Condition-based maintenance strategy for assembly line equipment based on Gamma process approach

In this paper, reduction factor, which describes the degree of service age reduction resulting from the preventive maintenance, is introduced into a condition-based maintenance strategy. The model determining the sequential test intervals is formulated under the constraint that the probability that the failure has not test successfully within each test interval should be less than a fixed threshold. The failure risk of deteriorating system can be reduced via shortening test interval. The effectiveness of the presented methods is illustrated via a numerical case.

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Diagnosis of Corrosion Processes in Nuclear Power Plants Secondary Piping Structures

Volume 7: Non-Destructive Examination ◽

10.1115/pvp2020-21184 ◽

2020 ◽

Author(s):

Koushik A. Manjunatha ◽

Andrea Mack ◽

Vivek Agarwal ◽

David Koester ◽

Douglas Adams

Keyword(s):

Experimental Test ◽

Classification Accuracy ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Operating Conditions ◽

Condition Based Maintenance ◽

Support Vector ◽

Maintenance Strategy ◽

Test Bed

Abstract The current aging management plans of passive structures in nuclear power plants (NPPs) are based on preventative maintenance strategies. These strategies involve periodic, manual inspection of passive structures using nondestructive examination (NDE) techniques. This manual approach is prone to errors and contributes to high operation and maintenance costs, making it cost prohibitive. To address these concerns, a transition from the current preventive maintenance strategy to a condition-based maintenance strategy is needed. The research presented in this paper develops a condition-based maintenance capability to detect corrosion in secondary piping structures in NPPs. To achieve this, a data-driven methodology is developed and validated for detecting surrogate corrosion processes in piping structures. A scaled-down experimental test bed is developed to evaluate the corrosion process in secondary piping in NPPs. The experimental test bed is instrumented with tri-axial accelerometers. The data collected under different operating conditions is processed using the Hilbert-Huang Transformation. Distributional features of phase information among the accelerometers were used as features in support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression methodologies to detect changes in the pipe condition from its baseline state. SVM classification accuracy averaged 99% for all models. LASSO classification accuracy averaged 99% for all models using the accelerometer data from the X-direction.

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Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models

Energies ◽

10.3390/en13225948 ◽

2020 ◽

Vol 13 (22) ◽

pp. 5948

Author(s):

Renxi Gong ◽

Siqiang Li ◽

Weiyu Peng

Keyword(s):

Decision Making ◽

Vector Space ◽

Assessment System ◽

Condition Based Maintenance ◽

Power Transformers ◽

Maintenance Strategy ◽

Cloud Models ◽

Optimal Maintenance ◽

Maintenance Decision ◽

Vector Space Models

Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which includes the consideration of technology, cost-effectiveness, and security, should be created, taking into account current research findings. In order to address the uncertainty of maintenance strategy selection, this paper proposes a maintenance decision-making model composed of cloud and vector space models. The optimal maintenance strategy is selected in a multivariate assessment system. Cloud models allow for the expression of natural language evaluation information and are used to transform qualitative concepts into quantitative expressions. The subjective and objective weights of the evaluation index are derived from the analytic hierarchy process and the grey relational analysis method, respectively. The kernel vector space model is then used to select the best maintenance strategy through the close degree calculation. Finally, an optimal maintenance strategy is determined. A comparison and analysis of three different representative maintenance strategies resulted in the following findings: The proposed model is effective; it provides a new decision-making method for power transformer maintenance decision-making; it is simple, practical, and easy to combine with the traditional state assessment method, and thus should play a role in transformer fault diagnosis.

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Electrical equipment condition based maintenance strategy

2016 International Conference and Exposition on Electrical and Power Engineering (EPE) ◽

10.1109/icepe.2016.7781439 ◽

2016 ◽

Cited By ~ 2

Author(s):

Evgenii L. Kokorin ◽

Alexandra I. Khalyasmaa

Keyword(s):

Electrical Equipment ◽

Condition Based Maintenance ◽

Maintenance Strategy

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A bi-objective model for vessel emergency maintenance under a condition-based maintenance strategy

SIMULATION ◽

10.1177/0037549717741973 ◽

2017 ◽

Vol 94 (7) ◽

pp. 609-624 ◽

Cited By ~ 4

Author(s):

Jinlou Zhao ◽

Liqian Yang

Keyword(s):

Complex Problem ◽

Spare Parts ◽

Condition Based Maintenance ◽

Maintenance Strategy ◽

Nsga Ii ◽

Maintenance Costs ◽

Inventory Costs ◽

Penalty Costs ◽

Proposed Model ◽

Objective Model

When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship’s machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as ‘vessel emergency maintenance’. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship’s main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.

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