Maintenance strategy for tilting table of rolling mill based on reliability considerations

Deshpande, Vinayak; Modak, J. P.

doi:10.1016/s0951-8320(02)00152-7

Cited by 19 publications

(6 citation statements)

References 3 publications

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“…In rolling mills, PdM techniques are nowadays implemented instead of traditional break-down or preventive maintenance strategies [37]. The majority of research studies concern fault detection in roller bearings, gearboxes and drivetrains, the tilting table, drivers, or the hydraulic systems [38][39][40][41][42][43]. Although there are studies for condition monitoring in rollers [44,45], no work was found dealing specifically with the coating segments, for which this paper's method is applied.…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

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The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.

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Section: Methods For Assessing Rulmentioning

confidence: 99%

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

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“…Deshpande and Modak (2002) adopted the RCM methodology to improve the maintenance strategy of steel melting equipment production in steel shops in the steel industry. Deshpande and Modak (2003) presented a case study to optimise the maintenance strategy of the tilting table system of a rolling mill using the RCM methodology. Fore and Msipha (2010) applied the RCM approach to create a cost-effective preventive maintenance strategy to address the dominant causes of equipment failure in ferrochrome processing plants.…”

Section: Rcm Applications – An Overviewmentioning

confidence: 99%

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

Ali Ahmed Qaid,

Ahmad,

Mustafa

et al. 2024

JQME

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PurposeThis study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.Design/methodology/approachThe proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.FindingsThe proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.Originality/valueThis paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

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“…Decision-making on maintenance risks has an aim to select the maintenance mode reasonably for avoiding the risk of equipment failure or reducing it to the acceptable level. First, determining the basic types of maintenance mode and their influence on the risk of equipment failure is the premise of decision-making on maintenance mode [1][2][3][4]. Now, the logical judgment in decision-making on maintenance mode is mainly applied in the reliability-based maintenance analysis, but it does not cover all risk factors and places much emphasis on economy in the analysis, so it is not suitable for the decisionmaking on task-based maintenance mode.…”

Section: Introductionmentioning

confidence: 99%

Research on Decision-making Model for Maintenance Mode Risks of Equipment Components within Task Cycle

Liu¹,

Lv²,

Yang³

2016

Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

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To develop scientific and reasonable maintenance decision-making scheme for equipment during task execution, select a suitable maintenance mode for components, control and reduce the risk of equipment failure to the maximum, this paper analyzes the basic types of maintenance mode for equipment components within task cycle, as well as their influence on the risk of equipment failure. Taking the risk, probability, consequence and detectability of failure as the decision-making indicators, the logical judgment method is employed to create the logical judgment diagram of decision-making risks for maintenance mode of equipment components within task cycle. If any maintenance mode cannot be judged through logical judgment diagram, the safety, task and economic risks are taken as the decision-making indicators, and the fuzzy theory and analytic network process are combined to build a decisionmaking model for maintenance mode risks of equipment based on analytic network process, so as to introduce the decisionmaking method for optimal maintenance mode.

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Maintenance strategy for tilting table of rolling mill based on reliability considerations

Cited by 19 publications

References 3 publications

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

Research on Decision-making Model for Maintenance Mode Risks of Equipment Components within Task Cycle

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