A condition monitoring tool based on a FMECA and FMMEA combined approach in Oil&Gas applications

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

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The use of line current as a condition monitoring tool for three phase induction motors

10.1049/ic:19990822 ◽

1999 ◽

Cited By ~ 2

Author(s):

J.F. Watson

Keyword(s):

Condition Monitoring ◽

Induction Motors ◽

Monitoring Tool ◽

Line Current ◽

Three Phase

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Practice and understanding of service condition monitoring for down-hole string in oil-gas wells

Procedia Engineering ◽

10.1016/j.proeng.2011.12.628 ◽

2012 ◽

Vol 27 ◽

pp. 1609-1617 ◽

Cited By ~ 1

Author(s):

Quan’an Yang ◽

Zewei Tang

Keyword(s):

Condition Monitoring ◽

Service Condition ◽

Gas Wells ◽

Oil Gas

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Intelligent Configuration of Condition Monitoring Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.794.355 ◽

2015 ◽

Vol 794 ◽

pp. 355-362 ◽

Cited By ~ 5

Author(s):

Abdelhakim Laghmouchi ◽

Eckhard Hohwieler ◽

Claudio Geisert ◽

Eckart Uhlmann

Keyword(s):

Embedded System ◽

Condition Monitoring ◽

Development Process ◽

Modular Design ◽

Production Systems ◽

Raspberry Pi ◽

Operating Parameters ◽

Monitoring Tool ◽

Test Rig ◽

Mems Sensor

The aim of this paper is to present the design of a condition monitoring tool, its use for the intelligent configuration of pattern recognition algorithms, for fault detection, and for diagnosis applications. The modular design and functionality of the tool will be introduced. The tool, developed and implemented by Fraunhofer IPK, can be used, in particular, to support the development process of algorithms for condition monitoring of wear-susceptible components in production systems. An example of the industrial application of the tool will be presented. This will include the implementation of configured algorithms using the tool on an embedded system using Raspberry Pi 2 and MEMS sensor. Finally, the evaluation of these algorithms on an axis test rig at different operating parameters will be presented.

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