| 基于无限隐Markov模型的旋转机械故障诊断方法研究 |
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| 引用本文： | 李志农,柳宝,侯娟. 基于无限隐Markov模型的旋转机械故障诊断方法研究[J]. 仪器仪表学报, 2016, 37(10): 2185-2192 |
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| 作者姓名： | 李志农 柳宝 侯娟 |
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| 作者单位： | 南昌航空大学 无损检测技术教育部重点实验室南昌330063,南昌航空大学 无损检测技术教育部重点实验室南昌330063,南昌航空大学 无损检测技术教育部重点实验室南昌330063 |
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| 基金项目： | 国家自然科学基金(51675258，51265039，51261024)、机械传动国家重点实验室开放基金( SKLMT-KFKT-201514)、广东省数字信号与图像处理技术重点实验室(2014GDDSIPL-01)项目资助 |
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| 摘 要： | 针对传统隐Markov模型(HMM)在机械故障诊断中存在的不足,即HMM过学习或溢出问题以及隐状态数需要事先假定,提出了基于无限隐马尔可夫模型(i HMM)的机械故障诊断方法。在提出的方法中,以谱峭度为特征提取,i HMM为识别器,并以最大似然估计来确定设备运转中出现的故障类型。同时,将提出的方法与传统的HMM故障识别方法进行了对比分析。实验结果表明,提出的方法是有效的,得到了非常满意的识别效果。提出的方法能够有效避免了HMM在建模初期遗留下的不足,可以自适应确定模型中隐藏状态数和模型数学结构,因此,提出的方法明显优于HMM故障识别方法。
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| 关 键 词： | 无限隐马尔可夫模型；故障诊断；谱峭度；最大似然估计；模式识别 |
| 收稿时间： | 2016-04-19 |
| 修稿时间： | 2016-08-03 |
Research on rotating machinery fault diagnosis method based on infinite hidden Markov model |
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| Li Zhinong,Liu Bao and Hou Juan. Research on rotating machinery fault diagnosis method based on infinite hidden Markov model[J]. Chinese Journal of Scientific Instrument, 2016, 37(10): 2185-2192 |
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| Authors: | Li Zhinong Liu Bao Hou Juan |
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| Affiliation: | Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 360063, China,Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 360063, China and Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 360063, China |
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| Abstract: | Aimingat the deficiency of traditional HMM fault recognition model in machinery fault diagnosis, i.e. over learning or overflow problems and requiring to assume the hidden states in advance, a new machinery fault diagnosismethod based on infinite Hidden Markov Model (iHMM) is proposed. In the proposed method, the spectral kurtosis is used as the fault feature extraction, the iHMM as theidentifier, and the maximum likelihood estimation is used to determinethe mechanical fault type occurred in the equipment operation. At the same time, the proposed method and traditional HMM fault identification method are compared and analyzed.The experiment result shows that the proposedrecognition method has very satisfactory recognition effect. The proposed method can effectively avoid the deficiency of the HMM method in the initial modeling stage, can adaptively determine the number of hidden states in the model and the mathematical structure of the model. Therefore,the proposed method is obviously superior to the traditional HMM fault recognition method. |
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| Keywords: | infinite hidden markov model (iHMM) fault diagnosis spectral kurtosis maximum likelihood estimation pattern recognition |
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