Optimization of industrial energy efficiency by intelligent Predictive Maintenance tools Case of misalignment of an industrial system

Kihel, Ali El; Kihel, Yousra El; Bakdid, Amar; Gziri, Hassan; Manssouri, Imad; Amegouz, Driss

doi:10.1109/icecocs50124.2020.9314505

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…They proposed an approach to provide intelligent predictive maintenance control by visualizing varying types of information using augmented reality. Mateusz Marzec et al [101] investigated various machine-learning techniques and proposed a procedure to automatize the intelligent predictive maintenance process. There are many research studies regarding the design and implementation of IPdM approaches in the literature and the majority of them characterized the models based on the following technologies [100,[102][103][104][105][106][107][108]:…”

Section: Intelligent Predictive Maintenance (Ipdm)mentioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Maktoubian

Taskhiri

Turner

2021

Forests

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The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

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Section: Intelligent Predictive Maintenance (Ipdm)mentioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Maktoubian

Taskhiri

Turner

2021

Forests

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“…It promises failure anticipation, quality improvements, and availability of automated production lines [4,5]. Predictive maintenance not only predicts possible failure but also identifies problems in complex machines and recognizes parts for repair [6,7]. To address this problem, businesses must generally digitize their operations by applying different technology levers that should support decentralized choices via system connection, digital transformation, and instantaneous communication.…”

Section: Introductionmentioning

confidence: 99%

Development process to bearing fault diagnostic and prognostic for the predictive maintenance era

Bouyahrouzi

Kihel

2022

E3S Web Conf.

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Today, the manufacturing industry seeks to improve competitiveness by converging on new technologies to ensure a new engine of growth, moreover, systems based on IoT and artificial intelligence are increasingly used in this convergence. This new industry must meet the challenges of productivity and competitiveness to interconnect the physical and digital world in which machines, information systems, and products communicate permanently, all to reduce consumers and maintain productivity gains and optimize them in terms of energy consumed reduced breakdowns... This article presents an original and innovative contribution. A new model has been proposed that summarizes an approach based on machine learning, intending to perform predictive maintenance based on artificial neural networks, considering the values acquired by sensors in real-time, it allows us a fast and very low implementation of predictive maintenance, particularly important for companies. The model is validated in real situations. The results show a very high level of accuracy.

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Optimization of Industrial Energy Efficiency by Intelligent Predictive Maintenance Tools, Case of Coupling Misalignment and Unbalance for an Industrial System

Kihel¹,

Bakdid²,

Kihel³

2023

Lecture Notes in Electrical Engineering

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Optimization of industrial energy efficiency by intelligent Predictive Maintenance tools Case of misalignment of an industrial system

Cited by 3 publications

References 3 publications

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Development process to bearing fault diagnostic and prognostic for the predictive maintenance era

Optimization of Industrial Energy Efficiency by Intelligent Predictive Maintenance Tools, Case of Coupling Misalignment and Unbalance for an Industrial System

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