Identification and Monitoring of Noise Sources of CNC Machine Tools by Acoustic Holography Methods

Jóźwik, Jerzy

doi:10.12913/22998624/63386

Cited by 23 publications

(16 citation statements)

References 12 publications

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“…htpim tpim frb htpim tpim1frb (3) where feature vectors htpim=[htpim 26 , htpim 51 , htpim 76 ] and tpim1frb=[tpim1frb 26 , tpim1frb 51 , tpim1frb 76 ]. The calculations were performed for 4 feature vectors: htpim, tpim1frb, tpim2frb, tpimfrsc.…”

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

“…The early detection of faults prevents financial loss and downtimes. In the literature researchers developed methods of fault detection based on acoustic [1][2][3][4][5][6][7][8][9][10][11][12][13], thermal [14][15][16][17][18][19][20] and vibration signals [21][22][23][24][25][26][27][28][29]. An acoustic signal is difficult to process because microphone records many sounds from environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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Section: Nearest Neighbour Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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show abstract

“…Some researchers reported that specific activity, namely eating, excavation and head-banging would generate the particular acoustic signal [10,11]. Over the last decade, the application of microphones has been successful to detect insects within wood [12][13][14] and to solve various engineering problems [15][16][17][18][19]. Therefore, in this study, we used the microphone electret sensor to sense the acoustic signal produced by termites.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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“…In [9], the authors applied their method for measuring noise in work environment based on the acoustic pressure of noise emitted by CNC machines. Using acoustic holography methods for identifying and monitoring of noise sources of CNC machine tools were presented in [10].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Eldwaik

2018

Technologies

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Abstract:Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

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Identification and Monitoring of Noise Sources of CNC Machine Tools by Acoustic Holography Methods

Cited by 23 publications

References 12 publications

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

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