Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCN...

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Veröffentlicht in:	Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-7
Hauptverfasser:	Zhang, Hongli, Chen, Lei-lei, Zhang, Jiang-an, Yang, Hong-bai, Liu, Shulin
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Back propagation networks Bearings Deep learning Fault diagnosis Feature extraction Gas valves International conferences Mathematical problems Methods Neural networks Pattern recognition Principal components analysis Reciprocating compressors Recognition Sensors Vibration Wavelet transforms White noise
Online-Zugang:	Volltext
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