A framework for identification of maintenance significant items in reliability centered maintenance

Tang, Yula; Liu, Qingyou; Jing, Jiajia; Yan, Yang; Zou, Zhengwei

doi:10.1016/j.energy.2016.11.011

Cited by 67 publications

(46 citation statements)

References 18 publications

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“…Therefore, by experts, professional maintenance personnel and field operators through the review pointed out that the risk level of influencing factors according to the situation divided into 3-5 levels, using a 10-point system for scoring. The scoring standards for the 10 influencing factors related to the risk level of mechanical equipment are as follows [11].…”

Section: Maintenance Decision Methods Based On Risk Level 21 Definitimentioning

confidence: 99%

Maintenance Decision Method Based on Risk Level

Tang¹,

Yang²,

Wang³

2021

Operations Management - Emerging Trend in the Digital Era

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Maintenance decision method of the mechanical equipment still has some deficiencies and shortcomings, including unreasonable maintenance methods, surplus or insufficient repair, and unscientific inspection and repair intervals, especially for equipment with different risks. As a consequence, more frequent failures and higher maintenance costs of the mechanical equipment occur along with some major safety accidents and economic losses in the process of production. To overcome these problems, a framework for maintenance decision method based on risk level is presented for mechanical equipment in the petrochemical industry. First, 10 evaluation indexes and a set of scoring criteria quantifying the subjective evaluation are defined to evaluate the risk level of each mechanical equipment. Based on the analytic hierarchy process (AHP), the weight of the evaluation indexes and the evaluation model of the risk levels were established. Then, the subjective effects in the scoring process were removed using the Monte Carlo simulation (MCS) approach. Next, maintenance decision-making trees of the mechanical equipment were formulated by referencing the logic decision tree of reliability-centered maintenance. Finally, the feasibility of the framework was effectively verified by testing a well-control system in the oil field, for which the risk level and maintenance methods were obtained.

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Section: Maintenance Decision Methods Based On Risk Level 21 Definitimentioning

confidence: 99%

Maintenance Decision Method Based on Risk Level

Tang¹,

Yang²,

Wang³

2021

Operations Management - Emerging Trend in the Digital Era

Self Cite

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“…In the Level of the RHT, there are four risk types relating to risk factors, including safety risk (SR), environmental risk (ER), economic loss risk (ELR), and maintenance cost risk (MCR). Based on semantic vagueness results of the risk factors in 3 FMECA reports, the quantization value of the risk factors corresponding to each failure mode is obtained by the Fuzzy Set Theory, as shown in Table 6 [17].…”

Section: Case Studymentioning

confidence: 99%

Quantitative Risk Evaluation Model of the Multilevel Complex Structure Hierarchical System in the Petrochemical Industry

Tang

Shu

et al. 2019

Mathematical Problems in Engineering

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In the petrochemical production system, the high-risk items malfunction may lead to major accidents so that the risk level of the items has become the highest focus of attention for the enterprises in petrochemical industry. Based on structural composition and risk relationship, a risk evaluation framework of the petrochemical production system can generally be divided into subsystems (SS), components and parts (CP), failure modes (FM), risk types, and risk factors. So it is a characteristic of multilevel, complex structure, and lack of evaluation criteria that the evaluated object has in the process of risk evaluation. However, there are few targeted modeling and calculation methods to carry out quantitative risk evaluation in the face of the evaluated object. In order to achieve risk quantitative evaluation of the complex structure hierarchical system, a multilevel Borda model (MLBM) is presented innovatively by us based on the traditional Borda method in this study. Moreover, the MLBM are applied to realize quantitative risk evaluation of the main structure system of truss type crane on the offshore platform. In this case study, the equivalent risk value (ERV) and risk priority number (RPN) of the evaluated object with multilevel, complex structure, and inadequate evaluation criteria are calculated and the risk ties in the RPN are effectively reduced. Then, the quantitative risk results can clarify the risk level and distribution of the high-risk items throughout the production system and provide data support for the development of risk control measures to better protect the production safety. Hence, the feasibility and practicability of the method are verified with the case study. The MLBM can be used to solve other comprehensive evaluation problems with a complex hierarchical structure as well.

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“…Reliability aspects like increased complexity represented by the interconnection and interoperability between different types of systems are an additional aspect to consider in the smart grid context and motivates the exploration of new approaches to assess the smart grid operational reliability. In this context, Reliability Centered Maintenance (RCM) has been a solution proposed in recent years and applied to electric power systems [4][5][6][7][8][9][10][11]; RCM combines the reliability evaluation and maintenance planning [5,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Benefits of an RCM approach far exceed those of any type of maintenance program and it has been widely used by the aircraft, oil & gas, spacecraft or nuclear industry [17]. However, the use of RCM is relatively new in industries outside of these areas [4][5][6][7][8][9][10][11]. RCM strategies and their impact on electrical power systems are still undefined due to the lack of evidence and current applications.…”

Section: Introductionmentioning

confidence: 99%

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

Zúñiga¹,

Baleia²,

Fernandes³

et al. 2020

Preprint

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Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

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A framework for identification of maintenance significant items in reliability centered maintenance

Cited by 67 publications

References 18 publications

Maintenance Decision Method Based on Risk Level

Maintenance Decision Method Based on Risk Level

Quantitative Risk Evaluation Model of the Multilevel Complex Structure Hierarchical System in the Petrochemical Industry

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

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