Intelligent Machinery Condition Monitoring based on Adaptive Measurements

Kostyukov, V. N.; Kostyukov, A. V.; Boychenko, S. N.

doi:10.1016/j.proeng.2016.07.650

Cited by 1 publication

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“…Condition monitoring is done through different methods. Each method emphasizes certain indicators of operational condition of a system, measures distinctive parameters, and communicates health of the system [3]. In vibration analysis technique, vibrations of compressor are measured by special sensors.…”

Section: Introductionmentioning

confidence: 99%

Compressor fault diagnosis based on SVM and GA

Golmoradi¹,

Ebrahimi²,

Javidan³

2017

Vib. proced.

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Abstract. Due to growth of mechanization and automation, today's industrial systems are becoming more complex. A small breakdown of any non-redundant machine component affects the operation of the entire system. Compressors are utilized widely in the oil and chemical industry. Great attention has been paid to the condition monitoring and fault diagnosis of the Compressor by the field engineers and technicians. In this study, an effective and reliable method based on vibration analysis and with signal processing and classification techniques is presented for troubleshooting of a centrifugal Compressor. Among different time -frequency methods, wavelet transformation extracts information about the signal time scale through a series of convolution operation between the measured signals and the basis wavelet which was used as a preprocessing. The used mother wavelet is (db4) in which the original signal is switched to multiple details signals; then it features are taken from pre-processed signals and they were introduced to support vector machines as input. Kernel function used here in the support vector machine is RBF in which the parameters of support vector machine were optimized using Genetic Algorithm for better performance to increase the accuracy of classification. The highest accuracy was obtained as 100 %. The obtained accuracy clearly indicates high safety margin of the multistage centrifugal pump for fault detection.

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Section: Introductionmentioning

confidence: 99%