Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Głowacz, Adam

doi:10.17559/tv-20150328135652

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Then, especially in vibroacoustic diagnostics (WA), it is possible to determine changes in the WA signal resulting from a change in the technical condition of the tested object. There are many publications in the literature presenting the use of vibroacoustic diagnostics (in some cases in combination with thermal imaging diagnostics) in automotive, railroad or air vehicles [19,[29][30][31][32][33][34][35][36]. Based on the changes presented in Figure 6, it can be concluded that the wear of the friction material affects the increase in the amplitude of vibration acceleration from the holder with friction linings.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Sawczuk

Ulbrich

Kowalczyk

et al. 2021

Sensors

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The article presents the results of friction and vibroacoustic tests of a railway disc brake carried out on a brake stand. The vibration signal generated by the friction linings provides information on their wear and offers evaluation of the braking process, i.e., changes in the average friction coefficient. The algorithm presents simple regression linear and non-linear models for the thickness of the friction linings and the average coefficient of friction based on the effective value of vibration acceleration. The vibration acceleration signals were analyzed in the amplitude and frequency domains. In both cases, satisfactory values of the dynamics of changes above 6 dB were obtained. In the case of spectral analysis using a mid-band filter, more accurate models of the friction lining thickness and the average coefficient of friction were obtained. However, the spectral analysis does not allow the estimation of the lining thickness and the friction coefficient at low braking speeds, i.e., 50 and 80 km/h. The analysis of amplitudes leads to the determination of models in the entire braking speed range from 50 to 200 km/h, despite the lower accuracy compared to the model, based on the spectral analysis. The vibroacoustic literature presents methods of diagnosis of the wear of various machine elements such as bearings or friction linings, based on amplitude or frequency analysis of vibrations. These signal analysis methods have their limitations with regard to their scope of use and the accuracy of diagnosis. There are no cases of simultaneous use of different methods of analysis. This article presents the simultaneous application of the amplitude and frequency methods in the analysis of vibroacoustic signals generated by brake linings. Moreover, algorithms for assessing the wear of friction linings and the average coefficient of friction were presented. The algorithm enables determination of the time at which the friction linings should be replaced with new ones. The final algorithm analyzes the vibration acceleration signals using both amplitude analysis for low braking speeds, as well as spectral analysis for medium and high braking speeds.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Sawczuk

Ulbrich

Kowalczyk

et al. 2021

Sensors

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“…With the increasing size and complexity of data, neuroevolution is used to optimize the strengths of neural connections and the structure of the network, such as conventional neuro evolution (CNE) [17] and differential evolution for neural networks (DENN) [18]. Moreover, some scholars have combined neural networks with data feature extraction technology to improve the fault recognition ability [19,20].…”

Section: Methodsmentioning

confidence: 99%

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Liu

2020

Symmetry

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Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

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“…Reference [29] carries out recognition of armature current of DC generator with the FFT, Method of Selection of Amplitudes of Frequencies and Linear Discriminant Analysis. Reference [30] proposes a new method of feature extraction SMOFS-25-EXPANDED (shorted method of frequencies selection- to analyze the acoustic signals for real incipient states of loaded synchronous motor.…”

Section: Introductionmentioning

confidence: 99%

Compensation for Inverter Nonlinearity in Permanent Magnet Synchronous Motor Drive and Effect on Torsional Vibration of Electric Vehicle Driveline

Wang

2018

Energies

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Permanent magnet synchronous motors (PMSMs) with inverters are widely used in electric vehicles (EVs). However, current harmonics caused by the nonlinearity of the inverter generate torque ripples and give rise to torsional vibration in the vehicle driveline. This paper proposes a new compensation method to suppress the torque ripples. This method extracts the 6th-order harmonic component online from the d-axis and q-axis currents with the approximate Fourier transform, and adopts a harmonic current PI regulator to calculate compensation voltage, which is added to the voltage reference to compensate the nonlinearity of the inverter. After correcting the current distortion and improving the motor torque smoothness, the torsional vibration of the driveline caused by the motor pulsating torque is reduced. According to the simulation results, the 6th-order of motor torque ripple and the torsional vibration response is reduced about 26–28%, which confirms the validity of the proposed strategy. The proposed method does not need any additional hardware and can be implemented broadly in PMSM drives.

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Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Cited by 12 publications

References 10 publications

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Compensation for Inverter Nonlinearity in Permanent Magnet Synchronous Motor Drive and Effect on Torsional Vibration of Electric Vehicle Driveline

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