Nonlinear learning approach to robust fault diagnosis

Author(s):  
A. T. Vemuri ◽  
M. M. Polycarpou
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 66595-66608 ◽  
Author(s):  
Tiancheng Shi ◽  
Yigang He ◽  
Tao Wang ◽  
Bing Li

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125662-125675 ◽  
Author(s):  
Yueping Wang ◽  
Kun Zhu ◽  
Mengyun Sun ◽  
Yueyu Deng

1998 ◽  
Vol 22 (1-2) ◽  
pp. 299-321 ◽  
Author(s):  
B. Özyurt ◽  
A.K. Sunol ◽  
M.C. Çamurdan ◽  
P. Mogili ◽  
L.O. Hall

Sign in / Sign up

Export Citation Format

Share Document