Motor Fault Diagnostics Based on Current Signatures: A Review

Niu, Gang; Dong, Xun; Chen, Yuejian

doi:10.1109/tim.2023.3285999

Cited by 20 publications

(9 citation statements)

References 109 publications

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“…Equation (11) presents the mathematical representation of the parameters '∆R h/w (%)' and '∆A(%)' employed for evaluating and assessing the evolution and progression of the fault. The parameter '∆R h/w (%)' denotes the percentage deviation in the h el /w el ratio of the fitted ellipse when comparing healthy and unhealthy cases of various failure severities.…”

Section: Signal Processing Using Mmsppmentioning

confidence: 99%

“…In principle, the electrical current signal in the motor's normal operating state comprises solely the power supply frequency. However, the degradation of the generator leads to alterations in the air-gap magnetic field and torque distribution within the generator [11], resulting in the production of an external magnetic leakage that harbors important information and features concerning the generator's state of degradation and the presence of faults.…”

Section: External Leakage Flux Signalsmentioning

confidence: 99%

“…At present, the main non-invasive condition monitoring technologies widely studied are current, vibration, and flux-based fault diagnosis methods, which have been welldeveloped and explored extensively in research [1,11,12]. The CMS of wind turbines is mainly focused on vibration technologies applied to drivetrains, including gearboxes and bearings [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…The motor current signature analysis, as one of the most widely studied methods, is currently being applied to the DFIG [1,2,5]. Now various advanced signal processing methods from the time-domain, frequency-domain, and timefrequency-domain analysis have been used for adaptive noise reduction and feature extraction of current or vibration signals, such as wavelet transform (WT), short-time fourier transform, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), variational mode decomposition (VMD), synchrosqueezing transform and so on [1,11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Zhao,

Chen,

Liang

et al. 2023

Meas. Sci. Technol.

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Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function (NCDF) and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from modified multiscale poincare plots, established by support vector data description (SVDD) with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of inter-turn short-circuit fault severity.

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Section: Signal Processing Using Mmsppmentioning

confidence: 99%

Section: External Leakage Flux Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Zhao,

Chen,

Liang

et al. 2023

Meas. Sci. Technol.

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“…Different faults may appear during PMSM operation and can be related to the rotor [1], bearings [2], magnets [3], or stator windings [4]. Continuous and online condition monitoring of the system offers the possibility for early fault detection and thus better maintenance planning [5], avoidance of catastrophic faults, and reduction in downtime [6]. Various tools are used for motor health assessment, such as vibration analysis, acoustic emission analysis, temperature monitoring, or Motor Current Signature Analysis.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection for Permanent Magnet Synchronous Motor (PMSM) Fault Detection and Severity Estimation

Koutrakos,

Mitronikas

2024

Applied Sciences

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Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor’s current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench.

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Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

Lin,

Zou,

et al. 2024

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023)

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Motor Fault Diagnostics Based on Current Signatures: A Review

Cited by 20 publications

References 109 publications

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Outlier Detection for Permanent Magnet Synchronous Motor (PMSM) Fault Detection and Severity Estimation

Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

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