Regressing sample quantiles to perform nonparametric capability analysis

Salazar-Alvarez, María I.; Temblador-Pérez, Carmen; Conover, W. J.; Tercero-Gómez, Víctor G.; Cordero-Franco, Álvaro E.; Beruvides, Mario G.

doi:10.1007/s00170-015-8285-6

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…is paper introduces the concept of sample quantile on the basis of multiscale permutation entropy. Quantile divides the probability distribution range of a random variable into equal numerical points, regardless of the time dimension characteristics of the time series, and directly and effectively characterizes the characteristics of signal time series data fluctuations [19]. For the rolling bearing vibration signal, the entropy data of the multiscale arrangement have no obvious periodicity and change law.…”

Section: Qmpe Feature Extraction Methodsmentioning

confidence: 99%

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Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

Long¹,

Shi²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Early fault diagnosis of bearings is the basis of condition-based maintenance. To overcome the difficulty of early fault diagnosis for the mechanical system, a new conception named quantile multiscale permutation entropy (QMPE) is defined, and a new feature extraction method based on QMPE is proposed. On the basis of the multiscale entropy, the multiscale permutation entropy for the gathered vibration signal of equipment is obtained, and the sample quantile is calculated, which is employed to analyze the weak change of the variation signal. The proposed method is verified with the full lifetime datasets of a certain bearing, which proves that signal features extracted by the QMPE method can not only truly express the bearing detailed condition changing from normal to fault but also duly detect the early fault of the bearing. Comparing with other methods for early fault diagnosis, the proposed method can advance the finding time of the early fault obviously.

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Section: Qmpe Feature Extraction Methodsmentioning

confidence: 99%

“…e time interval between every two files is 10 minutes. e start time of data collection was 10:32:39, February 12, 2004, and the end time was 06:22:39, February 19,2004. A total of 984 files were collected, and the test lasted 163.8 hours.…”

Section: Experimental Datamentioning

confidence: 99%

Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

Long¹,

Shi²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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“…On the basis of PE, this paper introduces the concept of sample quantile and defines SQPE. Sample quantile divides the probability distribution range of a random variable into equal numerical points, without considering the time dimension characteristics of the time series, and directly and effectively characterizes the characteristics of the fluctuation of the signal time series data [18].…”

Section: Sample Quantile Permutation Entropy (Sqpe)mentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

Qiangqiang¹,

Dai²,

Dai³

2019

Mathematical Problems in Engineering

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The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

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“…For instance, Field and Smith (1994) uses this approach to reduce the "pull" given by outliers when performing an esti-mation. Salazar-Alvarez et al (2016) presented a regression approach to improve the estimates when dealing with extreme values. Regression Approach for Quantile Estimation, RAQE, was introduced to solve the problem of process capability estimation based on the percentile method.…”

Section: Introductionmentioning

confidence: 99%

“…The problem was limited to the use of one sample and the estimation of process capability indexes. This paper aims to generalize the methodology proposed by Salazar-Alvarez et al (2016). Since RAQE is based on regression techniques it can be easily confused with research areas like quantile regression and extremal quantile regression; nevertheless, RAQE does not use variable conditioning.…”

Section: Introductionmentioning

confidence: 99%

A Quantile Estimate Based on Local Curve Fitting

Salazar-Alvarez,

Tercero-Gómez,

Cordero-Franco

et al. 2017

Preprint

Self Cite

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Quantile estimation is a problem presented in fields such as quality control, hydrology, and economics. There are different techniques to estimate such quantiles. Nevertheless, these techniques use an overall fit of the sample when the quantiles of interest are usually located in the tails of the distribution. Regression Approach for Quantile Estimation (RAQE) is a method based on regression techniques and the properties of the empirical distribution to address this problem. The method was first presented for the problem of capability analysis. In this paper, a generalization of the method is presented, extended to

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Regressing sample quantiles to perform nonparametric capability analysis

Cited by 3 publications

References 14 publications

Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

A Quantile Estimate Based on Local Curve Fitting

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