Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

Gobbato, Maurizio; Conte, Joseph Le; Kosmatka, J. B.

doi:10.1109/jproc.2015.2512979

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…The values used are again from British Standard 7910 25 and are shown in Table 1. While this is clearly a simplified model as pointed out in the literature, 30 it is believed that this simplified model is sufficient for illustrative purposes in this study; the approach used here to modelling uncertainty in fatigue is well established in widely accepted standards. 25,31 Moreover, the proposed framework has no theoretical limitations on the complexity of the defect growth model used.…”

Section: Description Of Example Problemmentioning

confidence: 99%

“…load ratio, R=−1). The loading conditions here are again a simplified model and assumes that the loading is a stationary process and can be approximated as a single equivalent stress range similar to References 30 and 33. In practice, there are situations where loading conditions may be nonstationary, meaning that loading conditions may vary with time.…”

Section: Description Of Example Problemmentioning

confidence: 99%

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A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

Leung

Corcoran

2021

Structural Health Monitoring

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The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.

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Section: Description Of Example Problemmentioning

confidence: 99%

Section: Description Of Example Problemmentioning

confidence: 99%

A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

Leung

Corcoran

2021

Structural Health Monitoring

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“…Structural health monitoring is becoming important for condition assessment and future performance predictions of critical load-bearing components in the automotive industry [1,2]. A well-designed monitoring system, assessing structure integrity and predicting remaining fatigue life in real time, can provide efficient inspection and improvement plans in both automotive durability and lightweight design in [3].…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring of Automotive Suspensions: A LSTM Network Approach

Luo

Deng

2021

Shock and Vibration

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In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load-bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large-scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short-term memory (LSTM) network has been proved to capture long-term dependencies in time-series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM-based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models.

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“…At the different product levels and at the different stages of development, the different methods of reliability prediction are used [3]- [6]. At present, the most commonly used reliability estimation methods for electronic equipment are parts count method and part stress analysis method [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Prediction of the Reliability Analysis of an 18-Pulse Rectifier Power Supply for Aircraft Based Applications

et al. 2020

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Due to its advantages of small size and light weight, multi-pulse rectifier has a broad application prospect in the field of aviation. The 18-pulse rectifier is more popular in practical engineering application because of its better rectifier characteristics than 12-pulse, its simpler structure process and easier to realize than 24-pulse. The theoretical research and engineering application of the 18-pulse rectifier have achieved some success. However, whether the reliability of the system can be satisfied has always been one of the key problems to be solved in practical engineering. The aim of this work is to study the reliability modeling and prediction of the 18-pulse rectifier power supply. Its reliability block diagram and mathematical model are established. According to US Military handbook MIL-HDBK-217F, the most authoritative and widely used in the world, the two most commonly used methods for reliability prediction are parts count method and part stress analysis method. The reliability data are estimated by these two methods. It is concluded that the value of mean time between failures in airborne environment is 23326 h, which meets the requirement of 1.5 times of 15000 h. According to the reliability data, the failure mode and effect analysis of its weak links are carried out, and the improvement measures are proposed. This will promote and guide the reliability growth and engineering popularization of the 18-pulse rectifier power supply. At the same time, the reliability of the actual project can be extended to other related engineering applications to ensure the reliable operation of the system.

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Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

Cited by 11 publications

References 15 publications

A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

Health Monitoring of Automotive Suspensions: A LSTM Network Approach

Modeling and Prediction of the Reliability Analysis of an 18-Pulse Rectifier Power Supply for Aircraft Based Applications

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