Chemical process fault diagnosis based on enchanted machine‐learning approach

2019 ◽  
Vol 97 (12) ◽  
pp. 3074-3086 ◽  
Author(s):  
Xiaohui Yang ◽  
Jingsong Zhou ◽  
Zihao Xie ◽  
Gong Ke
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Chemical Process ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Process Fault Diagnosis

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.

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 Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals

2019 ◽  
Vol 8 (2S11) ◽  
pp. 320-331
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Learning Approach ◽  
Feature Classification ◽  
Rule Based ◽  
Machine Learning Approach

The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.”

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