Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN
| Autor: | Noor Mohammed, D. Rama Prabha, Razia Sultana Wahab, N. Senthilnathan, T. Narendiranath Babu, Shailesh Pancholi, S.P. Nikhil Kumar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology Computer science General Engineering 02 engineering and technology Fault (power engineering) 020901 industrial engineering & automation Wavelet decomposition Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Fault analysis Algorithm Continuously variable transmission |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:1297-1307 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-210199 |
| Popis: | This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |