Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Riera-Guasp, M.; Antonino-Daviu, José A.; Capolino, Gérard‐André

doi:10.1109/tie.2014.2375853

Cited by 486 publications

(253 citation statements)

References 100 publications

(158 reference statements)

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“…(1) Original vibration signals are used directly as input of soft-max function; (2) Original vibration signals are used directly as input of the BP network with one hidden layer; (3) Original vibration signals are preprocessed to extract time domain features including mean value, root mean square (RMS) value, shape factor, skewness, kurtosis, impulse factor and crest factor [3], then 7 selected features are used as input of the BP network; (4) 4 features including shape factor, impulse factor, crest factor and kurtosis are used as input of the BP network, (5) Signals are preprocessed with 5-layer wavelet packet decomposition to get 63 sub-frequency bands, then the energy features at all sub-frequency bands are used as input of the BP network.…”

Section: Comparison Approachesmentioning

confidence: 99%

“…Effective diagnosis of these failures is essential in order to enhance reliability and reduce costs for operation and maintenance of the manufacturing equipment. As a result, research on fault diagnosis of manufacturing machines that utilizes data acquired by advanced sensors and makes decisions using processed sensor data has been seen success in various applications [1][2][3]. Induction motors, as the source of actuation, have been widely used in many manufacturing machines, and their working states directly influence system performance, thus affecting the production quality.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Shao

Sun

Yan

et al. 2017

Chin. J. Mech. Eng.

134

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Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-bylayer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

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Section: Comparison Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Shao

Sun

Yan

et al. 2017

Chin. J. Mech. Eng.

134

View full text Add to dashboard Cite

show abstract

“…The envelope of the signal is known to be a common method for identifying impulsive fault signatures, for example due to bearing faults [15], [16].…”

Section: A Feature Extractionmentioning

confidence: 99%

Two Stage Data Fusion of Acoustic, Electric and Vibration Signals for Diagnosing Faults in Induction Motors

Stief¹,

Ottewill²,

Orkisz³

et al. 2017

ElAEE

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Abstract-The increasing demand for predictive maintenance is a main driver of the development of better fault diagnosis algorithms. Each condition monitoring approach has its own strengths and weaknesses; there is not a single technique that can diagnose all types of faults. As a result, it can be a challenge to find the root cause of a problem when only a single feature is monitored. There is also a greater risk of missed-or false-alarms. It has been shown that data fusion, combining multiple features, can improve the effectiveness of fault diagnosis. In recent work, a two-stage Bayesian inference approach, in which data is fused at both a local, or component, level, as well as at a global, or system-wide level has been shown to refine the diagnostic assessment of machinery comprised of a number of interacting components. In this paper, we show that the approach may also be applied to combine information from multiple, diverse condition monitoring systems. Acoustic, electric and vibration signals were measured from healthy and faulty induction motors, operating under normal and noisy working conditions. The proposed method was shown to increase the reliability of the health assessment of the induction motors, reducing the risk of missed and false alarms.

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“…HE area of induction motor fault diagnosis has gained attention over the last two decades [1]- [3]. This is due to the keyrole played by those electrical machines in manufacturing and industrial production processes [4].…”

Section: Introductionmentioning

confidence: 99%

Reliable detection of broken rotor bars in induction motors via MUSIC and ZSC methods

Moriñigo-Sotelo

Romero‐Troncoso

Antonino-Daviu

et al. 2016

2016 XXII International Conference on Electrical Machines (ICEM)

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Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Cited by 486 publications

References 100 publications

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Two Stage Data Fusion of Acoustic, Electric and Vibration Signals for Diagnosing Faults in Induction Motors

Reliable detection of broken rotor bars in induction motors via MUSIC and ZSC methods

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