Author： Paya B.A. Esat I.I. Badi M.N.M.
Publisher： Academic Press
ISSN： 0888-3270
Source： Mechanical Systems and Signal Processing, Vol.11, Iss.5, 1997-09, pp. : 751-765
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce operational and maintenance costs and improve the level of safety. The condition of a model drive-line, consisting of various interconnected rotating parts, including an actual vehicle gearbox, two bearing housings, and an electric motor, all connected via flexible couplings and loaded by a disc brake, was investigated. This model drive-line was run in its normal condition, and then single and multiple faults were introduced intentionally to the gearbox, and to the one of the bearing housings. These single and multiple faults studied on the drive-line were typical bearing and gear faults which may develop during normal and continuous operation of this kind of rotating machinery. This paper presents the investigation carried out in order to study both bearing and gear faults introduced first separately as a single fault and then together as multiple faults to the drive-line. The real time domain vibration signals obtained for the drive-line were preprocessed by wavelet transforms for the neural network to perform fault detection and identify the exact kinds of fault occurring in the model drive-line. It is shown that by using multilayer artificial neural networks on the sets of preprocessed data by wavelet transforms, single and multiple faults were successfully detected and classified into distinct groups.
Related content
ART-KOHONEN neural network for fault diagnosis of rotating machinery
Mechanical Systems and Signal Processing, Vol. 18, Iss. 3, 2004-05 ,pp. :
Access to resources
Recommend
Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network
Applied Mechanics and Materials, Vol. 2014, Iss. 722, 2015-03 ,pp. :
Fault Diagnosis Based on Vibration Signal of Rotating Machinery
Applied Mechanics and Materials, Vol. 2014, Iss. 716, 2015-02 ,pp. :
Multi-Fault Diagnosis of Rotating Machinery Based on Spatially Constrained ICA
Applied Mechanics and Materials, Vol. 2015, Iss. 799, 2015-11 ,pp. :