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Fault diagnosis of helical gearbox using acoustic signal and wavelets
| Content Provider | Scilit |
|---|---|
| Author | Pranesh, Sk Abraham, Siju Sugumaran, V. Amarnath, M. |
| Copyright Year | 2017 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study |
| Related Links | http://iopscience.iop.org/article/10.1088/1757-899X/197/1/012079/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/197/1/012079 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Volume Number | 197 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2017-05-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Computer Science Fault Diagnosis Acoustic Signals Condition Monitoring Helical Gearbox Feature Selection Decision Tree |
| Content Type | Text |
| Resource Type | Article |
