Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

Ferreiro, Susana; Arnáiz, Aitor; Sierra, Basilio; Irigoien, Itziar

doi:10.1016/j.eswa.2011.12.027

Cited by 59 publications

(34 citation statements)

References 26 publications

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“…The research uses a stochastic technique based on Weibull Cumulative Damage Model and multiple service related stress profiles (e.g., mechanical, thermal and humidity stresses) to predict the remaining useful life of the component. A Bayesian learning based prognostics is also proposed by Ferreiro et al [44] to reduce the maintenance cost. Uncertainty in measurements is a major source of inaccuracies and therefore a challenge for the condition monitoring.…”

Section: Monitoring Diagnostics and Prognosticsmentioning

confidence: 98%

Continuous maintenance and the future – Foundations and technological challenges

et al. 2016

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Section: Monitoring Diagnostics and Prognosticsmentioning

confidence: 98%

Continuous maintenance and the future – Foundations and technological challenges

et al. 2016

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“…After a number of cycles these cracks develop into pitting or spalling with the subsequent removal of material. A degradation model for wear caused by subsurface fatigue has been proposed by Ghosh et al,142 who provided an experimental correlation between wear and crack propagation, which is a function of the shear stress on the surface as shown in equation (13), where N is the number of cycles to failure; a and b are empirical constants; and Q is the contact shear stress that depends on the friction coefficient and load applied…”

Section: Wearmentioning

confidence: 99%

“…[8][9][10] DDMs are based on statisticaland machine-learning techniques and do not rely on the knowledge of the physics that govern the system or its degradation mechanisms: 9 these techniques have proven successful for fault detection, 11 classification 12 and RUL estimation. 13 PbMs consist in the use of mathematical models that describe the physics of the component to assess its current and future health. The performance of PbMs depends on the capability of the models to accurately represent the failure and degradation phenomena.…”

Section: Introductionmentioning

confidence: 99%

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Cubillo

Perinpanayagam

Esperon-Miguez

2016

Advances in Mechanical Engineering

201

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Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal-metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery.

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“…This paper focuses on the knowledge base, model base and database of the data management module [4] .…”

Section: Design On Decision Support System Of Vehicle Maintenance Supmentioning

confidence: 99%

Research on Decision Support System of Vehicle Maintenance Support

Song-bai¹,

Kang²,

Li³

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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Abstract:Vehicle maintenance support decision is critical in emergency actions. Based on the analysis of vehicle maintenance support elements and processes, this paper builds the architecture of vehicle maintenance support decision support system, and establishes a function module which can accurately describe vehicle maintenance support decision. It will provide basis for vehicle maintenance support decision optimization.

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Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

Cited by 59 publications

References 26 publications

Continuous maintenance and the future – Foundations and technological challenges

Continuous maintenance and the future – Foundations and technological challenges

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Research on Decision Support System of Vehicle Maintenance Support

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