scholarly journals A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2243 ◽  
Author(s):  
Regivaldo Carvalho ◽  
Richardson Nascimento ◽  
Thiago D’Angelo ◽  
Saul Delabrida ◽  
Andrea G. C. Bianchi ◽  
...  
Keyword(s):  
Mining Industry ◽  
Field Tests ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Novel Approach ◽  
Extensive Field ◽  
Aerial Vehicle ◽  
Future Experimentation ◽  
Critical Components ◽  
Belt Conveyors

Frequent and accurate inspections of industrial components and equipment are essential because failures can cause unscheduled downtimes, massive material, and financial losses or even endanger workers. In the mining industry, belt idlers or rollers are examples of such critical components. Although there are many precise laboratory techniques to assess the condition of a roller, companies still have trouble implementing a reliable and scalable procedure to inspect their field assets. This article enumerates and discusses the existing roller inspection techniques and presents a novel approach based on an Unmanned Aerial Vehicle (UAV) integrated with a thermal imaging camera. Our preliminary results indicate that using a signal processing technique, we are able to identify roller failures automatically. We also proposed and implemented a back-end platform that enables field and cloud connectivity with enterprise systems. Finally, we have also cataloged the anomalies detected during the extensive field tests in order to build a structured dataset that will allow for future experimentation.

scholarly journals Series And Parallel Arc Fault Detection Based on Discrete Wavelet VS. FFT Techniques

2021 ◽  
Vol 18 (1) ◽  
pp. 38-47
Author(s):  
Elaf Saeed ◽  
Khalid Abdulhassan ◽  
Osama Khudair
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Processing Technique ◽  
Discrete Wavelet ◽  
Fault Models ◽  
Signal Processing Technique ◽  
Arc Model ◽  
Novel Approach ◽  
Fault Features ◽  
In Series

Arc problems are most commonly caused by electrical difficulties such as worn cables and improper connections. Electrical fires are caused by arc faults, which generate tremendous temperatures and discharge molten metal. Every year, flames of this nature inflict a great lot of devastation and loss. A novel approach for identifying residential series and parallel arc faults is presented in this study. To begin, arc faults in series and parallel are simulated using a suitable simulation arc model. The fault characteristics are then recovered using a signal processing technique based on the fault detection technique called Discrete Wavelet Transform (DWT), which is built in MATLAB/Simulink. Then came db2, and one level was discovered for obtaining arc-fault features. The suitable mother and level of wavelet transform should be used, and try to compare results with conventional methods (FFT-Fast Fourier Transform). MATLAB was used to build and simulate arc-fault models with these techniques.

scholarly journals Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 768 ◽  
Author(s):  
João Otávio Araujo ◽  
João Valente ◽  
Lammert Kooistra ◽  
Sandra Munniks ◽  
Ruud J. B. Peters
Keyword(s):  
Electrochemical Sensors ◽  
Field Tests ◽  
Air Pollutant ◽  
Automate Monitoring ◽  
Remote Sensors ◽  
Novel Approach ◽  
Wilcoxon Rank Sum Test ◽  
Significant Difference ◽  
Aerial Vehicle ◽  
And Performance

The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.

scholarly journals A Novel Approach for the Characterization of Triangular Modulated Frequency Modulated Continuous Wave Low Probability of Intercept Radar Signals via Application of the Wigner-Ville Distribution

2021 ◽  
pp. 19-28
Author(s):  
Daniel L. Stevens
Keyword(s):  
Signal Processing ◽  
Continuous Wave ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Novel Approach ◽  
Wave Radar ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Digital intercept receivers are changing from Fourier-based analysis to classical time-frequency analysis techniques for analyzing low probability of intercept radar signals. This paper presents a novel approach of characterizing low probability of intercept triangular modulated frequency modulated continuous wave radar signals through utilization and direct comparison of the signal processing techniques Wigner-Ville Distribution versus the Reassigned Smooth Pseudo Wigner-Ville Distribution. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Reassigned Smooth Pseudo Wigner-Ville Distribution signal processing technique produced more accurate characterization metrics than the Wigner-Ville Distribution signal processing technique.

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 Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction

Aerospace ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 79
Author(s):  
Carolyn J. Swinney ◽  
John C. Woods
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Operating Mode ◽  
Classification Problem ◽  
Large Signal ◽  
Signal Representations ◽  
Novel Approach ◽  
Power Spectral ◽  
Aerial Vehicle ◽  
Deep Convolution Neural Network

Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

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.

EXPRESS: The effect of low frequency equalisation on preference and sensorimotor synchronisation in music

2021 ◽  
pp. 174702182110371
Author(s):  
Scott Beveridge ◽  
Estefanía Cano ◽  
Steffen A. Herff
Keyword(s):  
Body Movement ◽  
Low Frequency ◽  
Empirical Support ◽  
Processing Technique ◽  
Original Version ◽  
Upper Body ◽  
Signal Processing Technique ◽  
Band Energy ◽  
Sensorimotor Synchronisation ◽  
Specific Frequency

Equalisation, a signal processing technique commonly used to shape the sound of music, is defined as the adjustment of the energy in specific frequency components of a signal. In this work we investigate the effects of equalisation on preference and sensorimotor synchronisation in music. Twenty-one participants engaged in a goal-directed upper body movement in synchrony with stimuli equalised in three low-frequency sub-bands (0 - 50 Hz, 50 - 100 Hz, 100 - 200 Hz). To quantify the effect of equalisation, music features including spectral flux, pulse clarity, and beat confidence were extracted from seven differently equalised versions of music tracks - one original and six manipulated versions for each music track. These music tracks were then used in a movement synchronisation task. Bayesian mixed effects models revealed different synchronisation behaviours in response to the three sub-bands considered. Boosting energy in the 100 - 200 Hz sub-band reduced synchronisation performance irrespective of the sub-band energy of the original version. An energy boost in the 0 - 50 Hz band resulted in increased synchronisation performance only when the sub-band energy of the original version was high. An energy boost in the 50 - 100 Hz band increased synchronisation performance only when the sub-band energy of the original version was low. Boosting the energy in any of the three subbands increased preference regardless of the energy of the original version. Our results provide empirical support for the importance of low-frequency information for sensorimotor synchronisation and suggest that the effect of equalisation on preference and synchronisation are largely independent of one another.

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