Mahalanobis-Taguchi System Research Articles

Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System

The detection of variables that contribute to the variation of a system is one of the most important considerations in the industrial manufacturing processes. This work presents the combination of Mahalanobis–Taguchi system and a hybrid binary metaheuristic based on particle swarm optimization and gravitational search algorithm (BPSOGSA) to perform an optimal feature selection in order to detect the relevant variables in a real process of foam injection in automotive industry. The proposed method is compared with other feature selection approach based in binary PSO algorithm. The experimental results revealed that BPSOGSA is faster and successfully converge selecting a smallest subset of features than BPSO. Moreover, the feature selection effect is validated through other widely used machine learning algorithms which improve their accuracy performance when they are trained with the subset of detected variables by the proposed system.

Research on High Efficiency and Optimization of Compact Wind Power Generation System applying "Mahalanobis-Taguchi-System"

Abstract: While global reinforcement of countermeasures against global warming is required, Japan depends on overseas imports for over 95% of energy consumption. From the resource production site to the domestic final consumption, it is important to continuously build a stable energy supply system. With the accident at TEPCO Fukushima nuclear power plant occurred on March 11, 2011, the expectation for renewable energy is rapidly rising. Among them, the eyes to wind power generation are changing in particular. At the end of 2012, the amount of wind power generation equipment installed worldwide corresponds to 280 nuclear power plants. National Institute of Technology Tsuruoka College introduced a fully independent power supply system by wind power as a part of human resource development project for engineers. In this research, we were able to extract the task by analyzing the power generation characteristics applying Mahalanobis-Taguchi-System (MTS), diagnosing the cause and optimizing power generation efficiency.

マハラノビス・タグチ・システムにおける高次元データ解析法の展開

マハラノビス・タグチ・システムにおける高次元データ解析法の展開

A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

In various abrasive machining processes, output quality is defined in terms of multiple critical responses and their deviations from target values. These multiple responses are often interacting and changing the process conditions for improving or controlling one response may deteriorate the quality of another. Thus, there is a need to simultaneously consider all responses and recommend a trade-off operating condition for process control and optimisation. Two important fields that consider simultaneous control and optimisation of multiple responses are the multivariate statistical process control (MSPC) and multiple response optimisation (MRO). Although various MSPC and MRO approaches have been proposed by researchers, there is limited prior research on the integration of MSPC and MRO approaches to ensure the stability and process capability. In this study, a synergic MSPC and MRO approach is proposed based on Mahalanobis-Taguchi system and nonlinear optimisation to ensure the stability and capability of the abrasive machining process.

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions.

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

統計的モデリングに基づくマハラノビス・タグチ・システム

統計的モデリングに基づくマハラノビス・タグチ・システム

Development of a Screening Method for Health Hazard Ranking and Scoring of Chemicals Using the Mahalanobis–Taguchi System

For efficient management of chemicals, it is necessary to preferentially select hazardous chemicals as being high-priority through a screening method. Over the past 20 years, chemical ranking and scoring (CRS) methods have been applied in many countries; however, these CRS methods have a few limitations. Most of the existing methods only use some of the variables to calculate the hazard of chemicals or use the most conservative score without consideration of the correlation between chemical toxicities. This evaluation could underestimate or overestimate the real health hazard of the chemicals. To overcome the limitations of these methods, we developed a new CRS method using the Mahalanobis–Taguchi System (MTS). The MTS, which conducts multivariate analysis, produced chemical rankings that took into accounts the correlation between variables related to chemical health hazards. Also, the proportion of chemicals managed by the Korea Chemicals Control Act that were given a high rating appeared to be higher when the MTS was used, compared to the existing methods. These results indicated that the new method evaluated the health hazards of chemicals more accurately, and we expect that the MTS method could be applied to a greater range of chemicals than the existing CRS methods.

Integration of Mahalanobis-Taguchi System and activity based costing for remanufacturing decision

Remanufacturing is a process that returns the second hand and used product that have theperformance similar or better than the new product. The remanufacturer cannot bepredicting the end of life (EOL) of crankshaft to remanufacture, repair or reject due to thelimited information provided by Original Equipment Manufacturer (OEM) which depend on atraditional inspection to make a decision. Subsequently, Traditional Cost Accounting (TCA)which the manufacturing overhead are driven by the production volume are being applied.The aim of this work is to develop a distinctive pattern of crankshaft and identify the criticaland non-critical parameter of crankshaft based on the Mahalanobis Taguchi System (MTS),then applied the Activity Based Costing (ABC) as a method of estimation for theremanufacturing cost of crankshaft. In methodology, the scatter diagram is construct todevelop the pattern recognition using T Method-3, Taguchi orthogonal array (OA) areapplied to identify the important parameter using T Method and lastly, the ABC system isanalysed the costing of critical parameters. Quality inspection is the most important in qualityinspection to reduce the number of parameter of each engine model and to compare whichcosting system can produce the cheapest price. Therefore, it is also to prove the combinationof applying MTS method with ABC system will make the profitability and manufacturing costare more accurate.

Increasing T-Method Accuracy Through Application of Robust M-Estimatior

Mahalanobis Taguchi System is an analytical tool involving classification, clustering as well as prediction techniques. T-Method which is part of it is a multivariate analysis technique designed mainly for prediction and optimization purposes. The good things about T-Method is that prediction is always possible even with limited sample size. In applying T-Method, the analyst is advised to clearly understand the trend and states of the data population since this method is good in dealing with limited sample size data but for higher samples or extremely high samples data it might have more things to ponder. T-Method is not being mentioned robust to the effect of outliers within it, so dealing with high sample data will put the prediction accuracy at risk. By incorporating outliers in overall data analysis, it may contribute to a non-normality state beside the entire classical methods breakdown. Considering the risk towards lower prediction accuracy, it is important to consider the risk of lower accuracy for the individual estimates so that the overall prediction accuracy will be increased. Dealing with that intention, there exist several robust parameters estimates such as M-estimator, that able to give good results even with the data contain or may not contain outliers in it. Generalized inverse regression estimator (GIR) also been used in this research as well as Ordinary Lease Square Method (OLS) as part of comparison study. Embedding these methods into T-Method individual estimates conditionally helps in enhancing the accuracy of the T-Method while analyzing the robustness of T-method itself. However, from the 3 main case studies been used within this analysis, it shows that T-Method contributed to a better and acceptable performance with error percentages range 2.5% ~ 22.8% between all cases compared to other methods. M-estimator is proved to be sensitive with data consist of leverage point in x-axis as well as data with limited sample size. Referring to these 3 case studies only, it can be concluded that robust M-estimator is not feasible to be applied into T-Method as of now. Further enhance analysis is needed to encounter issues such as Airfoil noise case study data which T -method contributed to highest error% prediction. Hence further analysis need to be done for better result review.

Anomaly detection in high-dimensional data with the Mahalanobis–Taguchi system

The Mahalanobis–Taguchi (MT) system is a typical Taguchi method and plays an important role in several fields. This study aims at improving the statistical procedure employed for anomaly detection in high-dimensional data with the MT system. The proposed study focuses on estimating the eigenvalues and eigenvectors of the covariance matrix and introduces an estimation procedure based on sparse principal component analysis (SPCA) in the MT system. By incorporating SPCA, eigenvalues and eigenvectors can be accurately estimated for high-dimensional data. In addition, the interpretation of the principal components can become simplified with decreasing number of nonzero elements in the estimated eigenvectors. Numerical experiments have confirmed that the proposed procedure is beneficial for both anomaly detection performance and investigating the cause of anomalies in high-dimensional data. Furthermore, a limitation of the proposed study is its emphasis on improving anomaly detection procedures founded on the first principal component and its residual component. However, the scope of such an anomaly detection procedure can be easily expanded for further improvement.

Integration of Mahalanobis-Taguchi system and traditional cost accounting for remanufacturing crankshaft

Remanufacturing is a sustainability strategic planning which transforming the end of life product to as new performance with their warranty is same or better than the original product. In order to quantify the advantages of this strategy, all the processes must implement the optimization to reach the ultimate goal and reduce the waste generated. The aim of this work is to evaluate the criticality of parameters on the end of life crankshaft based on Taguchi’s orthogonal array. Then, estimate the cost using traditional cost accounting by considering the critical parameters. By implementing the optimization, the remanufacturer obviously produced lower cost and waste during production with higher potential to gain the profit. Mahalanobis-Taguchi System was proven as a powerful method of optimization that revealed the criticality of parameters. When subjected the method to the MAN engine model, there was 5 out of 6 crankpins were critical which need for grinding process while no changes happened to the Caterpillar engine model. Meanwhile, the cost per unit for MAN engine model was changed from MYR1401.29 to RM1251.29 while for Caterpillar engine model have no changes due to the no changes on criticality of parameters consideration. Therefore, by integrating the optimization and costing through remanufacturing process, a better decision can be achieved after observing the potential profit will be gained. The significant of output demonstrated through promoting sustainability by reducing re-melting process of damaged parts to ensure consistent benefit of return cores.

Costing improvement of remanufacturing crankshaft by integrating Mahalanobis-Taguchi System and Activity based Costing

Integration between quality and costing system is very crucial in order to achieve an accurate product cost and profit. Current practice by most of remanufacturers, there are still lacking on optimization during the remanufacturing process which contributed to incorrect variables consideration to the costing system. Meanwhile, traditional costing accounting being practice has distortion in the cost unit which lead to inaccurate cost of product. The aim of this work is to identify the critical and non-critical variables during remanufacturing process using Mahalanobis-Taguchi System and simultaneously estimate the cost using Activity Based Costing method. The orthogonal array was applied to indicate the contribution of variables in the factorial effect graph and the critical variables were considered with overhead costs that are actually demanding the activities. This work improved the quality inspection together with costing system to produce an accurate profitability information. As a result, the cost per unit of remanufactured crankshaft of MAN engine model with 5 critical crankpins is MYR609.50 while Detroit engine model with 4 critical crankpins is MYR1254.80. The significant of output demonstrated through promoting green by reducing re-melting process of damaged parts to ensure consistent benefit of return cores.

Mahalanobis-Taguchi system: state of the art

PurposeThe purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.Design/methodology/approachThe Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making.FindingsIts scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses.Originality/valueThis paper is a summary of the main applications, contributions and changes to MTS.

A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

T-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it’s only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier’s effect.

A Road Quality Detection Method Based on the Mahalanobis-Taguchi System

As an extremely complicated task, road detection is of vital importance for the traveling comfort and driving safety. While high-end automobiles are already equipped with road detection function, most mid-range cars can only detect and evaluate road conditions leveraging remodeled or additional hardware devices built on vehicles, thereby constraining the road quality detection. With the growing popularity of smartphones, detections based on built-in sensors emerge. Most detections on built-in sensors, nevertheless, are on the basis of Euclidean distance, thus neglecting the correlation between characteristics in road quality, i.e., the acceleration sensor and gyroscope have obvious fluctuations when the vehicle passes through the larger pothole, and there is a connection between them. In this paper, we propose a novel road detection approach based on Mahalanobis–Taguchi system (MTS), leveraging smartphones for data collection and involving the correlation between characteristics. We develop an application to collect and process the data, and then classify road quality conditions. The experimental test was carried out on city roads in Xi’an, Shaanxi. Experiment results reveal that the road surface conditions, including manhole cover, pothole, and speed bump, can be well differentiated with the method based on MTS. To a certain extent, the strategy of marking road conditions to the navigation map can effectively improve not only driving experience and traveling comfort but also driving safety, thereby providing more supports for the maintenance units.

Assessment of Health Insurance Claims by Mahalanobis-Taguchi System

Assessment of Health Insurance Claims by Mahalanobis-Taguchi System

MAHALANOBIS-TAGUCHI SYSTEM FOR PATTERN RECOGNITION: A BRIEF REVIEW

Mahalanobis-Taguchi system (MTS) is a data mining method that employs Mahalanobis distance (MD) and Taguchi robust engineering philosophy, in order to explore and exploit data in a multidimensional system. Furthermore, MD calculation provides a measurement scale that is used to discriminate and interpret the relationships among data samples (abnormal vs normal). Another function of MD calculation is that it is an approach of measuring the level of dissimilarity between them (severity). One unique feature of MTS lies on its precision in assessing the variability among all levels of samples (noise). Besides, MTS possesses the ability to evaluate the significant and insignificant factors that contribute to the performance of system (optimize). This evaluation is performed through a simplistic yet effective technique (orthogonal array and signal to noise ratio). Moreover, MTS receives a tremendous amount of appreciations by various industries due to its ability to perform various functions in diverse industrial applications (among others) of optimization, inspection, diagnostics, monitoring, estimation, prediction, classification and discrimination. Despite the appreciations received by this method, some criticize the reliability of MTS on its effectiveness, while some contribute ingenious ideas (hybrid systems, intelligent search algorithms, novel mathematical computation methods, etc.) for a further improvement of this method’s implementation. Therefore, this paper provides a general review on MTS research trends and studies conducted on this method outside Japan, in order to identify future research prospects.

Optimal variable screening in automobile motor-head machining process using metaheuristic approaches in the Mahalanobis-Taguchi System

The Mahalanobis-Taguchi System is a data analytical method for the diagnosis and/or pattern recognition with multivariate data and it is useful for quantitative decision making where the Mahalanobis distance plays a key role. Over time, MTS has received wide acceptance in the scientific community as well as in practical industry and it has been applied to different problems where variable screening of the original set of attributes is essential. In this paper, MTS is applied to an automobile motor-head machining process and the corresponding mathematical model for dimensional reduction is solved using metaherustic algorithms from swarm intelligence optimization.

Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system

ABSTRACTSFor the timely identification of the potential faults of a rolling bearing and to observe its health condition intuitively and accurately, a novel fault diagnosis and health assessment model for a rolling bearing based on the ensemble empirical mode decomposition (EEMD) method and the adjustment Mahalanobis–Taguchi system (AMTS) method is proposed. The specific steps are as follows: First, the vibration signal of a rolling bearing is decomposed by EEMD, and the extracted features are used as the input vectors of AMTS. Then, the AMTS method, which is designed to overcome the shortcomings of the traditional Mahalanobis–Taguchi system and to extract the key features, is proposed for fault diagnosis. Finally, a type of HI concept is proposed according to the results of the fault diagnosis to accomplish the health assessment of a bearing in its life cycle. To validate the superiority of the developed method proposed approach, it is compared with other recent method and proposed methodology is successfully validated on a vibration data-set acquired from seeded defects and from an accelerated life test. The results show that this method represents the actual situation well and is able to accurately and effectively identify the fault type.

Fault detection and optimal feature selection in automobile motor-head machining process

This work presents a comparison among four methodologies for fault detection and feature selection applied to dimensional quality control in an automobile motor-head machining process. Three of them are based on the Mahalanobis Taguchi System (MTS) while the fourth one uses Support Vector Data Description (SVDD) one-class classification in order to build an hypersphere with the minimum volume with an enclosed boundary containing almost all target objects. Moreover, Gompertz binary particle swarm optimization (GBPSO) algorithm is applied to optimize kernel hyperparameters in SVDD and simultaneously solve the feature selection problem.