New prognosis approach for preventive and predictive maintenance — Application to a distillation column

Daher, Ahmad; Hoblos, Ghaleb; Khalil, Mohamad; Chetouani, Yahya

doi:10.1016/j.cherd.2019.10.029

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…Here fuzzy clustering is deployed to determine the severity of a failure, before deploying semantic technologies to investigate the time of the failure. Similarly, Daher et al [70], applies fuzzy C-means alongside ANFIS to detect the RUL of a distillation column.…”

Section: Clusteringmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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Section: Clusteringmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…The processed data can then be used to identify the remaining useful life (RUL) of the equipment by comparing the healthy and faulty data, by using certain condition indicators (see an example from ( Daher et al, 2020 ) applied in lab-scaled distillation column). Different models of RUL computation can be found in ( Okoh et al, 2014 ) and the review of data-driven statistical approaches of RUL estimation can be found in ( Cox, 1972 ).…”

Section: Future Research Directionsmentioning

confidence: 99%

Asset maintenance optimisation approaches in the chemical and process industries – A review

Chin

Varbanov

Benjamin

et al. 2020

Chemical Engineering Research and Design

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“…In the literature, many diagnosis approaches have been developed for continuous and discrete event systems (Van Gorp, 2013; Zouaghi et al, 2011;Daher, 2018). Most of the work consists of either extending the existing techniques from continuous systems or discrete event systems to hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

A Neural-Fuzzy Approach for Fault Diagnosis of Hybrid Dynamical Systems: Demonstration on Three-Tank System

Achbi

Kechida

Mhamdi

et al. 2021

Acta Mechanica Et Automatica

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This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment.

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New prognosis approach for preventive and predictive maintenance — Application to a distillation column

Cited by 12 publications

References 12 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Asset maintenance optimisation approaches in the chemical and process industries – A review

A Neural-Fuzzy Approach for Fault Diagnosis of Hybrid Dynamical Systems: Demonstration on Three-Tank System

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