Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Independent component analysis: algorithms and applications
Neural Networks
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Intelligent Data Mining: Techniques and Applications (Studies in Computational Intelligence)
Intelligent Data Mining: Techniques and Applications (Studies in Computational Intelligence)
Expert Systems with Applications: An International Journal
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Improved kernel principal component analysis for fault detection
Expert Systems with Applications: An International Journal
Markov Models for Pattern Recognition: From Theory to Applications
Markov Models for Pattern Recognition: From Theory to Applications
Expert Systems with Applications: An International Journal
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
ACM Computing Surveys (CSUR)
Fault Detection and Isolation with Robust Principal Component Analysis
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
Expert Systems with Applications: An International Journal
Robust condition monitoring for early detection of broken rotor bars in induction motors
Expert Systems with Applications: An International Journal
Lip reading of hearing impaired persons using HMM
Expert Systems with Applications: An International Journal
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets
Expert Systems with Applications: An International Journal
Support vector machine classifier for diagnosis in electrical machines: Application to broken bar
Expert Systems with Applications: An International Journal
A few useful things to know about machine learning
Communications of the ACM
Expert Systems with Applications: An International Journal
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This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator's three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator's current independently of the motor's load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.