scholarly journals A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Mansoor Ahmed Siddiqui ◽  
Shahid Ikramullah Butt ◽  
Aamer Ahmed Baqai ◽  
Jiping Lu ◽  
Faping Zhang
Keyword(s):  
Prognostic Model ◽  
Condition Based Maintenance ◽  
Search Process ◽  
Maintenance Strategies ◽  
Event Simulation ◽  
Simulation Techniques ◽  
Wide Range ◽  
Direct Linkage ◽  
Mc Simulation ◽  
Synergic Model

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions.

Rare event simulation

2017 ◽  
Author(s):  
Michael P. Allen ◽  
Dominic J. Tildesley
Keyword(s):  
Rare Event ◽  
Path Sampling ◽  
Rapid Progress ◽  
Computationally Efficient ◽  
Transition Path ◽  
Transition Path Sampling ◽  
Event Simulation ◽  
Reaction Coordinates ◽  
Simulation Techniques ◽  
Transition Interface

The development of techniques to simulate infrequent events has been an area of rapid progress in recent years. In this chapter, we shall discuss some of the simulation techniques developed to study the dynamics of rare events. A basic summary of the statistical mechanics of barrier crossing is followed by a discussion of approaches based on the identification of reaction coordinates, and those which seek to avoid prior assumptions about the transition path. The demanding technique of transition path sampling is introduced and forward flux sampling and transition interface sampling are considered as rigorous but computationally efficient approaches.

scholarly journals Tribology and Maintenance of Machinery – A Review

2021 ◽  
pp. 16-20
Author(s):  
Chandan Chattoraj ◽  
Keyword(s):  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Maintenance Strategies ◽  
Cost Benefits ◽  
Maintenance Practice ◽  
Useful Life ◽  
The Cost

The present paper considers the tribological principles on the maintenance of machinery whose three important areas are – Preventive, Condition Based and Proactive. Although breakdown is kept out of view, the morphology and analysis of failure provide important inputs for maintenance strategies. Condition based maintenance depends on three D’s – Detection, Diagnosis and Decision. In many machinery systems, the problem of predicting the remaining useful life – the Proactive part of the program, and evaluating the cost benefits are of enormous importance. Here the authors endeavor to highlight how the tribologist can significantly improve the maintenance practice.

scholarly journals Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

2021 ◽  
Vol 251 ◽  
pp. 03055
Author(s):  
John Blue ◽  
Braden Kronheim ◽  
Michelle Kuchera ◽  
Raghuram Ramanujan
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Event Simulation ◽  
Simulation Process ◽  
Adversarial Networks ◽  
Wide Range ◽  
Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

A Comparison of Self-Rectifying Turbines for OWC Based Wave Energy Power Extracting Device Using Numerical Simulation

2002 ◽  
Author(s):  
A. R. Ansari ◽  
H. B. Khaleeq ◽  
A. Thakker
Keyword(s):  
Numerical Simulation ◽  
Wave Energy ◽  
Sea Water ◽  
Time History ◽  
Flow Conditions ◽  
Wells Turbine ◽  
Impulse Turbine ◽  
Simulation Techniques ◽  
Simple Geometry ◽  
Wide Range

This paper presents a comparison of self-rectifying turbines for the Oscillating Water Column (OWC) based Wave Energy power extracting device using numerical simulation. The two most commonly used turbines for OWC based devices, the Impulse and the Wells turbines were evaluated under real sea simulated conditions. Assuming the quasi-steady condition, experimental data for both 0.6m turbines with 0.6 hub to tip ratio was used to predict their behavior under real sea conditions. The real sea water surface elevation time history data was used to simulate the flow conditions using standard numerical simulation techniques. A simple geometry of the OWC was considered for the simulation. The results show that the overall mean performance of an Impulse turbine is better than the Wells turbine under unsteady, irregular real sea conditions. The Impulse turbine was observed to be more stable over a wide range of flow conditions. This paper reports the comparison of performance characteristics of both these turbines under simulated real sea conditions.

Maintenance Strategy Optimization for Computer Numerical Control Machine Tools

2015 ◽  
Vol 809-810 ◽  
pp. 1504-1509 ◽  
Author(s):  
Ana Lacramioara Ungureanu ◽  
Gheorghe Stan ◽  
Paul Alin Butunoi
Keyword(s):  
Machine Tools ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Condition Based Maintenance ◽  
Financial Impact ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Maintenance Strategies ◽  
Time Period ◽  
A Company

In this paper are proposed two new approaches to maintenance strategies for Computer Numerical Control (CNC) machine tools. The analysis is done for different families of CNC machine tools from S.C. Elmet Bacau, a company specialized in aviation. In maintenance actions applied to CNC machine tools is very important to know the evolution of defects and critical state of electrical and mechanical components. The results of this analysis concludes that maintenance actions can be judged by the developing time period diagram, between failure appearance and interruptions in operation. It is also analyzed the financial impact, revealed from known maintenance strategies adopted on CNC machine tools, resulting in a positive approach of condition based maintenance.

scholarly journals Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4849
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Ana Sanchez ◽  
Tugce Akyazi ◽  
Elisabete Alberdi
Keyword(s):  
Preventive Intervention ◽  
Discrete Event ◽  
Condition Based Maintenance ◽  
Optimization Approach ◽  
Actual System ◽  
Multi Objective ◽  
Event Simulation ◽  
Industrial Chain ◽  
Efficiency And Effectiveness ◽  
Optimal Values

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

scholarly journals Surface contamination of cars: A review

2017 ◽  
Vol 231 (9) ◽  
pp. 1160-1176 ◽  
Author(s):  
Adrian P Gaylard ◽  
Kerry Kirwan ◽  
Duncan A Lockerby
Keyword(s):  
Numerical Simulation ◽  
Surface Water ◽  
Automotive Industry ◽  
Test Methods ◽  
Surface Contamination ◽  
Front Side ◽  
Water Droplets ◽  
Simulation Techniques ◽  
Wide Range ◽  
Safety Systems

This review surveys the problem of surface contamination of cars, which poses a growing engineering challenge to vehicle manufacturers, operators and users. Both the vision of drivers and the visibility of vehicles need to be maintained under a wide range of environmental conditions. This requires managing the flow of surface water on windscreens and side glazing. The rate of deposition of solid contaminants on glazing, lights, licence plates and external mirrors also needs to be minimised. Maintaining vehicle aesthetics and limiting the transfer of contaminants to the hands and clothes of users from soiled surfaces are also significant issues. Recently, keeping camera lenses clean has emerged as a key concern, as these systems transition from occasional manoeuvring aids to sensors for safety systems. The deposition of water and solid contaminants on to car surfaces is strongly influenced by unsteady vehicle aerodynamic effects. Airborne water droplets falling as rain or lifted as spray by tyres interact with wakes, vortices and shear flows and accumulate on vehicle surfaces as a consequence. The same aerodynamic effects also control the movement of surface water droplets, rivulets and films; hence, particular attention is paid to the management of surface water over the front side glass and the deposition of contaminants on the rear surfaces. The test methods used in the automotive industry are reviewed, as are the numerical simulation techniques.

Particle Resuspension in a Wall-Bounded Turbulent Flow

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
S. C. Fu ◽  
C. Y. H. Chao ◽  
R. M. C. So ◽  
W. T. Leung
Keyword(s):  
Angular Velocity ◽  
Uhlenbeck Process ◽  
Particle Resuspension ◽  
Air Stream ◽  
Safety And Health ◽  
Wide Range ◽  
Resuspension Rate ◽  
Mc Simulation ◽  
Using Data ◽  
Good Agreement

Resuspension is of common occurrence in a wide range of industrial and environmental processes. Excessive resuspension in these processes could have a severe impact on human safety and health. Therefore, it is necessary to develop a practical, yet reasonably accurate model to describe the resuspension phenomenon. It has been identified that rolling is the dominant mechanism for particle resuspension in the presence of an air stream, be it laminar or turbulent. Existing models predict the resuspension rate by regarding particles as being resuspended once they are set in motion; only a few of these models attempt to describe the full scenario, including rolling motion and the effect of turbulence. The objective of this paper is to propose a stochastic model to simulate the resuspension rate in the presence of a near-wall turbulent stream, and where the rolling mechanism is assumed to dominate the resuspension process. The fluctuating part of the angular velocity of a rolling particle is modeled by the Langevin equation (i.e., an Ornstein–Uhlenbeck process); thus, the overall angular velocity is modeled as a diffusion process. A free parameter of the proposed resuspension model is determined using data obtained from a Monte Carlo (MC) simulation of the problem. Once determined, the parameter is found to be universal for different materials and different sizes of particles tested. The modeling results obtained using this parameter are found to be in good agreement with experimental data, and the model performs better compared to other models.

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