Estimação espectral do sinal de vibração para o monitoramento do desgaste do dressador de ponta única

Conceição, Pedro Oliveira; Marchi, Marcelo; Martins, Carla Braga; D’Addona, Doriana M.; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos

doi:10.1590/s1517-707620160004.0079

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Brzezinski et al [35] proposed a new method for grinder dressing fault mitigation using real-time peak detection. In Junior et al [36,37], the evaluation of single-point dresser wear was proposed through intelligent spectral analysis of vibration signals. Miranda et al [38] presented a methodology to estimate the wear of single-point dressers based on AE, vibration and fuzzy models.…”

Section: Dressing Operation Monitoringmentioning

confidence: 99%

“…These parameters are directly connected with the sharpness that the dressing operation produces on the grinding wheel. In this study, an U d = 1 was adopted at the beginning of the experiment, according to literature recommendations [3,5,36]. The other main parameters were the constant dressing depth equal to 40 µm and the constant dressing speed set at 3.45 mm/s.…”

Section: Experimental Test and Evaluationmentioning

confidence: 99%

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Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

Oliveira

D’Addona

Aguiar

et al. 2018

Sensors

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This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation.

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Section: Dressing Operation Monitoringmentioning

confidence: 99%

Section: Experimental Test and Evaluationmentioning

confidence: 99%

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

Oliveira

D’Addona

Aguiar

et al. 2018

Sensors

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Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

Zou

2021

International Journal of Distributed Sensor Networks

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The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.

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Estimação espectral do sinal de vibração para o monitoramento do desgaste do dressador de ponta única

Cited by 2 publications

References 32 publications

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

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