Intelligent Bearing Fault Diagnosis Based on Tacholess Order Tracking for a Variable-Speed AC Electric Machine

Niu, Jiahao; Lu, Siliang; Liu, Yongbin; Zhao, Jiwen; Wang, Qunjing

doi:10.1109/jsen.2018.2883955

Cited by 27 publications

(15 citation statements)

References 38 publications

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“…Analysed Signal Angle Estimate [5][6][7][8][9][10][11][12] vibration vibration [13][14][15] vibration current [16] vibration currents/voltages + observer [17] vibration voltages [18][19][20] current current [21] IF current [22] IF currents/voltages + observer [23] I A and IF current [24] sound current [25] sound video…”

Section: Refmentioning

confidence: 99%

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

et al. 2021

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This article deals with the detection of mechanical faults in synchronous machines from single current measurement at variable speed. The proposed approach is based on an order tracking method in which the analysis signal is sampled as a function of the mechanical angle. In this case, the spectral components become independent of the speed and the frequency analysis can be exploited. Order tracking is generally implemented from a position measurement. In this work we present a method that allows us to estimate this position and the analysis signal from only one current measurement. The proposed approach allows an intuitive adjustment of the algorithm parameters. Secondly, a statistical method is used to finalize the diagnosis. At variable speed, this type of method is difficult to implement and we show that order tracking makes it possible to simplify the analysis. The procedure is tested in simulation and on a experimental test bench.

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Section: Refmentioning

confidence: 99%

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

et al. 2021

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“…Most of the proposed studies that have analyzed and identified the occurrence of faults in bearings are commonly focused on analyzing those faults that are related to the electrical erosion phenomenon [13,15,28]. In this regard, in the research field, the faults produced by electrical erosion are artificially produced by drilling a hole in the outer race, inner race and/or by damaging the rolling elements of the bearing [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

et al. 2021

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Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

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“…To this end, synchrosqueezing transform (SST) was first investigated in Daubechies et al [9], originally based on the continuous wavelet transform (CWT) [10,11], to enhance the localization property by rearranging the energies of TF coefficients in the data. SST can overcome the limitations of TF analysis by producing high-resolution ridges without spreading the energy of each term.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Zhao

2021

IET Microwaves Antenna & Prop

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The micro-Doppler (m-D) effect depends on the rotation of rotor blades in addition to the translation of the platform. Thus it is a characteristic for identifying small unmanned aerial vehicles (UAVs). However, compared with the Doppler signal induced by the translation of the platform, the m-D signal is weak. In this article, a highly localised dataassociation method, intrinsic synchrosqueezing analysis (ISA), is proposed for estimating m-D characteristics from the returned signal of small UAVs with a dual-channel radar. Employing synchrosqueezing transform on intrinsic mode functions derived from noiseassisted multivariate empirical mode decomposition, the proposed ISA method separates the Doppler signal and enables denoising and sharpening time-frequency representation of the m-D signal. Simulation results confirm the theoretical analysis, showing the feasibility of estimating m-D features in a noisy environment. Applications on field data illustrate brighter prospects for identifying small UAVs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Intelligent Bearing Fault Diagnosis Based on Tacholess Order Tracking for a Variable-Speed AC Electric Machine

Cited by 27 publications

References 38 publications

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

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