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

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 bioen...

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Autores principales:	Jamal Maktoubian, Mohammad Sadegh Taskhiri, Paul Turner
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intelligent predictive maintenance (IPdM) big data analytics chipper machine learning operator fatigue remaining useful life (RUL) Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/a8f54dff4051452ead34bfd5d8612ad4
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spelling	oai:doaj.org-article:a8f54dff4051452ead34bfd5d8612ad42021-11-25T17:37:54ZIntelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities10.3390/f121114951999-4907https://doaj.org/article/a8f54dff4051452ead34bfd5d8612ad42021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1495https://doaj.org/toc/1999-4907The 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.Jamal MaktoubianMohammad Sadegh TaskhiriPaul TurnerMDPI AGarticleintelligent predictive maintenance (IPdM)big data analyticschippermachine learningoperator fatigueremaining useful life (RUL)Plant ecologyQK900-989ENForests, Vol 12, Iss 1495, p 1495 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent predictive maintenance (IPdM) big data analytics chipper machine learning operator fatigue remaining useful life (RUL) Plant ecology QK900-989
spellingShingle	intelligent predictive maintenance (IPdM) big data analytics chipper machine learning operator fatigue remaining useful life (RUL) Plant ecology QK900-989 Jamal Maktoubian Mohammad Sadegh Taskhiri Paul Turner Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
description	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.
format	article
author	Jamal Maktoubian Mohammad Sadegh Taskhiri Paul Turner
author_facet	Jamal Maktoubian Mohammad Sadegh Taskhiri Paul Turner
author_sort	Jamal Maktoubian
title	Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
title_short	Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
title_full	Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
title_fullStr	Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
title_full_unstemmed	Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities
title_sort	intelligent predictive maintenance (ipdm) in forestry: a review of challenges and opportunities
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a8f54dff4051452ead34bfd5d8612ad4
work_keys_str_mv	AT jamalmaktoubian intelligentpredictivemaintenanceipdminforestryareviewofchallengesandopportunities AT mohammadsadeghtaskhiri intelligentpredictivemaintenanceipdminforestryareviewofchallengesandopportunities AT paulturner intelligentpredictivemaintenanceipdminforestryareviewofchallengesandopportunities
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Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

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