotong university,2007,12(5):592-596. |
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| Authors: | XU Guo-ping TIAN Wei-feng JIN Zhi-hua QIAN Li |
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| Institution: | 1. Dept. of Information Measurement Technology and Instrument, Shanghai Jiaotong Univ. , Shanghai 200030, China
2. Research Inst. of Micro-Nano Science and Technology, Shanghai Jiaotong Univ. , Shanghai 200030, China |
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| Abstract: | Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG. |
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| Keywords: | statistical parameter analysis (SPA) support vector machine (SVM) radial-basis function (RBF) neural network fault diagnosis dynamically tuned gyroscope |
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