A Model-Driven Approach for the Failure Data Analysis of Multiple Repairable Systems Without Information on Individual Sequences

Pulcini, Gianpaolo

doi:10.1109/tr.2013.2273040

Cited by 8 publications

(4 citation statements)

References 23 publications

(66 reference statements)

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“…Usually the model-based approach makes use of mathematical models for the monitored system, but is difficult to model for a complex system (e.g. aircraft engines) [13], [14]. The experience-based prognostic approach uses probabilistic or stochastic models for the degradation by taking into account the data and the knowledge accumulated through experience, but the results are often not accurate enough to accommodate the engine's dynamic complex processes [15].…”

Section: Introductionmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

137

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As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.Index Terms-Fusion prognostics, sensor data, prognostics and health management, remaining useful life, aircraft engines.

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Section: Introductionmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

137

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“…And the random shock process is often described as a homogeneous Poisson process [13]. According to the various failure mechanisms of hard failure, the following shock models mainly exist: extreme shock [14], cumulative shock [15], δ shock [16], and hybrid shock [17]. As an example, Sandia National Laboratories conducted reliability tests on MEMS parts [18].…”

Section: Introductionsmentioning

confidence: 99%

Multi-Performance Degradation System Reliability Analysis with Varying Failure Threshold Based on Copulas

Gan,

Tang

2024

Symmetry

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This paper investigated reliability modeling for systems subject to dependent competing risks considering that variation of the failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of the threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of Copula functions is symmetry. Then, a linear Wiener process model was applied to model performance degradation failure, and a multi-performance degradation correlated-competition model based on a Copula function was constructed, which considered the correlated competition between multi-performance degradation failures. Lastly, a micromotor system was used to analyze the applicability of the proposed model for bivariate instances, demonstrating the rationality and effectiveness of the proposed model.

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“…And the random shock process is often described as a homogeneous Poisson process [13]. According to the various failure mechanisms of hard failure, the following shock models mainly exist: extreme shock [14], cumulative shock [15], 𝛿𝛿 shock [16], and hybrid shock [17]. As an example, Sandia National Laboratories conducted reliability tests on MEMS parts [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Performance-Degradation System Reliability Analysis with Varying Failure Threshold Based on Copula

Gan,

Tang

2023

Preprint

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This paper investigated reliability modeling for systems subject to dependent competing risks considering that the variation of failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of copula is symmetry. Then, A linear Wiener process model is applied to model performance degradation failure, and a multi-performance-degradation correlated-competition model based on a Copula function is constructed, which considers the correlated competition between multiperformance degradation failures. Lastly, the micromotor system is used to analyze the applicability of the proposed model for bivariate instances. demonstrating the rationality and effectiveness of the proposed model.

show abstract

A Model-Driven Approach for the Failure Data Analysis of Multiple Repairable Systems Without Information on Individual Sequences

Cited by 8 publications

References 23 publications

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Multi-Performance Degradation System Reliability Analysis with Varying Failure Threshold Based on Copulas

Multi-Performance-Degradation System Reliability Analysis with Varying Failure Threshold Based on Copula

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