Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm

Yang, Ming; Na, Chai; Liu, Zirui; Ren, Biying; Xu, Dianguo

doi:10.1109/tie.2019.2922919

Cited by 26 publications

(11 citation statements)

References 25 publications

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“…The study showed that the kurtosis spectrum analysis of the speed signal can achieve similar results to those obtained for the vibration method. In [140], the ENV of the speed signal is analyzed. In order to suppress velocity ripples, the authors used additional resonant controllers in parallel with existing proportional-integral controller.…”

Section: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

confidence: 99%

“…Experimental tests are carried out using artificially modelled damages. Most studies are carried out using physically damaged bearings by cutting, drilling or electrically engraved pitting of the component [117], [120], [121], [125], [127], [129], [138]- [140]. In [116] the faulty bearing was artificially aged by a burning grease process at 200°C during 60 hours and a broken cage.…”

Section: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

confidence: 99%

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Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSM) are currently very topical due to the increasing use of these drives in safety-critical devices. Every year, more and more articles on this subject are published. Therefore, the aim of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods using the analysis of control structure signals for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of PMSM drives failures. Based on the presented literature analyses, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

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Section: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

confidence: 99%

Section: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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“…The electrical method realizes the system malfunction diagnosis by collecting signals such as phase current, electromagnetic torque, or speed and cooperating with signal processing technology [ 23 ]. Yang et al [ 24 ] carried out malfunction diagnosis of motor bearing based on the characteristic analysis method of speed signal, which preliminarily showed the feasibility of the usual analysis method of the speed signal for mechanical malfunction diagnosis. Chandra and Sekhar [ 25 ] detect the coupling misalignment based on the torque signal.…”

Section: Introductionmentioning

confidence: 99%

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

Yu²

2022

Computational Intelligence and Neuroscience

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Aiming at the quantitative diagnosis of shafting misalignment malfunction, a novel method based on speed signal with singular value decomposition and Hilbert transform (SVD-HT) and cubic spline fitting-Pareto particle swarm optimization-echo state network (CSF-PPSO-ESN) method is proposed. The malfunction diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment malfunction model. Then, the SVD-HT and CSF-PPSO-ESN methods are applied to obtain the relationship between the shaft misalignment malfunction and the amplitude of the time and the rotation frequency (fr) component of the speed signal. The parameters of the CSF-PPSO-ESN method are settled according to the shaft misalignment malfunction and the fr component of the speed signal. The accuracy of the proposed method is verified by using the fr component of the speed signal and the trained CSF-PPSO-ESN to obtain the value of the shaft misalignment malfunction. The repeating experimental results show that the diagnosing error of the shaft misalignment malfunction can reach less than ±10 μm. The method presented in this paper provides a novel way to diagnose shaft misalignment malfunction quantitatively.

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“…A Hall sensor is used to detect the bearing faults of a motor in [20]. The fault detection method was also applied based on the motor speed signature [21]. Image signals obtained by an underwater camera were used to detect blade imbalance fault in an MCT in [22,23].…”

Section: Introductionmentioning

confidence: 99%

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Wei

Xie

Shi

et al. 2021

JMSE

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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

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Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm

Cited by 26 publications

References 25 publications

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

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