scholarly journals Fault diagnosis studies of face milling cutter using machine learning approach

2016 ◽  
Vol 35 (2) ◽  
pp. 128-138 ◽  
Author(s):  
CK Madhusudana ◽  
S Budati ◽  
N Gangadhar ◽  
H Kumar ◽  
S Narendranath
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Face Milling ◽  
Learning Approach ◽  
Milling Cutter ◽  
Machine Learning Approach

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

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.

Chemical plant fault diagnosis through a hybrid symbolic-connectionist machine learning approach

1998 ◽  
Vol 22 (1-2) ◽  
pp. 299-321 ◽  
Author(s):  
B. Özyurt ◽  
A.K. Sunol ◽  
M.C. Çamurdan ◽  
P. Mogili ◽  
L.O. Hall
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Chemical Plant ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for Gearbox System Fault Diagnosis

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1130
Author(s):  
Jan Vrba ◽  
Matous Cejnek ◽  
Jakub Steinbach ◽  
Zuzana Krbcova
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Prediction Error ◽  
Support Vector ◽  
Learning Approach ◽  
Reference Methods ◽  
Machine Learning Approach ◽  
Public Dataset ◽  
Diagnosis Approach ◽  
Better Than

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.

Sign in / Sign up

Export Citation Format

Share Document