Fault diagnosis of pneumatic systems with artificial neural network algorithms

Demetgül, Mustafa; Tansel, İbrahim N.; Taşkın, Sezai

doi:10.1016/j.eswa.2009.01.028

Cited by 72 publications

(30 citation statements)

References 23 publications

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“…We found no papers in the maintenance area but fault diagnosis remains a very popular area of application of NNs. There are very many examples including fault diagnosis of pneumatic systems (Demetgul et al 2009), automotive generators ), engines (Wu & Liu 2008), solar thermal applications (Kalogirou et al 2008) and a suck rod pumping system (Xu et al 2007). …”

Section: Neural Network In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Neural Network In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Independent usage of artificial neural networks and fuzzy logic algorithm for FD&D can see in a vast area of researches [3][4][5]. But now more interest are on neuro-fuzzy networks and vast researches are done on it.…”

Section: Introductionmentioning

confidence: 99%

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

Shabanian

Montazeri²

2011

Int. J. Control Autom. Syst.

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This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To diagnose the type, size, time and fault conditions, are used analytical approach and neural network for tracking fault developing online. The neuro-fuzzy nets are compared with some other identification methods in application of power plant gas turbine. Faults are considered in two forms, step, and ramp shape. This work was implemented with real data from gas turbine of Kazeroun (Iran) power plant (Mitsubishi unit) and result is presented to demonstrate the benefits of the proposed method.

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“…The classifier is based on statistical learning theory, and it can obtain the good recognition rate derived from fewer training samples than the neural network classifier [31]. Kernel function is a key parameter for SVM, which includes linear, polynomial, Gaussian RBF, and sigmoid.…”

Section: Bolt Loosening Damage Evaluation Agent (Bl Dea)mentioning

confidence: 99%

Mobile Multi-Agent Evaluation Method for Wireless Sensor Networks-Based Large-Scale Structural Health Monitoring

Yuan

Liang

Qiu

et al. 2012

International Journal of Distributed Sensor Networks

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Wireless sensor networks are increasingly adopted both in aircraft and civil structural health monitoring. However, due to the limited resources on a wireless sensor node, structural evaluation ability of the wireless sensor network is also limited, especially for large-scale structures. This paper presents a multi-agent cooperation system based on mobile agent technology for wireless sensor network-based structural health monitoring which deals with the evaluation of large-scale structure. Mobile agent with the signal processing or evaluation algorithm is designed to migrate among different sensor nodes and perform network selforganization, data processing, information fusion, and damage estimation. This method can reduce the request to the sensor nodes and achieve a better monitoring and evaluation performance through multiagent cooperation. The proposed mobile multiagent system is evaluated by a large dimension aerospace aluminum structure monitoring experiment in which strain distribution and joint failure are monitored by different sensors. Experimental results show that the system can significantly improve the monitoring performance for large-scale structures.

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Fault diagnosis of pneumatic systems with artificial neural network algorithms

Cited by 72 publications

References 23 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

Mobile Multi-Agent Evaluation Method for Wireless Sensor Networks-Based Large-Scale Structural Health Monitoring

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