Kernel method for overlapping coefficients estimation

Eidous, Omar; AL-Talafha, Saif Al-Deen A.

doi:10.1080/03610918.2020.1757711

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…The computed features are then segregated into three categories depending on the APU State determined from the FADEC Data. For each of the features, coefficients of overlap (Δ and Δ ) [33] are then computed to evaluate performance, where:…”

Section: ) Evaluation Of Feature Response Against the Apu Statesmentioning

confidence: 99%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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This paper presents a comprehensive evaluation of the near-field acoustics of an aircraft auxiliary power unit (APU), based on experimental data acquired from an in-situ APU. The aim is to establish whether near-field acoustics can be implemented for online condition monitoring. The APU of Cranfield University's demonstrator aircraft, a Boeing 737-400, has been instrumented to acquire acoustics (near-field and far-field) and vibration data in synchronization with aircraft state parameters under a wide range of operating conditions. The acquired data is first implemented to determine the efficacy of employing nearfield / far-field microphones, and vibration sensors, to monitor the combustion noise and tonal frequency levels from the APU components. Subsequently, an evaluation of the broadband characteristics of the vibroacoustic data and its variations against APU states and performance parameters is conducted based on several categories of feature extraction techniques. The findings suggest that nearfield acoustics lacks the ability to capture the combustion noise process. In addition, the tonal frequencies are also lost due to the level of background noise, fluctuations in the APU speeds, and scattering effects. For the same reasons, the phase couplings occurring between the signals generated by the APU components cannot be detected using acoustic data. Nevertheless, the overall analysis substantiates that the near-field acoustic data can be used to predict the APU operating states and has the potential to be implemented for developing APU performance parameter estimation models to enable condition monitoring.

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Section: ) Evaluation Of Feature Response Against the Apu Statesmentioning

confidence: 99%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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“…A performance metric was needed to compare the performance of each feature in terms of similarity between the results at the two sensor locations, and if applicable, its similarity with background noise features. For this purpose, the average values of the coefficients of overlap [32] were selected, and are defined as follows:…”

Section: Performance Metric For Feature Evaluationmentioning

confidence: 99%

Signal Processing of Acoustic Data for Condition Monitoring of an Aircraft Ignition System

2022

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Degradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this research study, the experimental data set from Cranfield University’s Boeing 737-400 aircraft was utilized. The overall execution of the approach comprised background noise suppression, estimation of the spark repetition frequency and its fluctuation, spark event segmentation, and feature extraction, in order to monitor the state of the ignition system. The methodology successfully demonstrated the usefulness of the approach in terms of detecting inconsistencies in the behavior of the ignition exciter, as well as detecting trends in the degradation of spark acoustic characteristics. The identified features proved to be robust against non-stationary background noise, and were also found to be independent of the acoustic path between the igniter and microphone locations, qualifying an acoustics-based approach to be practically viable.

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“…Some authors defined to be the common area intersected by two or more probability density functions. There are another OVL measures that studied in the literature, such as Matusita measure (𝜌), Morsita measure (𝜆) and Weitzman measure (∆) (see and Eidous and Al-Talafha, 2020). There are another two overlap coefficients (OVL) known as, Pianka`s measure (PI) (see Chauby et al, 2008) and Kullback-Leibler`s (KL) (see Dhaker et al, 2019 and2021).…”

Section: Introductionmentioning

confidence: 99%

“…There are another OVL measures that studied in the literature, such as Matusita measure (𝜌), Morsita measure (𝜆) and Weitzman measure (∆) (see and Al-Talafha, 2020 andAl-Daradkeh, 2022 and the references therein). The overlap measures are commonly used is reliability analysis to estimate the proportion of machine or electronic devices that have similar range of failure time (Dhaker et al,2019) and genetics (Federer et al,.1963).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Pianka and Kullback-Leibler Overlapping Coefficients: Weibull Distributions

Omar

2022

Preprint

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This paper deals with the estimation problem of two the overlapping (OVL) coefficients known as Pianka`s (PI) and Kullback-Leibler`s (KL) measures when two independent random variables 𝑋 and 𝑌 follow Weibull distributions with the same shape parameters. The maximum likelihood estimator (MLE) procedures of the distributions parameters are considered and then the point and interval estimators of PI and KL are obtained. A limited simulation study is achieved to study the behavior of the interested point estimators for finite sample sizes.

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Kernel method for overlapping coefficients estimation

Cited by 7 publications

References 20 publications

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Signal Processing of Acoustic Data for Condition Monitoring of an Aircraft Ignition System

Estimation of Pianka and Kullback-Leibler Overlapping Coefficients: Weibull Distributions

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