Subspace-Based Identification of Acoustic Noise Spectra in Induction Motors

High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO 2 emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

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“…[6][7][8][9][10][11][12][13][14][15][16][17][18][19].53] Na A Na A Approximation (7) [19.53-0] Na Na Na Na A: Applicable; Na: Not applicable.…”

Section: Log-energy Entropymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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“…The research of motor noise can be generally divided into five directions: motor noise evaluation, motor noise diagnosis, motor noise prediction, the influencing factor analysis, and the optimisation and control of motor noise. Currently, many researchers have made important contributions to studying motor noise [6][7][8][9][10][11][12][13][14][15][16][17]. In the aspect of motor noise evaluation, a SQ evaluation and prediction method of PMSM for EVs was presented in [6], by means of which the physical characteristics of noises and their effects on human subjective sensation can be reflected fully.…”

Section: Introductionmentioning

confidence: 99%

“…However, previous studies [6][7][8][9][10][11][12][13][14][15][16][17] about motor noise have several common problems as follows:…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is an urgent need for a new acquisition method with no sound attenuation, low cost, and strong anti-interference. (ii) Currently, sound pressure level or sound power level is regarded as the only evaluation index of the noise from electrical motors in the majority of literatures [7][8][9][10][11][12][13][14][15][16][17]. However, the index cannot comprehensively reflect the human's subjective feelings because it only represents the noise energy.…”

Section: Introductionmentioning

confidence: 99%

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Sound‐quality diagnosis method of permanent magnet synchronous motor for electric vehicles based on critical band analysis

Liu³

et al. 2019

IET Electric Power Applications

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As it has been widely and wrongly believed that noises at any frequency of permanent magnet synchronous motor (PMSM) are positively correlated with subjective annoyance, reducing sound pressure level or sound power level of PMSM is mostly regarded as the only optimisation goal of sound quality (SQ). This study presents a sensitive critical band (SCB) diagnostic method for SQ of PMSM for electric vehicles (EV). First, a new acquisition method of near-field noises without sound attenuation of PMSM was proposed and a neural network model for SQ evaluation was established to determine the specific operating condition with the worst SQ. Second, the band-pass filter of CB (BPFCB) and band-stop filter of CB (BSFCB) were designed to diagnose positive SCBs or negative SCBs in which noises were positively or negatively correlated with the subjective annoyance, respectively. Finally, the major excitation sources of abnormal noises in SCBs were diagnosed by using the analytical calculation. An 8-pole-48-slot PMSM was considered as a case study to testify to the effectiveness and practicability of the proposed method. It may provide a new route for the precise SQ optimisation of PMSM.

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“…Identification of multi-input/multi-output systems from a measured power spectrum arises in many applications, for example, designing shape filters for road disturbances experienced by vehicles moving forward [1] or acoustic noise in electric motors [2]. The goal here is to approximate noise spectra by rational functions of reasonably low order.…”

Section: Introductionmentioning

confidence: 99%

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

Akçay

2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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In this survey article, we study methods to identify multi-input/multi-output, discrete-time, linear time-invariant systems from power spectrum measurements. First, we examine subspace-based identification algorithms. A hindrance to these methods is splitting of two invariant spaces generated by causal and anti-causal eigenvalues in order to determine model order.Next, we study model order selection criteria based on the regularized nuclear norm and the regularized and reweighted nuclear norm heuristics. The latter heuristic, formulated in a different way, is used to ensure positivity of the spectrum estimate delivered by subspace identification algorithms. A numerical example illustrates properties of the regularized and reweighted nuclear norm heuristic.

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Subspace-Based Identification of Acoustic Noise Spectra in Induction Motors

Cited by 13 publications

References 30 publications

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Sound‐quality diagnosis method of permanent magnet synchronous motor for electric vehicles based on critical band analysis

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

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