Aiming at the non-stationary features of the roller bearing fault vibration signal,a roller bearing fault diagnosis method based on improved Local Mean Decomposition(LMD)and Support Vector Machine(SVM)is proposed.In t...Aiming at the non-stationary features of the roller bearing fault vibration signal,a roller bearing fault diagnosis method based on improved Local Mean Decomposition(LMD)and Support Vector Machine(SVM)is proposed.In this paper,firstly,the wavelet analysis is introduced to the signal decomposition and reconstruction;secondly,the LMD method is used to decompose the reconstruction signal obtained by the wavelet analysis into a number of Product Functions(PFs)that include main fault characteristics,thus,the initial feature vector matrixes could be formed automatically;Thirdly,by applying the Singular Value Decomposition(SVD)techniques to the initial feature vector matrixes,the singular values of the matrixes can be obtained,which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier;Finally,the recognition results can be obtained from the SVM output.The results of analysis show that the proposed method can be applied to roller bearing fault diagnosis effectively.展开更多
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines ...Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.展开更多
It is desirable to get a uniform load distribution for the pressure rollers of the mangle.In thispaper,the load uniforming principle of the axially-staggered pressure rollers and the rules for theroller design have be...It is desirable to get a uniform load distribution for the pressure rollers of the mangle.In thispaper,the load uniforming principle of the axially-staggered pressure rollers and the rules for theroller design have been analysed by the theory of bending of beams.The ratio of maximum tominimum value of the distributed load in the nip being taken as a useful index to describe the uni-formity of the load distribution,the performance of the so-called load-uniforming pressure rollerswith rubber covering,under various operation conditions,has been evaluated.As a result,it isproved that the load-uniforming pressure rollers with axially-staggered contact is a pratically use-ful means of uniformizing the load distribution,which ensures that the mangle will squeeze the ma-terial to be processed uniformly over its whole width under any technological load,and needs onlysome simple regulations.The conclusions obtained in this paper can easily be extended to the pressure rollers with anysupport conditions.展开更多
针对行星滚柱丝杠(planetary roller screw mechanism,PRSM)在实际应用中故障机理不明和故障种类少,难以有效进行故障决策这一现存问题,提出采用单分类模型——深度支持向量数据描述(deep support vector data description,deep SVDD)...针对行星滚柱丝杠(planetary roller screw mechanism,PRSM)在实际应用中故障机理不明和故障种类少,难以有效进行故障决策这一现存问题,提出采用单分类模型——深度支持向量数据描述(deep support vector data description,deep SVDD)进行故障检测,判断PRSM是否处于正常状态。首先,在PRSM试验台上采集正常状态、润滑失效和滚柱一侧断齿3种状态的振动信号;其次,对数据进行归一化并通过窗口裁剪的方式进行数据增强,以扩充样本数量;然后,通过小波包变换对信号进行分解,以初步提取数据的特征;最后,利用deep SVDD实现PRSM故障检测,同时与单分类支持向量机(one-class support vector machine,OCSVM)和支持向量数据描述(support vector data description,SVDD)方法进行对比,结果表明,deep SVDD具有更好的分类能力和较高的训练效率,较为适合实现PRSM故障检测。展开更多
基金supported by Chinese National Science Foundation Grant(No.50775068)China Postdoctoral Science Foundation funded project(No.20080430154)High-Tech Research and Development Program of China(No.2009AA04Z414)
文摘Aiming at the non-stationary features of the roller bearing fault vibration signal,a roller bearing fault diagnosis method based on improved Local Mean Decomposition(LMD)and Support Vector Machine(SVM)is proposed.In this paper,firstly,the wavelet analysis is introduced to the signal decomposition and reconstruction;secondly,the LMD method is used to decompose the reconstruction signal obtained by the wavelet analysis into a number of Product Functions(PFs)that include main fault characteristics,thus,the initial feature vector matrixes could be formed automatically;Thirdly,by applying the Singular Value Decomposition(SVD)techniques to the initial feature vector matrixes,the singular values of the matrixes can be obtained,which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier;Finally,the recognition results can be obtained from the SVM output.The results of analysis show that the proposed method can be applied to roller bearing fault diagnosis effectively.
基金supported by the Program for New Century Excellent Talents in University (NoNCET- 08-0836)the National Natural Science Foundation of China (Nos60804022, 60974050 and 61072094)+1 种基金the Fok Ying-Tung Education Foundation for Young Teachers (No121066)by the Natural Science Foundation of Jiangsu Province (No.BK2008126)
文摘Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.
文摘It is desirable to get a uniform load distribution for the pressure rollers of the mangle.In thispaper,the load uniforming principle of the axially-staggered pressure rollers and the rules for theroller design have been analysed by the theory of bending of beams.The ratio of maximum tominimum value of the distributed load in the nip being taken as a useful index to describe the uni-formity of the load distribution,the performance of the so-called load-uniforming pressure rollerswith rubber covering,under various operation conditions,has been evaluated.As a result,it isproved that the load-uniforming pressure rollers with axially-staggered contact is a pratically use-ful means of uniformizing the load distribution,which ensures that the mangle will squeeze the ma-terial to be processed uniformly over its whole width under any technological load,and needs onlysome simple regulations.The conclusions obtained in this paper can easily be extended to the pressure rollers with anysupport conditions.
文摘针对行星滚柱丝杠(planetary roller screw mechanism,PRSM)在实际应用中故障机理不明和故障种类少,难以有效进行故障决策这一现存问题,提出采用单分类模型——深度支持向量数据描述(deep support vector data description,deep SVDD)进行故障检测,判断PRSM是否处于正常状态。首先,在PRSM试验台上采集正常状态、润滑失效和滚柱一侧断齿3种状态的振动信号;其次,对数据进行归一化并通过窗口裁剪的方式进行数据增强,以扩充样本数量;然后,通过小波包变换对信号进行分解,以初步提取数据的特征;最后,利用deep SVDD实现PRSM故障检测,同时与单分类支持向量机(one-class support vector machine,OCSVM)和支持向量数据描述(support vector data description,SVDD)方法进行对比,结果表明,deep SVDD具有更好的分类能力和较高的训练效率,较为适合实现PRSM故障检测。
