Search Results (18)

The traditional Mahalanobis–Taguchi System (MTS) employs two main strategies for multi-class classification: the partial binary tree MTS (PBT-MTS) and the multi-tree MTS (MT-MTS). The PBT-MTS relies on a fixed binary tree structure, resulting in limited model flexibility, while the MT-MTS suffers from its dependence on pre-defined category partitioning. Both methods exhibit constraints in adaptability and classification efficiency within complex data environments. To overcome these limitations, this paper proposes an innovative Adaptive Tree-structured Mahalanobis–Taguchi System (ATMTS). Its core breakthrough lies in the ability to autonomously construct an optimal multi-layer classification tree structure. Unlike conventional PBT-MTS, which establishes a Mahalanobis Space (MS) containing only a single category per node, ATMTS dynamically generates the MS that incorporates multiple categories, substantially enhancing discriminative power and structural adaptability. Furthermore, compared to MT-MTS, which depends on prior label information, ATMTS operates without predefined categorical assumptions, uncovering discriminative relationships solely through data-driven learning. This enables broader applicability and stronger generalization capability. By introducing a unified multi-objective joint optimization model, our method simultaneously optimizes structure construction, feature selection, and threshold determination, effectively overcoming the drawbacks of conventional phased optimization approaches. Experimental results demonstrate that ATMTS outperforms PBT-MTS, MT-MTS, and other mainstream classification methods across multiple benchmark datasets, achieving significant improvements in the accuracy and robustness of multi-class classification tasks. Full article

To promote global sustainable development, this paper focuses on the identification of relative poverty. On the one hand, based on the sustainable livelihoods framework, a multi-dimensional relative poverty identification index system is constructed, covering six dimensions—human capital, financial capital, natural capital, physical capital, social capital, and livelihood environment—with a total of 18 indexes. On the other hand, addressing the limitations of traditional relative poverty identification methods in handling dynamic three-dimensional data, the multiway Mahalanobis–Taguchi system (MMTS) is proposed to identify dynamic relative poverty. This method first unfolds dynamic three-dimensional data into two-dimensional data along the sample direction through multiway statistical analysis techniques, then constructs multiway Mahalanobis distances to measure sample differences, and finally uses a Taguchi orthogonal experimental design for dimensionality reduction and noise reduction to optimize the model. Experiments using tracking survey data from 2020 to 2024 in three poverty-stricken counties in China’s Dabie Mountain area show that MMTS performs better than the Two-Way Fixed Effects (Two-way FE) model and Dynamic LSTM. MMTS shows a higher specificity, stronger noise resistance, smaller result fluctuations, better G-means performance, and a better balance between sensitivity and specificity. This proves its scientific validity and practical applicability. Full article

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies. Full article

System integration is the act of combining numerous distinct subsystems into one bigger system that allows the subsystems to work together. The integrated system removes necessity of repeating operations. The purpose of this work was to investigate the best system integration in the production environment. A few methods were tested such as conventional, Mahalanobis-Taguchi System (MTS), Activity-Based Costing (ABC) and Time-Driven Activity-Based Costing (TDABC). As a result, critical activities may now be completed more effectively while reducing expenses. The organization should define the relation between cost and quality through system integration. As a consequence of system integration, four forms of integration are described, namely, integration A (conventional-ABC), integration B (conventional-TDABC), integration C (MTS-ABC), and integration D (MTS-TDABC). Integration D is the best in the production environment when compared to others because MTS recognizes the degree of contribution for each parameter that impacts the increase or decline in the final cost. Moreover, TDABC determines capacity cost rate from the costs associated with capacity provided, and time equations with versatility to dissipate the product’s complex nature. As a result of the integration of MTS and TDABC, various degrees of parameter contributions impact the time equations and capacity cost rate to generate a lower cost of product in the production environment. Full article

Data imbalance is a common problem in classification tasks. The Mahalanobis-Taguchi system (MTS) has proven to be promising due to its lack of requirements for data distribution. The MTS is a binary classifier. However, multi-classification problems are more common in real life and the diversity of categories may further aggravate the difficulty of classifying imbalanced data. Imbalanced multi-classification has become an important research topic. To improve the performance of MTS in imbalanced multi-classification, we propose an algorithm called optimized binary tree MTS (Optimized BT-MTS). Mahalanobis space (MS) construction, feature selection, and threshold determination are incorporated in a unified classification framework, and joint optimization is carried out according to the principles of maximizing separability, signal-to-noise ratio, dimensionality reduction, and minimizing misclassification cost. Experimental results on several datasets show that the method can significantly reduce the overall misclassification cost and improve the performance of imbalanced data multi-classification. Full article

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms. Full article

Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field. Full article

The 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. The MD metric provides a measurement scale to classify classes of samples (Abnormal vs. Normal) and gives an approach to measuring the level of severity between classes. An accurate classification result depends on a threshold value or a cut-off MD value that can effectively separate the two classes. Obtaining a reliable threshold value is very crucial. An inaccurate threshold value could lead to misclassification and eventually resulting in a misjudgment decision which in some cases caused fatal consequences. Thus, this paper compares the performance of the four most common thresholding methods reported in the literature in minimizing the misclassification problem of the MTS namely the Type I–Type II error method, the Probabilistic thresholding method, Receiver Operating Characteristics (ROC) curve method and the Box–Cox transformation method. The motivation of this work is to find the most appropriate thresholding method to be utilized in MTS methodology among the four common methods. The traditional way to obtain a threshold value in MTS is using Taguchi’s Quadratic Loss Function in which the threshold is obtained by minimizing the costs associated with misclassification decision. However, obtaining cost-related data is not easy since monetary related information is considered confidential in many cases. In this study, a total of 20 different datasets were used to evaluate the classification performances of the four different thresholding methods based on classification accuracy. The result indicates that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Nevertheless, the study recommends the use of the Type I–Type II error method due to its less computational complexity as compared to the other three thresholding methods. Full article

Mahalanobis distance (MD) is a well-known metric in multivariate analysis to separate groups or populations. In the context of the Mahalanobis-Taguchi system (MTS), a set of normal observations are used to obtain their MD values and construct a reference Mahalanobis distance space, for which a suitable classification threshold can then be introduced to classify new observations as normal/abnormal. Aiming at enhancing the performance of feature screening and threshold determination in MTS, the authors have recently proposed an integrated Mahalanobis classification system (IMCS) algorithm with robust classification performance. However, the reference MD space considered in either MTS or IMCS is only based on normal samples. In this paper, an investigation on the influence of the reference MD space based on a set of (i) normal samples, (ii) abnormal samples, and (iii) both normal and abnormal samples for classification is performed. The potential of using an alternative MD space is evaluated for sorting complex metallic parts, i.e., good/bad structural quality, based on their broadband vibrational spectra. Results are discussed for a sparse and imbalanced experimental case study of complex-shaped metallic turbine blades with various damage types; a rich and balanced numerical case study of dogbone-cylinders is also considered. Full article

Product delivery via logistic systems is becoming more efficient, rapidly and continuously bringing products to the customer. The continuous operation of logistic equipment, however, can lead to mechanical stoppages due to excessive use. To avoid system failures, fatigue in each part of the system should be monitored, enabling the accurate prediction of potential stoppages and thus promoting overall system efficiency. To date, various kinds of anomaly-detection methodologies have been proposed. Among them, the Mahalanobis–Taguchi method, which simply describes the extent of a failure using the Mahalanobis distance, has been utilized to detect changes in the mechanical condition of facilities. However, the technique has not yet been applied to anomaly detection in a logistic operating system. In this paper, anomaly detection using the Mahalanobis–Taguchi method targeting the operational characteristics of a large-scale vertical transfer system is proposed and the validity of the method is discussed. The calculation used to produce proper values of the Mahalanobis distance is first developed based on simple excitation using a shaker. Mahalanobis distances under conditions of continuous operation of the target vertical transfer system are then obtained; distances for the system in an artificially damaged condition are compared to values produced under normal conditions, and any significant increase is used as an indicator of a problem. The applicability of the approach to a case involving continuous long-term operation is discussed using a simulation in which the target vertical transfer system is in continuous operation over a two-year period. Full article

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams. Full article

In decision-making with interval numbers, there are problems such as how to reduce the loss of decision information to improve decision accuracy and the difficulty of using interval numbers for sorting. On the basis of fully considering the subjective and objective weights of indexes, the grey entropy method (GEM) is improved by taking advantage of the Mahalanobis-Taguchi System (MTS) in which the orthogonal design has few tests but much obtained information, and the Mahalanobis distance can reflect the correlation between indexes. Then, the signal-to-noise ratio is integrated with the improved degree of balance and approach, and a multi-dimensional interval number decision model based on MTS and GEM is put forth. This model is applied to selecting the optimal scheme of controlling the Pankou reservoir’s water level in flood season. Compared with the decision results of other methods, the optimal scheme selected by the proposed model can achieve greater benefits within an acceptable risk range and thus better coordinate the balance between risk and benefit, which verifies the feasibility and validity of the model. Full article

Gait analysis is recognized as a method used in quantifying gait disorders and in clinical evaluations of patients. However, the current guidelines for the evaluation of post anterior cruciate ligament reconstruction (ACLR) patient outcomes are primarily based on qualitative assessments. This study aims to apply gait analyses and mathematical, index-based health management, using the Mahalanobis Taguchi System (MTS) and the Kanri Distance Calculator (KDC) to diagnose the level of the gait abnormality and to identify its contributing factors following ACLR. It is hypothesized that (1) the method is able to discriminate the gait patterns between a healthy group (HG) and patients with ACLR (PG), and (2) several contributing factors may affect ACLR patients’ rehabilitation performance. This study compared the gait of 10 subjects in the PG group with 15 subjects in the HG. The analysis was based on 11 spatiotemporal parameters. Gait data of all subjects were collected in a motion analysis laboratory. The data were then analyzed using MTS and KDC. In this study, two significant groups were recognized: the HG, who achieved results which were within the Mahalanobis space (MS), and (ii) the PG who achieved results above the MS. The results may be seen as being on-target and off-target, respectively. Based on the analysis, three variables (i.e., step width, single support time, and double support time) affected patient performance and resulted in an average mark of above 1.5 Mahalanobis distance (MD). The results indicated that by focusing on the contributing factors that affect the rehabilitation performance of the patients, it is possible to provide individualized and need-based treatment. Full article

This study aims to provide a new methodology using the Globally Harmonized System (GHS) and the Mahalanobis–Taguchi System (MTS) that can be used to assess the overall hazard of a chemical using GHS information. Previously, hazardous chemicals were designated and managed by the Chemical Management Act, but many more chemicals are now in use. Damage prediction modeling programs predict the extent of damage and proactively manage high-risk chemicals, but the lack of physical and chemical characterization information relating to chemicals has limitations that cannot be modeled. To overcome such limitations, a new method of chemical management prioritization was developed using the GHS and Mahalanobis–Taguchi System (MTS). For effective management, the risk of a chemical can be ranked according to a comprehensive risk assessment and calculated through multivariate analysis using the GHS. Relative hazards are then identified using MTS multivariate analysis with GHS information, even when there is insufficient information about the chemical’s characteristics, and the method can be applied to a large number of different chemicals. Full article

In this paper, a gait patterns classification system is proposed, which is based on Mahalanobis–Taguchi System (MTS). The classification of gait patterns is necessary in order to ascertain the rehab outcome among anterior cruciate ligament reconstruction (ACLR) patients. (1) Background: One of the most critical discussion about when ACLR patients should return to work (RTW). The objective was to use Mahalanobis distance (MD) to classify between the gait patterns of the control and ACLR groups, while the Taguchi Method (TM) was employed to choose the useful features. Moreover, MD was also utilised to ascertain whether the ACLR group approaching RTW. The combination of these two methods is called as Mahalanobis-Taguchi System (MTS). (2) Methods: This study compared the gait of 15 control subjects to a group of 10 subjects with laboratory. Later, the data were analysed using MTS. The analysis was based on 11 spatiotemporal parameters. (3) Results: The results showed that gait deviations can be identified successfully, while the ACLR can be classified with higher precision by MTS. The MDs of the healthy group ranged from 0.560 to 1.180, while the MDs of the ACLR group ranged from 2.308 to 1509.811. Out of the 11 spatiotemporal parameters analysed, only eight parameters were considered as useful features. (4) Conclusions: These results indicate that MTS can effectively detect the ACLR recovery progress with reduced number of useful features. MTS enabled doctors or physiotherapists to provide a clinical assessment of their patients with more objective way. Full article