scholarly journals Maintenance Decision-Making for Systems Operating Under Indirect Condition Monitoring: Value of Online Information and Impact of Measurement Uncertainty

2012 ◽  
Vol 61 (2) ◽  
pp. 410-425 ◽  
Author(s):  
Khac Tuan Huynh ◽  
Anne Barros ◽  
Christophe Berenguer
Keyword(s):  
Decision Making ◽  
Measurement Uncertainty ◽  
Condition Monitoring ◽  
Online Information ◽  
Maintenance Decision

scholarly journals A DECISION TREE-BASED CLASSIFICATION FRAMEWORK FOR USED OIL ANALYSIS APPLYING RANDOM FOREST FEATURE SELECTION

2018 ◽  
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Random Forest ◽  
Decision Tree ◽  
Condition Monitoring ◽  
Critical Parameters ◽  
Oil Analysis ◽  
Used Oil ◽  
Maintenance Decision

Lubricant condition monitoring (LCM), part of condition monitoring techniques under Condition Based Maintenance, monitors the condition and state of the lubricant which reveal the condition and state of the equipment. LCM has proved and evidenced to represent a key concept driving maintenance decision making involving sizeable number of parameter (variables) tests requiring classification and interpretation based on the lubricant’s condition. Reduction of the variables to a manageable and admissible level and utilization for prediction is key to ensuring optimization of equipment performance and lubricant condition. This study advances a methodology on feature selection and predictive modelling of in-service oil analysis data to assist in maintenance decision making of critical equipment. Proposed methodology includes data pre-processing involving cleaning, expert assessment and standardization due to the different measurement scales. Limits provided by the Original Equipment Manufacturers (OEM) are used by the analysts to manually classify and indicate samples with significant lubricant deterioration. In the last part of the methodology, Random Forest (RF) is used as a feature selection tool and a Decision Tree-based (DT) classification of the in-service oil samples. A case study of a thermal power plant is advanced, to which the framework is applied. The selection of admissible variables using Random Forest exposes critical used oil analysis (UOA) variables indicative of lubricant/machine degradation, while DT model, besides predicting the classification of samples, offers visual interpretability of parametric impact to the classification outcome. The model evaluation returned acceptable predictive, while the framework renders speedy classification with insights for maintenance decision making, thus ensuring timely interventions. Moreover, the framework highlights critical and relevant oil analysis parameters that are indicative of lubricant degradation; hence, by addressing such critical parameters, organizations can better enhance the reliability of their critical operable equipment.

THE RESEARCH OF RISK- AND CONDITION-BASED MAINTENANCE DECISION-MAKING AND TASK OPTIMIZING SYSTEM FOR ROTATING EQUIPMENT IN LARGE PETROCHEMICAL PLANTS

2012 ◽  
Vol 19 (04) ◽  
pp. 1250017
Author(s):  
QINGBIN YUAN ◽  
QINGFENG WANG ◽  
JINJI GAO
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Condition Monitoring ◽  
Service Oriented Architecture ◽  
Condition Based Maintenance ◽  
Decision Making System ◽  
Maintenance Decision ◽  
Rotating Equipment ◽  
The Right ◽  
And Task

Downtime of rotating equipment in large petrochemical plants often led to serious or even disastrous safety and environmental accidents, which generally stem from inadequate maintenance or incapability of failure prediction. In order to allocate maintenance resources rationally and improve the reliability, availability and safety of equipment, a kind of risk- and condition-based maintenance decision-making and task optimizing system for rotating equipment in large petrochemical plants is established in this paper. Using real-time database, web service and service-oriented architecture (SOA), a risk- and condition-based maintenance decision-making system architecture is developed to provide a unified data structure and man–machine interface, which integrates reliability-centered maintenance (RCM), condition monitoring system (CMS) and manufacturing executive system (MES) together. Risk assessment and condition monitoring technology is applied to form maintenance decision making, such as to determine the priority maintenance level, to optimize maintenance content, and to determine the right maintenance time. Based on the decision-making system, the risk rank and degradation trend of failure characteristics are used to support the decision making and to optimize maintenance tasks. The result of an engineering case shows that the maintenance decision-making based on the risk assessment and condition monitoring can lower the operational risk while enhancing the reliability, availability and safety.

scholarly journals Bayesian maintenance decision optimisation based on computing the information value from condition inspections

2021 ◽  
pp. 1748006X2097812
Author(s):  
Guang Zou ◽  
Kian Banisoleiman ◽  
Arturo González
Keyword(s):  
Decision Making ◽  
Structural Integrity ◽  
Fatigue Cracking ◽  
Cost Effective ◽  
Offshore Structures ◽  
Information Value ◽  
Bayesian Decision ◽  
Maintenance Strategies ◽  
Optimal Maintenance ◽  
Maintenance Decision

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

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