Intelligent monitoring of tapping tools

Liu, T. I.; Ko, E. J.; Sh, Sha

doi:10.1007/bf02833821

Cited by 12 publications

(2 citation statements)

References 7 publications

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“…The most common strategy is based on a monitoring analysis of the torque and force signals. In the three-pioneering works, a diagnosis of three common tapping faults was based on conditional probability functions [13], on diagnostic feedback neuronal system [30] and on neural networks [31], respectively. The outputs of all the studies were the level of tool wear, a measure of the misalignment and a measure of the under/oversized pre-drilled diameters of the holes.…”

Section: Introductionmentioning

confidence: 99%

High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning

Díez-Pastor

Val²,

Veiga³

et al. 2021

Measurement

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Section: Introductionmentioning

confidence: 99%

High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning

Díez-Pastor

Val²,

Veiga³

et al. 2021

Measurement

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“…Firstly, signal processing is performed using envelope method in [4] to extract the demodulated signals. Next, statistical features calculation from time domain are used for current, torque, and vibration signal analysis [163][164][165][166]. The statistical features that are used in this chapter are summarized as: Amplitude of 3rd harmonic of outer race frequency [169] The 12 features in Table 5.…”

Section: Computation Of Corresponding Featuresmentioning

confidence: 99%

Advanced feature extraction and selection for fault diagnosis and prognosis

Zhou¹

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Diagnosis of roller bearing defects using neural networks

Liu

Iyer

1993

Int J Adv Manuf Technol

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A new approach for the diagnosis of bearing defects has been utilised. Artificial neural networks (ANN) were employed for the diagnosis of various kinds of bearing defects. The features selected for this purpose were: the average of the top five values of amplitude in the high-frequency region (5 kHz-22 kHz ), the peak value of the amplitude in the high-frequency region, the average of the top five values in the prime spike region , the autocorrelation function in the prime spike region, the autocorrelation function in the high-frequency region, and the cepstrum function in the high-frequency region.Data were collected using a data acquisition system. The data collected for the five different defective roller bearings as well as for a normal bearing were used to train neural networks. The trained neural networks were used for the diagnosis of roller bearings. Various neural network sizes were used. It was found that neural networks were able to distinguish a normal bearing from defective bearings with 100% reliability. Furthermore, roller bearings can be classified into six different states with a success rate of up to 94%.

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Intelligent monitoring of tapping tools

Cited by 12 publications

References 7 publications

High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning

High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning

Advanced feature extraction and selection for fault diagnosis and prognosis

Diagnosis of roller bearing defects using neural networks

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