Repair and maintenance of rotating equipment components

Bloch, Heinz P.; Geitner, Fred K.

doi:10.1016/b978-0-12-818729-6.00008-3

Cited by 2 publications

(3 citation statements)

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“…Companies and industries have adopted various maintenance approaches and policies to diminish maintenance expenses and enhance their productivity and risk management. The concept of maintenance has evolved over the last few decades from a corrective approach [44,45] (maintenance actions after a failure) to a preventive approach (maintenance actions to prevent the failure) and recently, key industrial manufacturers have invested in predictive maintenance (PdM) to maximize the availability of machine parts and equipment uptime and deploy maintenance more cost effectively [46]. Unlike traditional models, PdM is designed to monitor the actual condition of the equipment to alert the system in advance and determine whether maintenance will be required.…”

Section: Maintenance Modelsmentioning

confidence: 99%

“…In the last two decades, with the accelerated growth of computer science and emerging IoT technologies, maintenance strategies have revolutionized from preventive maintenance to predictive maintenance (PdM) [58][59][60]. Recently, key industrial manufacturers have invested in PdM to maximize the availability of machine parts and equipment uptime and deploy maintenance more cost effectively [46]. The possibility of conducting predictive maintenance contributes to reducing machine downtime and costs and improving the control and quality of production [61][62][63][64].…”

Section: Predictive Maintenance (Pdm)mentioning

confidence: 99%

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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: Maintenance Modelsmentioning

confidence: 99%

Section: Predictive Maintenance (Pdm)mentioning

confidence: 99%

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

Maktoubian

Taskhiri

Turner

2021

Forests

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“…Practical knowledge indicates that these valves have problems related to frictional resistance and accelerated wear of cooperating parts. To confirm this fact, literature was cited indicating the existence of practical problems related to the reliable operation of the analyzed pair of materials [39][40][41][42][43]. The sliding layer created during operation of the expanded graphite-steel combination affects the effectiveness of the friction process.…”

Section: Introductionmentioning

confidence: 99%

Sliding Layer Formation during Tribological Contact between Expanded Graphite and Stainless Steel—A Pilot Study

Rewolińska,

Leksycki,

Perz

et al. 2024

Applied Sciences

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The sliding layer created during operation of the expanded graphite–steel combination has had a huge impact on the effectiveness of the friction process, and thus on the sustainable development of society. Knowledge of the factors determining the properties of the sliding layer will make it possible to reduce friction resistance in the future through the proper design and selection of sliding pairs for given applications. This paper studies the effect of the moisture content of expanded graphite on the formation of a sliding layer on a stainless steel surface. The tests were carried out in static contact for 30 s and dynamic contact for 15 and 30 min, for loads of 10, 20, and 30 N and speeds of 25 and 50 mm/s. To determine the changes in surface geometry due to material transfer, the Ra roughness value of the surface of stainless steel samples was measured. In order to realize the purpose of the work and evaluate the effect of moist rings on the resulting sliding layer, the results of the surface roughness of stainless steel samples working with dry and moist graphite rings were compiled. The obtained results show that the presence of water in the stainless steel-expanded graphite friction node affects the formation of a sliding layer. The resulting layer reduces the surface roughness of the cooperating materials and prevents their accelerated wear. After 5 min of work with the water-soaked graphite counter-sample, depending on the applied friction conditions, a reduction in the surface roughness of the stainless steel sample was achieved in the range of 11–18% compared to the initial value. After 30 min of operation, the surface roughness decreased by 3 to 25%. Pilot studies have shown that operating conditions influence the formation of a sliding layer in the stainless steel-expanded graphite tribological contact. This confirms the validity of conducting further research in this area.

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Repair and maintenance of rotating equipment components

Cited by 2 publications

References 0 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

Sliding Layer Formation during Tribological Contact between Expanded Graphite and Stainless Steel—A Pilot Study

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