Bearing diagnosis based on Mahalanobis–Taguchi–Gram–Schmidt method

Shakya, Piyush; Kulkarni, Makarand S.; Darpe, Ashish K.

doi:10.1016/j.jsv.2014.10.034

Cited by 52 publications

(19 citation statements)

References 22 publications

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“…Improved MTS Algorithm. We used MTS for data classification [18][19][20][21][22][23][24][25]. In MTS, Mahalanobis space (MS; reference group) is obtained using standardized variables of healthy or normal data.…”

Section: Reducing Highly Correlatedmentioning

confidence: 99%

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Zhan

Chen

Cheng

et al. 2020

Computational Intelligence and Neuroscience

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Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.

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Section: Reducing Highly Correlatedmentioning

confidence: 99%

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Zhan

Chen

Cheng

et al. 2020

Computational Intelligence and Neuroscience

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“…It leads to an approximate singular correlation matrix that imposes difficulty to compute MDs. We have used Gram–Schmidt orthogonalization process (GSP) [ 49 , 50 , 51 ] to tackle this issue of multicollinearity. Thus, MDs can be computed using GSP and the computation process is as follows:…”

Section: Technical Backgroundmentioning

confidence: 99%

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Habib

Rai

Kim

2020

Sensors

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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.

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“…Based on Schmidt orthogonalization, Su and Hsiao [34] proposed weighted Schmidt orthogonalization to calculate MD. Shakya et al [35] used an integrated Schmidt orthogonalization method for the classification of rolling bearings. On the basis of the generalized inverse matrix, Han et al [36] redefined MD and proposed the Mahalanobis-Taguchi generalized inverse matrix method.…”

Section: Introductionmentioning

confidence: 99%

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

Xiao

Shi

et al. 2020

Computational Intelligence and Neuroscience

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The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

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Bearing diagnosis based on Mahalanobis–Taguchi–Gram–Schmidt method

Cited by 52 publications

References 22 publications

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

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