Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars

Rangel-Magdaleno, José; Peregrina-Barreto, Hayde; Ramírez-Cortés, Juan Manuel; Cruz-Vega, Israel

doi:10.1016/j.measurement.2017.05.070

Cited by 62 publications

(34 citation statements)

References 28 publications

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“…As a result, current and induced electromagnetic signal are found at different slip frequencies. In addition, due to rotor oscillation, there exits an upper sideband current component in the stator winding [14]. Considering all these effects, sideband frequencies sb f due to broken rotor bars will be ( )…”

Section: The Methodologymentioning

confidence: 99%

“…Empirical mode decomposition (EMD) can solve several of these problems [13]. The EMD decomposes the signal into different frequency bands, known as intrinsic mode function (IMF), to process non-stationary signals for IM fault detection [14] [15]. However, when the signal length becomes longer, EMD generates extra number of IMFs due to the spline interpolation, which will not only increase processing time [16] [17], but also result in some false spectral components in the frequency bands [18].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive EMD Technique for Induction Motor Fault Detection

Mahmud¹,

Wang²

2019

JSIP

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Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.

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Section: The Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive EMD Technique for Induction Motor Fault Detection

Mahmud¹,

Wang²

2019

JSIP

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show abstract

“…Many studies have been proposed on the diagnosis and prognosis of mechanical parts such as bearings, gears, and motors [ 1 , 2 , 3 , 4 ]. The study in [ 2 ] proposed a current signal analysis method by empirical mode decomposition and the Hilbert spectrum for the incipient broken rotor of induction motors. Through statistical analysis, the damage was detected by the kurtosis value in early phases.…”

Section: Introductionmentioning

confidence: 99%

Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

Lee

2020

Sensors

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This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

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“…The orthogonal matching pursuit algorithm (OMP) in [9] focused on the decomposition of signals, which demonstrated the detection method is effective. On the other hand, several MCSA-based demodulation techniques such as empirical mode decomposition (EMD) and neural networks [10], Hilbert-Huang transform (HHT) [11], multiple signal classification (MUSIC) [12], and synchrosqueezing transform (SST) [13] were also developed for BRB fault diagnosis. There are also other techniques such as magnetic field analysis [14], infrared data analysis [15], and air-gap torque analysis [16] that were applied for BRB faults detection.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

Wang

Yang

et al. 2019

Energies

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Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

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Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars

Cited by 62 publications

References 28 publications

An Adaptive EMD Technique for Induction Motor Fault Detection

An Adaptive EMD Technique for Induction Motor Fault Detection

Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

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