An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system （MTS）, which is based on statistical technology, is proposed in this paper. Multifractal features of vibra...An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system （MTS）, which is based on statistical technology, is proposed in this paper. Multifractal features of vibration signals obtained from machine state monitoring are extracted by multifractal spectrum analysis and generalized fractal dimensions. Considering the situation of mass samples of normal mechanical running state and few fault states, the feature parameters corresponding to different mechanical running states are further optimized by a statistical method, based on which incipient faults are subsequently identified and diagnosed accurately. Experimental results proved that the method combining multifractal theory and MTS can be used for incipient fault state recognition effectively during the mechanical running process, and the accuracy of fault state identification is improved.展开更多
Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scal...Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scale.In this paper,considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS,a feature recognition and selection model of the equipment state based on the improved MTS is proposed,and two aspects of the model namely construction of the original Mahalanobis space(MS)and determination of the threshold are studied.Firstly,the original training sample space is statistically controlled by the X-bar-S control chart,and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model,so as to construct a more robust MS.Furthermore,the box plot method is used to determine the threshold of the model.And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range.Finally,the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature.The result shows that compared with the traditional model,the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.展开更多
The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operati...The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operation process generally consists of four specialised stages,namely,Mahalanobis space(MS)construction,MS validation,MS optimisation,and diagnosis and prognosis.In recent years,a significant amount of research work has been conducted on the improvement of key technologies and these four processes.Numerous applications of MTS have also been studied in various fields.There are,of course,excellent reviews of theoretical research and applied research of MTS.However,there is no systematic review that covers both theoretical and applied research comprehensively.To fill this gap,this paper reviews MTS from key technolo-gies,four processes and application research,and provides researchers with a discussion of the current situation,upcoming challenges and possible future trends.展开更多
The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper pro...The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.展开更多
基金supported by the National High Technology Research and Development Program of China (Grant No. 2008AA06Z209)CNPC Innovation Fund (Grant No. 2006-A)+1 种基金Special Items Fund of Beijing Municipal Commiss ion of EducationProgram for New Century Excellent Talents,Ministry of Education (Grant No. NCET-05-0110)
文摘An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system （MTS）, which is based on statistical technology, is proposed in this paper. Multifractal features of vibration signals obtained from machine state monitoring are extracted by multifractal spectrum analysis and generalized fractal dimensions. Considering the situation of mass samples of normal mechanical running state and few fault states, the feature parameters corresponding to different mechanical running states are further optimized by a statistical method, based on which incipient faults are subsequently identified and diagnosed accurately. Experimental results proved that the method combining multifractal theory and MTS can be used for incipient fault state recognition effectively during the mechanical running process, and the accuracy of fault state identification is improved.
基金the National Natural Science Foundation of China(No.71401016)the Shaanxi Provincial Natural Science Foundation of China(No.2019JM-495)the Fundamental Research Funds for Central Universities of Chang'an University(Nos.300102228110 and 300102228402)。
文摘Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scale.In this paper,considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS,a feature recognition and selection model of the equipment state based on the improved MTS is proposed,and two aspects of the model namely construction of the original Mahalanobis space(MS)and determination of the threshold are studied.Firstly,the original training sample space is statistically controlled by the X-bar-S control chart,and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model,so as to construct a more robust MS.Furthermore,the box plot method is used to determine the threshold of the model.And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range.Finally,the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature.The result shows that compared with the traditional model,the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.
文摘The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operation process generally consists of four specialised stages,namely,Mahalanobis space(MS)construction,MS validation,MS optimisation,and diagnosis and prognosis.In recent years,a significant amount of research work has been conducted on the improvement of key technologies and these four processes.Numerous applications of MTS have also been studied in various fields.There are,of course,excellent reviews of theoretical research and applied research of MTS.However,there is no systematic review that covers both theoretical and applied research comprehensively.To fill this gap,this paper reviews MTS from key technolo-gies,four processes and application research,and provides researchers with a discussion of the current situation,upcoming challenges and possible future trends.
基金the National Natural Science Foundation of China(No.61473144)。
文摘The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.
