Artificial Intelligence in Maintenance

Kobbacy, Khairy A. H.

doi:10.1007/978-1-84800-011-7_9

Cited by 26 publications

(25 citation statements)

References 96 publications

(99 reference statements)

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“…CBM programs have received several criticisms due to their complexity (Pintelon and Parodi-Herz 2008) (Kobbacy 2008) and to their challenges for practical implementation (see graphic description in Figure 2):…”

Section: Complexity Causes and Implementation Challenges Of Cbm Programsmentioning

confidence: 99%

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

Guillén

Crespo

Gómez

et al. 2016

Computers in Industry

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CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company's strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept "symptom of failure", its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM opensoftware application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.

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Section: Complexity Causes and Implementation Challenges Of Cbm Programsmentioning

confidence: 99%

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

Guillén

Crespo

Gómez

et al. 2016

Computers in Industry

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“…The information stored about the issues contain all sensor values prominent to the issue, the root cause of the issue and the measures that where implemented to fix the issue. [11] Realtime data is fed to the software that continuously checks the fit of the current situation against the recorded cases in the database.…”

Section: A Industry 40 and Intelligent Predictive Maintenancementioning

confidence: 99%

Smart Maintenance-Industry 4.0 and Smart Maintenance: from Manufacturing to Subsea Production Systems

Marhaug¹,

Schjølberg²

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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“…Furthermore, the design and maintenance of complex systems, such as engineering structures and machines, is made challenging by the unpredictable collective behaviors and properties of these concurrently operating and interacting components. These issues are typically difficult to analyze through conventional methods [1]. Most of these complex engineering systems are continuous, and partial differential equation (PDE) models are used to characterize and understand their behavior.…”

Section: Introductionmentioning

confidence: 99%

Predicting traction return current in electric railway systems through physics-informed neural networks

Kapoor

Wang

Núñez

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theory, where the double beams are connected with a Winkler foundation. In particular, forward and inverse problems for the Euler-Bernoulli and Timoshenko partial differential equations (PDEs) are solved using nondimensional equations with the physics-informed loss function. Higher-order complex beam PDEs are efficiently solved for forward problems to compute the transverse displacements and cross-sectional rotations with less than 1e − 3 percent error. Furthermore, inverse problems are robustly solved to determine the unknown dimensionless model parameters and applied force in the entire space-time domain, even in the case of noisy data. The results suggest that PINNs are a promising strategy for solving problems in engineering structures and machines involving beam systems.

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Artificial Intelligence in Maintenance

Cited by 26 publications

References 96 publications

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

Smart Maintenance-Industry 4.0 and Smart Maintenance: from Manufacturing to Subsea Production Systems

Predicting traction return current in electric railway systems through physics-informed neural networks

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