scholarly journals Intelligent Condition Monitoring Models For Rotating

2021 ◽  
Author(s):  
Kamyar Rashidi
Keyword(s):  
Condition Monitoring ◽  
Risk Function ◽  
Maintenance Cost ◽  
Machine Condition ◽  
Bayesian Decision ◽  
Noise Levels ◽  
Vibration Signals ◽  
Conditional Risk ◽  
Rotating Machine ◽  
Monitoring Model

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.

scholarly journals Intelligent Condition Monitoring Models For Rotating

2021 ◽  
Author(s):  
Kamyar Rashidi
Keyword(s):  
Condition Monitoring ◽  
Risk Function ◽  
Maintenance Cost ◽  
Machine Condition ◽  
Bayesian Decision ◽  
Noise Levels ◽  
Vibration Signals ◽  
Conditional Risk ◽  
Rotating Machine ◽  
Monitoring Model

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

2013 ◽  
pp. 158-172 ◽  
Author(s):  
Cyprian F. Ngolah ◽  
Ed Morden ◽  
Yingxu Wang
Keyword(s):  
Machine Condition ◽  
Vibration Signals ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Operators ◽  
System Vibration ◽  
Machine Health ◽  
Condition Based Monitoring ◽  
Industrial Machine ◽  
Previous Training

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

An Object-Oriented Machine Condition Monitoring Scheme for Rotating Machinery

1997 ◽  
Author(s):  
Bernhard P. Bettig ◽  
Ray P. S. Han
Keyword(s):  
Condition Monitoring ◽  
Numerical Models ◽  
Object Oriented ◽  
Rotating Machinery ◽  
Machine Condition ◽  
Rotating Machine ◽  
Machine Condition Monitoring ◽  
Framework Approach ◽  
Bearing Stiffness ◽  
User Friendly

Abstract The increasing reliability and accuracy of sensors as well as improvements in data acquisition and display have lead to the predictive maintenance method of scheduling machinery overhauls and replacement. Numerical models of the dynamics of a machine may be used to more accurately predict and schedule maintenance by relating variables describing deterioration mechanisms to machine measurements. For instance, vibration measurements may be used to determine bearing stiffness which is then trended. The present paper describes the concepts of a program to interactively examine and predict the health of a rotating machine. To facilitate a user-friendly implementation of the method, the paper also presents an object-oriented framework approach for introducing GUI-based graphics.

scholarly journals Structural health monitoring: Closing the gap between research and industrial deployment

2018 ◽  
Vol 17 (5) ◽  
pp. 1225-1244 ◽  
Author(s):  
Peter Cawley
Keyword(s):  
Structural Health Monitoring ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Research Effort ◽  
Industrial Applications ◽  
Machine Condition ◽  
Large Area ◽  
Structural Health ◽  
Rotating Machine ◽  
Machine Condition Monitoring

There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there are many fewer industrial applications. Reasons for the slow transfer from research to practical application of structural health monitoring include lack of attention to the business case for monitoring, insufficient attention to how the large data flows will be handled and the lack of performance validation on real structures in industrial environments. These issues are discussed and ways forward proposed; it is concluded that given better focused research and development considering the key factors identified here, structural health monitoring has the potential to follow the path of rotating machine condition monitoring and become a widely deployed technology.

An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks

2013 ◽  
Vol 9 (4) ◽  
pp. 43-62 ◽  
Author(s):  
K. Jenab ◽  
K. Rashidi ◽  
S. Moslehpour
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Operating Performance ◽  
Condition Based Maintenance ◽  
Maintenance Cost ◽  
Production Loss ◽  
Noise Levels ◽  
Vibration Signals ◽  
Maintenance Service ◽  
Artificial Neural

This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.

scholarly journals Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Preventive Maintenance ◽  
False Negative ◽  
Maintenance Cost ◽  
Arbitrary Distribution ◽  
Average Lifetime ◽  
True Negative ◽  
Degradation Failure ◽  
Lifetime Maintenance

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

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