An integrated methodological approach for optimising complex systems subjected to predictive maintenance

Ahmed, Umair; Carpitella, Silvia; Certa, Antonella

doi:10.1016/j.ress.2021.108022

Cited by 27 publications

(13 citation statements)

References 65 publications

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“…It is specifically useful to build a baseline of the potential failure modes and effects but also has some criticalities that can be overcome by the introduction of specific predictive maintenance policies. Indeed, as pointed out by Ahmed et al. (2021), FMECA does not take into account coexisting failures and mutual relationships among them and it is also reasonably vulnerable to human error.…”

Section: Discussionmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

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PurposeThe research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.Design/methodology/approachAssets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).FindingsA case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.Originality/valueThe novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

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Section: Discussionmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

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“…However, it is very difficult to create such a model as most machines consist of complex mechanical and electrical systems that are difficult to formulate mathematically. 35,36 In data-driven methods, the data obtained from the sensors is processed by machine learning methods and used to examine the deterioration of components or the current health status of the system or its RUL. However, these models use computational power and a large amount of data.…”

Section: Literature Reviewmentioning

confidence: 99%

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

Özkat

Bektaş

Nielsen

et al. 2023

International Journal of Micro Air Vehicles

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Unmanned Aircraft Systems (UAS) has become widespread over the last decade in various commercial or personal applications such as entertainment, transportation, search and rescue. However, this emerging growth has led to new challenges mainly associated with unintentional incidents or accidents that can cause serious damage to civilians or disrupt manned aerial activities. Machine failure makes up almost 50% of the cause of accidents, with almost 40% of the failures caused in the propulsion systems. To prevent accidents related to mechanical failure, it is important to accurately estimate the Remaining Useful Life (RUL) of a UAS. This paper proposes a new method to estimate RUL using vibration data collected from a multi-rotor UAS. A novel feature called mean peak frequency, which is the average of peak frequencies obtained at each time instance, is proposed to assess degradation. The Long Short-Term Memory (LSTM) is employed to forecast the subsequent 5 mean peak frequency values using the last 7 computed values as input. If one of the estimated values exceeds the predefined 50 Hz threshold, the time from the estimation until the threshold is exceeded is calculated as the RUL. The estimated mean peak frequency values are compared with the actual values to analyze the success of the estimation. For the 1st, 2nd, and 3rd replications, RUL results are 4 s, 10 s, and 10 s, and root mean square error (RMSE) values are 3.7142 Hz, 1.4831 Hz, and 1.3455 Hz, respectively.

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“…Despite the fact that MCDM methods have been largely applied in literature to deal with the evaluation of both sustainability indicators (Li et al, 2020) and general maintenance issues (Ahmed et al, 2021;Benítez et al, 2019), such authors as Olugu et al (2021) notice as limited research has been produced on maintenance decision-making as a driver for sustainable management. To the best the authors' knowledge, an ANP-based approach evaluating the influence of sustainability factors for the prioritisation of maintenance interventions has never been proposed for optimising such critical systems as industrial water distribution networks.…”

Section: Literature Reviewmentioning

confidence: 99%

How sustainability factors influence maintenance of water distribution systems feeding manufacturing industries

Carpitella¹,

Certa²,

Marcon³

2022

Proceedings of the 10th International Workshop on Simulation for Energy, Sustainable Development &Amp; Environment, SESDE

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This work aims to analyse the role played by relevant sustainability factors towards the implementation of maintenance interventions in the manufacturing industrial sector. In this context, we focus on industrial water distribution systems, on whose effective work depends the functioning of core plants as well as general industrial facilities. In detail, we propose a Multi-Criteria Decision-Making (MCDM) application based on the use of the Analytic Network Process (ANP) as a methodological way to prioritise maintenance interventions while considering the influence of some of the most relevant sustainability factors identified in literature. The main advantage of such an approach consists in the elaboration of a flexible maintenance procedure for companies based on a well-known and reliable multi-criteria application. The novelty of our work refers to the development of a structured link between sustainability factors and maintenance management of industrial water distribution systems, something that is fundamental in manufacturing but also in other fields of application.

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An integrated methodological approach for optimising complex systems subjected to predictive maintenance

Cited by 27 publications

References 65 publications

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS

How sustainability factors influence maintenance of water distribution systems feeding manufacturing industries

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