A New Model for Strategy Formulation Using Mahalanobis-Taguchi System and Clustering Algorithm

The devastating complexity of decision making in severe dynamic competitive environment of the universe, has forced the wise managers to have relevant strategic plans for their firms. In this paper, a new approach by utilizing Mahalanobis-Taguchi System (MTS) and clustering algorithm in formulating the strategy has been proposed. In this approach, first by performing environmental analysis all internal and external factors affecting organization will be listed. Then the long range goals will be identified by top managers. By applying MTS the main set of factors affecting goals will come out. By identifying main factors, the goal-factor matrix will be formed. At this stage, by using clustering algorithm the proper clusters containing goals and factors influencing them will be constructed. Finally, from the created clusters the appropriate strategies would be generated. The advantage of applying this method is its accuracy and ease of applications in the environment with plenty of goals and numerous factors with interactions among them.

Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance,identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities.Existing studies on identifying critical components of complex products are mainly from two aspects i.e.,topological properties and functional properties,respectively.In this paper,we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values.Considering the mutual correlation between attributes,a combination of AHP(Analytic Hierarchy Process)and Improved Mahalanobis-Taguchi System(MTS)is used to determine the λ-Shapley fuzzy measures for attributes.Then,the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)method is proposed for calculating the closeness degrees of components to generate their ranking order.Finally,a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method.This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes,which provides the implementation of protection measures for the system reliability of complex products.

Feature Selection Optimization for Mahalanobis-Taguchi System Using Chaos Quantum-Behavior Particle Swarm

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.

Mahalanobis-Taguchi system: a systematic review from theory to application

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.

Feature Recognition and Selection Method of the Equipment State Based on Improved Mahalanobis-Taguchi System

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.

Incipient mechanical fault detection based on multifractal and MTS methods

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.

机械设备状态的经验模式分解统计识别方法

采用经验模式分解与基于统计的Mahalanobis-Taguchi system相结合的方法进行机械设备状态识别。利用经验模式分解自适应提取设备状态监测信号的特征,针对机械设备实际运行中存在的大量正常样本和少量异常状态样本的情况,运用统计方法实现对机械设备状态的识别诊断。实验表明,运用经验模式分解与Mahalanobis-Taguchi system方法能有效地识别设备运行状态,提高状态识别的准确性。