scholarly journals Value of condition monitoring information for maintenance decision-making

2010 ◽  
Author(s):  
Tuan K. Huynh ◽  
Anne Barros ◽  
Christophe Berenguer ◽  
Inmaculada T. Castro
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Maintenance Decision ◽  
Monitoring Information

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.

scholarly journals Performance Reliability Prediction of Complex System Based on the Condition Monitoring Information

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hongxing Wang ◽  
Yu Jiang
Keyword(s):  
Complex System ◽  
Condition Monitoring ◽  
System Performance ◽  
Aircraft Engine ◽  
Reliability Prediction ◽  
Support Vector ◽  
Reliability Level ◽  
Maintenance Decision ◽  
Input Variables ◽  
Monitoring Information

Complex system performance reliability prediction is one of the means to understand complex systems reliability level, make maintenance decision, and guarantee the safety of operation. By the use of complex system condition monitoring information and condition monitoring information based on support vector machine, the paper aims to provide an evaluation of the degradation of complex system performance. With degradation assessment results as input variables, the prediction model of reliability is established in Winer random process. Taking the aircraft engine as an example, the effectiveness of the proposed method is verified in the paper.

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 Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

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.

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