RETRACTED: Design of artificial neural networks for rotor dynamics analysis of rotating machine systems

Kalkat, Menderes; Yıldırım, Şahin; Uzmay, İbrahim

doi:10.1016/j.mechatronics.2004.11.002

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…ANNs are computational models of the human brain and widely used in modelling mental activities -especially learning processes and classification tasks -in many different areas of social and physical sciences and, of course, in engineering, where, during the last decades, ANNs have proven their efficiency in solving complex problems. For the latter, the literature provides several applications [22][23][24][25][26]. ANNs imitate the biological nervous systems and, more specifically, the structure of the human brain [27].…”

Section: Online Monitoring Using Annsmentioning

confidence: 99%

Identification of wear and misalignment on journal bearings using artificial neural networks

Saridakis

Nikolakopoulos

Papadopoulos

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part J:

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Journal bearing bushing wear and shaft misalignment are the common defects observed in rotating systems. Both defects influence their dynamic behaviour, stability and life time and their detection is a very important task. This article introduces a defect diagnosis approach that uses artificial neural networks. Reynolds equation is solved by finite element method and wear/misalignment data are provided. Then, the proposed model uses values of journal bearing performance characteristics, such as eccentricity, attitude angle and film thickness, and it trains an artificial neural network so that reliable defect identification is performed. The accuracy of the proposed model is demonstrated -for different misalignment angles, wear depths and L/D ratios -for a worn/misaligned journal bearing. The potential use of the artificial neural networks-based model for a real-time condition monitoring system is also discussed. The outcome of this study is to obtain an efficient engineering tool that would be capable of detecting wear and misalignment of journal bearings for different operating conditions using artificial neural networks.

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Section: Online Monitoring Using Annsmentioning

confidence: 99%

Identification of wear and misalignment on journal bearings using artificial neural networks

Saridakis

Nikolakopoulos

Papadopoulos

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part J:

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“…Machinery monitoring is of utmost importance in modern industries as it can enhance machines reliability and decrease the loss of production because of damage caused by different defects. Fault diagnosis of rolling element bearings using artificial neural networks (ANNs) [1][2][3][4], back-propagation neural network [5,6], hybrid neural network [7][8][9][10], support vector machines [11,12], wavelet [13,14] and empirical mode decomposition analysis [15][16][17][18] there are methods that provide abundant information about machine faults and have been widely used to detect bearing faults. Rolling bearings being the important components in machinery are extensively used in the electric motors.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Helmi¹,

Forouzantabar²

2019

IET Electric Power Applications

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Demands for various products, higher qualities, reduction of costs and competitiveness, have resulted in the use of intelligent fault detection systems. Bearing fault diagnosis as a major component of the electric motors has had an essential role in the operation of production units' reliability. In addition, vibration analysis is one of the most powerful tools in diagnostics. Advances in signal processing technology and electrical equipment have developed a machinery condition monitoring for defect detection. This study has used the extracted features of vibration signals and the adaptive neuro-fuzzy interface system (ANFIS) network proposing a structure for fault detection and diagnosis of rolling bearings. Time-domain and frequency-domain statistical characteristics have been extracted fault information from vibration signals. Besides, the test data sets are presented to the ANFIS network. Simulation results indicated that the performance of the ANFIS network is acceptable. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.

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“…An air crash was caused by bearing failure resulting from damage to the cage supporting the bearing balls (Smalley, Baldwin, Mauney, & Millwater, 1996). The reports (Kalkat, Yildirim, & Uzmay, 2005;Sun, Chen, & Li, 2007) have clearly described the fault diagnosis of rotational mechanical system which focused on faulty shaft. Vibration-based monitoring techniques, both in the time and frequency domains, have been widely used for detection and diagnosis of bearing defects for several decades.…”

Section: Introductionmentioning

confidence: 99%

Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

Saimurugan

Ramachandran

Sugumaran

et al. 2011

Expert Systems with Applications

167

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RETRACTED: Design of artificial neural networks for rotor dynamics analysis of rotating machine systems

Cited by 10 publications

References 10 publications

Identification of wear and misalignment on journal bearings using artificial neural networks

Identification of wear and misalignment on journal bearings using artificial neural networks

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

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