scholarly journals MODWT, PCA and Decision Tree based Fault diagnosis of Gear

2021 ◽  
Vol 23 (07) ◽  
pp. 376-386
Author(s):  
Mansi Mansi ◽  
◽  
Sukhdeep S. Dhami ◽  
Vanraj Vanraj ◽  
◽  
...  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Processing Technique ◽  
Classification Performance ◽  
Performance Comparison ◽  
Vibration Monitoring ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Signal Processing Technique

A gearbox is an important power transmission equipment. Its maintenance is a top requirement because it is prone to a variety of failures. For gearbox fault diagnosis, techniques such as vibration monitoring have been widely used. Also, when it comes to machine Condition monitoring and fault diagnostics, feature extraction is the crucial step. For a classifier to perform accurately, it must have the appropriate discriminative information or features. Hence, this paper proposes a signal processing methodology based on Maximal overlap discrete wavelet transform (MODWT) and a dimensionality reduction technique i.eprincipal component analysis (PCA) to reduce the dimensionality of the feature space and obtain an ideal subspace for machine fault classification. Firstly, the raw vibration signature is denoised with the help of a state-of-the-art MODWT signal processing technique to identify the hidden fault signatures. Then various traditional statistical features are extracted from this denoised signal. These multi-dimensional features are then processed with PCA and further, the Decision Tree is used for fault classification. Performance comparison of the proposed method with traditional raw analysis and without application of PCA is presented and the proposed method outperforms at every level.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Condition Monitoring of PMSM and Stator-inter Turn Fault Diagnosis by Advanced Signal Processing Technique

2021 ◽  
Vol 23 (07) ◽  
pp. 1419-1430
Author(s):  
Khadim Moin Siddiqui ◽  
◽  
Farhad Ilahi Bakhsh ◽  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Dynamic Performance ◽  
Processing Technique ◽  
Industrial Applications ◽  
Fault Analysis ◽  
Industrial Plant ◽  
Signal Processing Technique ◽  
Permanent Magnet Synchronous Motors

In the present time, Permanent Magnet Synchronous Motors (PMSMs) are extensively used in many industrial applications due to its advantages over conventional synchronous motor. The PMSM is compact and efficient with high dynamic performance, thus having more advantages such as light weight, small size and bulky burden ability. When PMSMs are failed during the operation then large revenue losses occurs for industries. Hence, it is essential to diagnose these faults before occurring, for protection of any industrial plant. In the paper, firstly a comprehensive review of condition monitoring has been done for PMSM faults and their diagnostics techniques. From review, it is found that the stator inter-turn fault diagnosis has been the challenging task for many researchers. Hence, the work has been extended for fault analysis of stator inter-turn under transient conditions, which is effectively analyzed with the help of advanced signal processing technique.

Improving the robustness of beamforming using asymmetric sensor arrays

2018 ◽  
Vol 30 (3) ◽  
pp. 351-370
Author(s):  
Hwee Kwon Jung ◽  
Sujie Zhou ◽  
Gyuhae Park
Keyword(s):  
Signal Processing ◽  
Sensor Array ◽  
Signal Transmission ◽  
Sensor Arrays ◽  
Processing Technique ◽  
Performance Comparison ◽  
Sensor Deployment ◽  
Experimental Investigations ◽  
Signal Processing Technique ◽  
Spatial Aliasing

Beamforming or phased array with an array of sensors is an advanced signal processing technique for directional signal transmission or reception. This directionality is achieved by phase shifts of received signals of each sensor for the constructive interference of wavefronts, resulting in the amplification of the signal from a particular direction. In this research, the use of an asymmetric sensor array is proposed to reduce the effects of “spatial aliasing,” which is typically encountered in the structural health monitoring practice when employing phased arrays. In this technique, a sensor array is asymmetrically and closely deployed for beamforming and for robust source localization. This sensor deployment has a great effect on reducing the spatial aliasing errors. Although many advanced signal processing algorithms have been developed in the past, an asymmetric sensor array proposed in this study is used to reduce the spatial aliasing error from the sensor deployment perspective. In order to demonstrate the proposed sensor array technique, several simulation and experimental investigations are carried out, and the performance comparison is made to demonstrate the superior robustness of the asymmetric sensor array.

scholarly journals A Remote Sensor System Based on TDLAS Technique for Ammonia Leakage Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2448
Author(s):  
Hongbin Lu ◽  
Chuantao Zheng ◽  
Lei Zhang ◽  
Zhiwei Liu ◽  
Fang Song ◽  
...  
Keyword(s):  
Signal Processing ◽  
Limit Of Detection ◽  
Processing Technique ◽  
Tunable Diode Laser ◽  
Sensor System ◽  
Signal Processing Technique ◽  
Suppression Effect ◽  
Output Wavelength ◽  
Deviation Analysis ◽  
Remote Sensor

The development of an efficient, portable, real-time, and high-precision ammonia (NH3) remote sensor system is of great significance for environmental protection and citizens’ health. We developed a NH3 remote sensor system based on tunable diode laser absorption spectroscopy (TDLAS) technique to measure the NH3 leakage. In order to eliminate the interference of water vapor on NH3 detection, the wavelength-locked wavelength modulation spectroscopy technique was adopted to stabilize the output wavelength of the laser at 6612.7 cm−1, which significantly increased the sampling frequency of the sensor system. To solve the problem in that the light intensity received by the detector keeps changing, the 2f/1f signal processing technique was adopted. The practical application results proved that the 2f/1f signal processing technique had a satisfactory suppression effect on the signal fluctuation caused by distance changing. Using Allan deviation analysis, we determined the stability and limit of detection (LoD). The system could reach a LoD of 16.6 ppm·m at an average time of 2.8 s, and a LoD of 0.5 ppm·m at an optimum averaging time of 778.4 s. Finally, the measurement result of simulated ammonia leakage verified that the ammonia remote sensor system could meet the need for ammonia leakage detection in the industrial production process.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

scholarly journals Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 858 ◽  
Author(s):  
Timothy A. Vincent ◽  
Yuxin Xing ◽  
Marina Cole ◽  
Julian W. Gardner
Keyword(s):  
Signal Processing ◽  
Mobile Robot ◽  
Real Time ◽  
Low Cost ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Baseline Drift ◽  
Gas Sensor Array ◽  
Sensor Module ◽  
High Bandwidth

A new signal processing technique has been developed for resistive metal oxide (MOX) gas sensors to enable high-bandwidth measurements and enhanced selectivity at PPM levels (<50 PPM VOCs). An embedded micro-heater is thermally pulsed from 225 to 350 °C, which enables the chemical reactions in the sensor film (e.g., SnO2, WO3, NiO) to be extracted using a fast Fourier transform. Signal processing is performed in real-time using a low-cost microcontroller integrated into a sensor module. The approach enables the remove of baseline drift and is resilient to environmental temperature changes. Bench-top experimental results are presented for 50 to 200 ppm of ethanol and CO, which demonstrate our sensor system can be used within a mobile robot.

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