scholarly journals Robust Detection of Hidden Material Damages Using Low-Cost External Sensors and Machine Learning

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 56 ◽  
Author(s):  
Stefan Bosse ◽  
Dirk Lehmhus
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Tight Coupling ◽  
Destructive Testing ◽  
Robust Detection ◽  
Real Time Scale ◽  
Wide Range ◽  
Mass Spring ◽  
Multi Body ◽  
Non Destructive

Machine learning (ML) techniques are widely used in structural health monitoring (SHM) and non-destructive testing (NDT), but the learning process, the learned models, and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damage in materials monitored using low-cost strain sensors. The investigation is performed by means of a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

scholarly journals A framework for corrosion assessment in metallic structures, from data analysis to risk based inspection

2021 ◽  
Vol 23 (1) ◽  
pp. 11-20
Author(s):  
Xiaofei Cui ◽  
Xiaoxia Liang ◽  
Ujjwal Bharadwaj
Keyword(s):  
Corrosion Damage ◽  
Quantitative Information ◽  
Assessment Process ◽  
Steel Bridge ◽  
Destructive Testing ◽  
Metallic Structures ◽  
Wide Range ◽  
Corrosion Assessment ◽  
Metallic Corrosion ◽  
Non Destructive

Metallic corrosion is a big challenge affecting many sectors in a nation’s economy. Necessary corrosion prevention actions have to be taken in order to maintain the integrity of engineering assets susceptible to corrosion. This paper proposes a holistic framework to support the management of corrosion in metallic structures. It is a fully automation corrosion assessment process, with risk updated by Bayesian theory. Through analyzing the thickness data measured by non-destructive testing (NDT) techniques, the influence of corrosion on the component can be estimated using statistical methods, which will enable users to make decisions on maintenance based on quantitative information. A case study using corrosion data from a steel bridge is included to demonstrate the proposed framework. It improved the conventional corrosion analysis method by the proposed statistical approach using representative thickness data, which aims to take full use of the remaining life. This model can be adapted to a wide range of metallic structure suffering from corrosion damage.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

Quantitative Measurement System of Liquid Products in Sealed Tank

2019 ◽  
Vol 970 ◽  
pp. 63-74
Author(s):  
Yuliya Shulgina ◽  
Maria A. Kostina ◽  
P.V. Sorokin ◽  
Marina Polonskaya ◽  
O.A. Kozhemyak ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Optimal Solution ◽  
Storage Conditions ◽  
Ultrasonic Pulse ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Wide Range ◽  
Liquid Products ◽  
Manometric Method ◽  
Non Destructive

Many industries apply pressure tanks for the storage of various types of liquids [1]. It can be toxic, chemically active liquids or food products. Storage conditions of these liquids can have a wide range of pressures and temperatures; therefore it is preferable to control the liquids levels from the outside of the tank. The most optimal solution in this case is the ultrasonic pulse time method [2-6], which is also widely used in robotics [7], fishing, shipping [8-9], archeology [10-11], non-destructive testing [12-17] and manometric method [18].

Advances in the Computed Tomography of Turbine Blades

2005 ◽  
Author(s):  
Martin Allen ◽  
Andrew T. Ramsey
Keyword(s):  
Computed Tomography ◽  
Data Extraction ◽  
Turbine Blades ◽  
Practical Method ◽  
International Standards ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Wide Range ◽  
Non Destructive ◽  
Testing And Evaluation

Recent advances in virtually all areas of industrial Computed Tomography (CT) now allow faster, higher resolution, and increasingly economic CT inspection of turbine blades than ever before. CT is now used for a wide range of Non Destructive Testing and Evaluation (NDT&E) applications including first article inspection, defect detection, internal measurement, wear (and failure) analysis, and reverse engineering. Improvements range from the introduction of international standards on CT, through improvements in acquisition, reconstruction, and data extraction. Some of the most significant advances have been made in the ability to process the data generated by the CT systems. Today, CT is an increasingly practical method for the Non Destructive Testing and Evaluation of turbine blades.

Technical Note: Detection of delamination in tyres using eddy currents

1997 ◽  
Vol 211 (1) ◽  
pp. 79-82 ◽  
Author(s):  
X. E. Gros
Keyword(s):  
Eddy Currents ◽  
Structural Integrity ◽  
Low Cost ◽  
Technical Note ◽  
Quality And Safety ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Safety Standards ◽  
Current Testing ◽  
Non Destructive

Non-destructive testing (NDT) is a useful tool to assess the structural integrity of components in order to maintain quality and safety standards. A low-cost electromagnetic technique based on eddy currents induced into a material appeared promising for the inspection of composite materials. Experiments were carried out in order to assess the potential of eddy currents in detecting delamination in rubber tyres. Infrared thermography was used to verify inspection results achieved with eddy currents. Non-destructive examination results are presented in this paper; these confirm that eddy current testing is an economically viable alternative for the inspection of steel reinforced truck tyres.

scholarly journals Reconfigurable Information-Measuring System of Thermophysical Properties of Solid Materials with an Intelligent Sensor

2019 ◽  
pp. 4-19
Author(s):  
Z.M. Selivanova ◽  
V.S. Eryshova
Keyword(s):  
Thermophysical Properties ◽  
Measuring System ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Solid Materials ◽  
Intelligent Sensor ◽  
Information Measuring ◽  
Wide Range ◽  
Information Measuring System ◽  
Non Destructive

An information-measuring system for non-destructive testing of thermophysical properties of solid materials with an intelligent sensor has been developed. Mathematical models for reconfiguring the structure of the information-measuring system and the intelligent sensor were built. Algorithms for changing the configuration of the intelligent sensor and the functioning of the information-measuring system have been developed, allowing us to expand the systems functionality for studying thermophysical properties of solid materials in a wide range of thermal conductivities, as well as to improve the accuracy and efficiency of thermophysical measurements by adapting the system to the class of materials studied.

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