A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Koteleva, Natalia; Korolev, Nikolay; Zhukovskiy, Y. L.; Baranov, Georgii D.

doi:10.3390/s21237900

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Structural optimization is usually carried out in the selected bearings to achieve higher power generation, regular operation, and long-term reliability of the generator [56]. The current forms of generator bearing wear are fatigue wear, adhesive wear, and current pitting wear [57,58].…”

Section: (4) Generator Bearingsmentioning

confidence: 99%

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

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With the significant penetration of wind generation, wind turbines require higher and higher lubrication performance for bearings. To improve the lubrication performance of wind power bearings, this study takes wind power bearings as the research object and comprehensively analyzes the wear forms of wind power bearings as well as intelligent lubrication methods. Firstly, the main roles and wear forms of wind turbine bearings are sorted out and analyzed. Secondly, the common lubrication problems of wind power bearings are analyzed from the bearing grease selection, lubrication mode, and lubrication status, highlighting the important influence of lubrication on bearings. Thirdly, the wind turbine bearing wisdom lubrication method research and organization, mainly including the wind power generation bearing lubrication materials, lubrication devices and monitoring methods, and other issues of research and analysis. Finally, current challenges and future development directions are summarized, which are designed to provide theoretical reference and technical support for the related research and engineering practice in the field of wind power engineering.

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Section: (4) Generator Bearingsmentioning

confidence: 99%

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

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“…Soft sensor modeling, also known as virtual sensing or data-driven modeling, enables the estimation of specific physical or chemical parameters using available data and mathematical models 6 – 8 . In the context of air ozone prediction, soft sensor modeling involves constructing models using relevant environmental variables such as meteorological data, pollutant concentrations and historical ozone measurements to predict ozone levels in real-time or for future periods.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

Zhou,

Qiu,

Zhang

2023

Sci Rep

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The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.

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“…After Park vector estimation, various sophisticated methods of signal processing can be used for the detection of fault-related components [ 30 , 35 ]. This approach can be applied for the detection of bearing faults [ 30 , 31 , 35 , 41 , 42 , 43 , 44 , 45 ] as well as other IM faults, such as broken rotor bars [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ] and stator winding faults [ 53 , 54 , 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Investigation of Higher Order Spectral Technologies for Fault Diagnosis of Motor-Based Rotating Machinery

Ciszewski

Gelman

Ball

et al. 2023

Sensors

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In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation.

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A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Cited by 7 publications

References 44 publications

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

Novel Investigation of Higher Order Spectral Technologies for Fault Diagnosis of Motor-Based Rotating Machinery

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