Diagnostic features for the condition monitoring of hypoid gear utilizing the wavelet transform

Skrickij, Viktor; Bogdevičius, Marijonas; Junevičius, Raimundas

doi:10.1016/j.apacoust.2015.12.018

Cited by 27 publications

(11 citation statements)

References 21 publications

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“…Therefore, the WT can extract information in the time domain with reference to different frequency bands. This simultaneous time-frequency decomposition gives the WT a special advantage over the traditional Fourier transform in analysing non-stationary signals [31,32,33]. …”

Section: Feature Extraction Through Wavelet Packet Transformmentioning

confidence: 99%

“…The main procedures for signal features extraction which are typically used in sensor monitoring research can be classified as follows: time-domain methods (e.g., principal component analysis (PCA)) [21,22,23]), frequency domain methods (e.g., fast Fourier transform (FFT)) [20,30]) and time-frequency domain methods (e.g., wavelet transform (WT) [31,32,33]).…”

Section: Introductionmentioning

confidence: 99%

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Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Segreto

Caggiano

Teti

2017

Sensors

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Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

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Section: Feature Extraction Through Wavelet Packet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Segreto

Caggiano

Teti

2017

Sensors

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“…However, an experimental way cannot be the only technique for investigating all possible combinations of operating parameters due to high costs and time spent for determining the optimal values. Mathematical modeling of a physical system employing numerical methods has become an effective alternative (Bogdevičius and Vitkūnas 2016;Andrikaitis and Fedaravičius 2014;Najafi and Ahmadzadeh 2014;Skrickij et al 2016;Spruogis et al 2015;Dyakov and Prentkovskis 2008;Moezi et al 2015;Bogdevičius et al 2015;Dönmez Demir et al 2015;Khatir et al 2014;Ahmadi et al 2015;Zhang et al 2015;Simonović 2015;Allali et al 2015). The paper is aimed on proposing mathematical model for the pneumatic damping system.…”

Section: Introductionmentioning

confidence: 96%

Behavior and Optimization of Pneumatic Damping System

Bogdevičius

Prentkovskis

Junevičius

et al. 2016

Iran. J. Sci. Technol. Trans. Mech. Eng.

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Vibration isolation is one of the most important research areas. Considerable research has been conducted in this area and on ways to reduce vibration. Pneumatic damping system with four gas chambers, which are connected by throttles in valves and permanent magnets, is considered. The mathematical model of pneumatic system is presented. The following factors are evaluated in this mathematical model: forces of pressure, friction, magnetic and contact forces between system bodies. The sensitive analysis of system parameters on which the quality of vibration isolation depends is done and critical parameters are identified. The optimization problem of pneumatic damping system with twelve parameters is presented. The frequency of external force is equal to 10 Hz. The objective function is the amplitude of the relative velocity of the fourth mass when each system parameter can be changed within an allowable interval. The objective function is minimized by the frequency range from 0 to 20 Hz. After optimization process, the value of objective function is decreased approximately three times and optimal system parameters are obtained.

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“…For investigation, the following diagnostic parameters were employed: X1 -Peak, X2 -Peak to Peak, X3 -RMS. Usage of these parameters gives good diagnostic results and minimal number of faulty diagnosis [28].…”

Section: Discrete Wavelet Transform (Dwt) and Daubechies 5 (Db5)mentioning

confidence: 99%

“…If compared to STFT, Wavelet uses narrow time windows at high frequencies and wide time windows at low frequencies, using Wavelet transform computing time is decreasing. Skrickij et al [28] showed that using wavelet transform and an extended frequency range, AE and VS signal monitoring of the gear unit is much more sensitive, and the occurrence of teeth faults and their growth can be recognized at an earlier stage.…”

Section: Science and Technologymentioning

confidence: 99%