Applying the Mahalanobis-Taguchi System to Vehicle Handling

The Mahalanobis-Taguchi system (MTS) is a diagnosis and forecasting method using multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and patterns that can be identified and analyzed with respect to a base or reference group. The MTS is of interest because of its reported accuracy in forecasting using small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This article presents the application of the MTS, its applicability in identifying a reduced set of useful variables in multidimensional systems, and a comparison of results with those obtained from a standard statistical approach to the problem

Data classification and forecasting using the Mahalanobis-Taguchi method

Classification and forecasting are useful concepts in the field of condition monitoring. Condition monitoring refers to the analysis and monitoring of system characteristics to understand and identify deviations from normal operating conditions. This can be performed for prediction, diagnosis, or prognosis or a combination of any these purposes. Fault identification and diagnosis are usually achieved through data classification, while forecasting methods are usually used to accomplish the prediction objective. Data gathered from monitoring systems often consists of multiple multivariate time series and is fed into a model for data analysis using various techniques. One of the data analysis techniques used is the Mahalanobis-Taguchi strategy (MTS) because of its suitability for multivariate data analysis. MTS provides a means of extracting information in a multidimensional system by integrating information from different variables into a single composite metric. MTS is used to conduct analysis on the measurement parameters and seeks a correlation with the result while also seeking to optimize the analysis by identifying variables of importance strongly correlated with a defect or fault occurrence. This research presents the application of a MTS based system for predicting faults in heavy duty vehicles and the application of MTS in a multiclass classification problem. The benefits and practicality of the methodology in industrial applications are demonstrated through the use of real world data and discussion of results. --Abstract, page iv

Integration of mahalanobis-taguchi system and activity based costing in decision making for remanufacturing

Classifying components at the end of life (EOL) into remanufacture, repair or dispose is still a major concern to automotive industries. Prior to this study, no specific approach is reported as a guide line to determine critical crankpins that justifying economical remanufacturing process. Traditional cost accounting (TCA) has been used widely by remanufacturing industries but this is not a good measure of estimating the actual manufacturing costs per unit as compared to activity based costing (ABC). However, the application of ABC method in estimating remanufactured cost is rarely reported. These issues were handled separately without a proper integration to make remanufacturing decision which frequently results into uneconomical operating cost and finally the decision becomes less accurate. The aim of this work is to develop a suitable pattern recognition method for classifying crankshaft into three different EOL groups and subsequently evaluates the critical and non-critical crankpins of the used crankshaft using Mahalanobis-Taguchi System (MTS). A remanufacturability assessment technique was developed using Microsoft Excel spreadsheet on pattern recognition and critical crankpins evaluation, and finally integrates these information into a similar spreadsheet with ABC to make decision whether the crankshaft is to be remanufactured, repaired or disposed. The developed scatter diagram was able to recognize group pattern of EOL crankshaft which later was successfully used to determine critical crankpins required for remanufacturing process. The proposed method can serve as a useful approach to the remanufacturing industries for systematically evaluate and decide EOL components for further processing. Case study on six engine models, the result shows that three engines can be securely remanufactured at above 40% profit margin while another two engines are still viable to remanufacture but with less profit margin. In contrast, only two engines can be securely remanufactured due overcharge when using TCA. This inaccuracy affects significantly the overall remanufacturing activities and revenue of the industry. In conclusion, the proposed integration on pattern recognition, parameter evaluation and costing assists the decision making process to effectively remanufacture EOL automotive components as confirmed by Head of workshop of Motor Teknologi Industri Sdn. Bhd

Forecasting using the T-method

The T-method is a technique developed by Genichi Taguchi to calculate an overall prediction based on the signal-to-noise ratio without the use of Gram-Schmidt orthogonalization. The Taguchi Methods, also known as robust design principles, is used to determine the optimal levels of control factors through planning and conducting experiments, and evaluating their results. The primary goal of Robust Design is to minimize variance in the presence of noise factors to achieve a robust process. T-Method is one of the techniques evolved from Taguchi Methods. This thesis illustrates the use of the T-method and outlines it steps using four forecasting case studies from various areas with a univariate response to illustrate the technique. The methodology used to forecast in the case study is explained and the results obtained are demonstrated. In addition, a basic comparison with the Mahalanobis-Taguchi system is provided --Abstract, page iii

Sensor data-based decision making

Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv

Modified Mahalanobis Taguchi System for Imbalance Data Classification

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA)

IMECE2006-13161 APPLYING THE MAHALANOBIS-TAGUCHI SYSTEM TO VEHICLE RIDE

ABSTRACT The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis-Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the Mahalanobis-Taguchi System and its application to identify a reduced set of useful variables in multidimensional systems. INTRODUCTION The primary objective of this research is to develop a methodology, which demonstrates the relationship between actions of a producer and its suppliers (for instance, target setting of sub-systems or components' performance attributes) and consumer satisfaction ratings. The goal is to efficiently forecast consumer satisfaction ratings (CSRs) as a function of available vehicle level performance data for vehicle ride

Characterization of bees algorithm into the Mahalanobis-Taguchi system for classification

Mahalanobis-Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. In order to improve recognition accuracy of the MTS, features that do not provide useful and beneficial information to the recognition function is removed. A matrix called Orthogonal Array (OA) to search for the useful features is utilized by MTS to accomplished the search. However, the deployment of OA as the feature selection search method is seen as ineffective. The fixed-scheme structure of the OA provides a non-heuristic search nature which leads to suboptimal solution. Therefore, it is the objective of this research to develop an algorithm utilizing Bees Algorithm (BA) to replace the OA. It will act as the alternative feature selection search strategy in order to enhance the search mechanism in a more heuristic manner. To understand the mechanism of the Bees Algorithm, the characteristics of the algorithmic nature of the algorithm is determined. Unlike other research reported in the literature, the proposed characterization framework is similar to Taguchi-sound approach because Larger the Better (LTB) type of signal-to-noise formulation is used as the algorithm’s objective function. The Smallest Position Value (SPV) discretization method is adopted by which the combinations of features are indexed in an enumeration list consisting of all possible feature combinations. The list formed a search landscape for the bee agents in exploring the potential solution. The proposed characterization framework is validated by comparing it against three different case studies, all focused on performance in terms of Signal-to-Noise Ratio gain (SNR gain), classification accuracy and computational speed against the OA. The results from the case studies showed that the characterization of the BA into the MTS framework improved the performance of the MTS particularly on the SNR gain. It recorded more than 50% improvement (on average) and nearly 4% improvement on the classification accuracy (on average) in comparison to the OA. However, the OA on average was found to be 30 times faster than the BA in terms of computational speed. Future research on improving the computational speed aspect of the BA is suggested. This study concludes that the characterization of BA into the MTS optimization methodology effectively improved the performances of the MTS, particularly with respect of the SNR gain performance and the classification accuracy when compared to the OA

Feasibility study on the implementation of mahalanobis taguchi system and time driven activity based costing in electronic industry

Electrical and electronic industry is one of Malaysia’s leading industries which covers around 24.5% in manufacturing production sector. With a continuous innovation of the Industry, inductor component gets higher demand from customer and it is good if there is a study to convince that those factors are really significant to the production as well. Meanwhile, the current costing being used is difficult to access the complete activities required for each workstation and need separate analysis to measure the un-used capacity in term of resources and cost. The objective of this work is to clarify the relationship between Mahalanobis-Taguchi system (MTS) and time driven activity-based costing (TDABC) in the electronic industry. The data collection is focused on inductor component by consiedring the historical data in 2018. MTS is used as a method to optimize various parameters while TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. There are 7 parameters considered which are condition of wire, condition of winding, condition of epoxy, condition of core, condition of lead part, condition of marking and condition of soldering. As a result, MTS is successfully developed the normal and abnormal Mahalanobis distance (MD). In February, the normal MD is 0.9998 and the abnormal is 15.6538 with 2 significant parameters with signal to noise is 0.1244. In addition, there are 3 parameters consistently influenced along 10 months such as condition of core, condition of lead part and condition of soldering and 2 parameters are not consistently influenced such as condition of epoxy and condition marking. On the other hand, the total used and un-used capacity of time are 257124.02 minutes and 5217031.43 minutes respectively while the total of used and un-used of cost are MYR6,296,493.10 and MYR6214807.07 respectively. Eventually, this work concludes that both methods are a great tool and feasible to be implemented in the electronic industry

DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE

Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems. The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different. A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics. For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance. A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior. A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation. A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form. Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data