Journal ArticleDOI
Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique
TLDR
A new approach for fault detection and diagnosis of IMs using signal-based method based on signal processing and an unsupervised classification technique called the artificial ant clustering is described, which proves the efficiency of the approach compared with supervised classification methods in condition monitoring of electrical machines.Abstract:
The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.read more
Citations
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Journal ArticleDOI
A review on the application of deep learning in system health management
Samir Khan,Takehisa Yairi +1 more
TL;DR: This article presents a systematic review of artificial intelligence based system health management with an emphasis on recent trends of deep learning within the field and demonstrates plausible benefits for fault diagnosis and prognostics.
Journal ArticleDOI
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches
TL;DR: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints.
Journal ArticleDOI
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
TL;DR: The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.
Journal ArticleDOI
Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art
TL;DR: An analysis of the state of the art in this field of electrical machines and drives condition monitoring and fault diagnosis is presented.
Journal ArticleDOI
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
TL;DR: A novel convolutional deep belief network (CDBN) is proposed for bearing fault diagnosis with an auto-encoder used to compress data and reduce the dimension and exponential moving average is employed to improve the performance of the constructed deep model.
References
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ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Survey of clustering algorithms
Rui Xu,Donald C. Wunsch +1 more
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Book
Robust Model-Based Fault Diagnosis for Dynamic Systems
Jie Chen,Ron J. Patton +1 more
TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
Journal ArticleDOI
Fuzzy Model Identification Based on Cluster Estimation
TL;DR: An efficient method for estimating cluster centers of numerical data that can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means is presented.