| 自适应小波降噪的泵机组故障诊断 |
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| 引用本文： | 武建军,邓松圣,周爱华,秦瑞胜. 自适应小波降噪的泵机组故障诊断[J]. 化工自动化及仪表, 2010, 37(4): 36-38 |
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| 作者姓名： | 武建军 邓松圣 周爱华 秦瑞胜 |
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| 作者单位： | 解放军后勤工程学院,军事供油工程系,重庆,401331;重庆工业自动化仪表研究所,重庆,400001;新疆军区联勤部,军需物资油料处,乌鲁木齐,830042 |
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| 基金项目： | 解放军后勤工程学院博士生创新基金,重庆市自然科学基金 |
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| 摘 要： | 泵机组故障诊断的难点在于信号特征向量的提取,而故障特征往往淹没在复杂的噪音中。本文利用自适应小波函数对采集到的振动信号进行降噪,滤掉了无关的噪声信息,根据振动能量的分布,对降噪过的信号进行四层小波包分解,提取出的特征向量分布明显。最后将分类特征向量输入神经网络进行训练,测试的结果证明,该方法识别精度高、速度快,具有良好的应用前景。
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| 关 键 词： | 泵机组 自适应小波 降噪 故障诊断 神经网络 |
Pump Fault Diagnosis Based on Self-adaptive Wavelet Denoise |
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| WU Jian-jun,DENG Song-sheng,ZHOU Ai-hua,QIN Rui-sheng. Pump Fault Diagnosis Based on Self-adaptive Wavelet Denoise[J]. Control and Instruments In Chemical Industry, 2010, 37(4): 36-38 |
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| Authors: | WU Jian-jun DENG Song-sheng ZHOU Ai-hua QIN Rui-sheng |
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| Affiliation: | 1.The Logistics Engineering University of PLA,Chongqing 401331,China;2.Industrial Automation Meter Institute of Chongqing,Chongqing 400001,China;3.Military Supply Materials and Oil Combined Services Force of Xinjiang Military Region,Wulumuqi 830042,China) |
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| Abstract: | The adaptive wavelet function was used to denoise the collected vibrate signal and filter those unrelated noises,then based on the signal energy distribution,the denoised signal was decomposed to four layers with wavelet package and made the characteristic vector distribution more obvious.The classified characteristic vector was brought to BP neural network for training.The testing result proves this method＇s high accuracy in recognition and speed,and good application prospect. |
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| Keywords: | pump adaptive wavelet denoise fault diagnosis neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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