Journal ArticleDOI
Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals
TLDR
In this article, an early fault diagnostic technique based on acoustic signals was used for a single-phase induction motor, which can be also used for other types of rotating electric motors.About:
This article is published in Measurement.The article was published on 2018-01-01. It has received 286 citations till now. The article focuses on the topics: Induction motor & Electric motor.read more
Citations
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Journal ArticleDOI
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
TL;DR: By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, it is found that these kernels act as filters and they become complex when the layers go deeper, which may help to understand what DNCNN has learned in intelligent fault diagnosis of machinery.
Journal ArticleDOI
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Purushottam Gangsar,Rajiv Tiwari +1 more
TL;DR: Overall, this paper includes review of system signals, conventional and advance signal processing techniques; however, it mainly covers, the selection of effective statistical features, AI methods, and associated training and testing strategies for fault diagnostics of IMs.
Journal ArticleDOI
A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing
TL;DR: In order to make the filtering results of Condition Monitoring (CM) data smoother and avoid misjudgment of status when the degradation speed is negative, the measurement error parameter is selected as the standard deviation of CM data in the degradation stage.
Journal ArticleDOI
Acoustic based fault diagnosis of three-phase induction motor
TL;DR: The Nearest Neighbour classifier, backpropagation neural network and modified classifier based on words coding were used for recognition of acoustic signals and developed fault diagnosis techniques based on acoustic signals that can find applications in the industry.
Journal ArticleDOI
A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection
TL;DR: A practical framework to detect termites nondestructively by using the acoustic signals extraction is proposed, which has the pros to maintain the quality of wood products and prevent higher termite attacks.
References
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Journal ArticleDOI
Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques
Humberto Henao,Gerard-Andre Capolino,Manes Fernandez-Cabanas,Fiorenzo Filippetti,Claudio Bruzzese,Elias G. Strangas,Remus Pusca,Jorge O. Estima,Martin Riera-Guasp,Shahin Hedayati-Kia +9 more
TL;DR: The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years as discussed by the authors, and this topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years.
Journal ArticleDOI
Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis
TL;DR: The method is able to detect incipient faults and diagnose the locations of faults under masking noise, and provides a health index that tracks the degradation of faults without missing intermittent faults.
Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background
TL;DR: In this article, the k-Nearest Neighbor (kNN) classification method has been used for economic forecasting in Iran and the results showed that kNN is more capable than other methods.
Journal ArticleDOI
Methodology for fault detection in induction motors via sound and vibration signals
Paulo Antonio Delgado-Arredondo,Daniel Morinigo-Sotelo,Roque Alfredo Osornio-Rios,Juan Gabriel Avina-Cervantes,Horacio Rostro-Gonzalez,Rene de Jesus Romero-Troncoso +5 more
TL;DR: In this article, the authors proposed a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals, using the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions.
Journal ArticleDOI
Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation
TL;DR: In this paper, a modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes, which is based on optimizing the ratio of Kurtosis and Shannon entropy.