Recent Advances in Multifunctional Sensing Technology on a Perspective of Multi-Sensor System: A Review

Majumder, Bansari Deb; Roy, Joyanta Kumar; Padhee, Subhransu

doi:10.1109/jsen.2018.2882239

Cited by 70 publications

(36 citation statements)

References 117 publications

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“…To overcome these disadvantages, this work proposes the use of Artificial Neural Networks (ANNs) to compose the indirect estimation block (

). The main advantages for using an ANN is the lack of mathematical models, mainly because it is a complex and multivariable problem, due to its generalization capacity, fault tolerance, self-learning, noise immunity, and adaptability [ 12 , 20 , 29 , 30 , 31 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The various methodologies of indirect estimation are based on the techniques of least squares, multiple linear regression, models based on Kalman filter, Multisensor Data Fusion (MDF), etc. [ 12 , 13 ]. However, these methodologies have limitations due to the numerous variables integrated in the detection and the use of approximate mathematical methods for the reconstruction of the signals, which require a high computational effort coupled with a greater possibility of failure, if the system modeling presents a low performance.…”

Section: Introductionmentioning

confidence: 99%

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Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Flores

Villanueva

Gomes

et al. 2020

Sensors

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Indirect measurement can be used as an alternative to obtain a desired quantity, whose physical positioning or use of a direct sensor in the plant is expensive or not possible. This procedure can been improved by means of feedback control strategies of a secondary variable, which can be measured and controlled. Its main advantage is a new form of dynamic response, with improvements in the response time of the measurement of the quantity of interest. In water pumping networks, this methodology can be employed for measuring the flow indirectly, which can be advantageous due to the high price of flow sensors and the operational complexity to install them in pipelines. In this work, we present the use of artificial intelligence techniques in the implementation of the feedback system for indirect flow measurement. Among the contributions of this new technique is the design of the pressure controller using the Fuzzy logic theory, which rules out the need for knowing the plant model, as well as the use of an artificial neural network for the construction of nonlinear models with the purpose of indirectly estimating the flow. The validation of the proposed approach was carried out through experimental tests in a water pumping system, fully automated and installed at the Laboratory of Hydraulic and Energy Efficiency in Sanitation at the Federal University of Paraiba (LENHS/UFPB). The results were compared with an electromagnetic flow sensor present in the system, obtaining a maximum relative error of 10%.

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“…To overcome these disadvantages, this work proposes the use of Artificial Neural Networks (ANNs) to compose the indirect estimation block (

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Flores

Villanueva

Gomes

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Multi-sensor data fusion technology is defined as the comprehensive decision-making process based on the information obtained by multiple sensors [34][35][36]. There are two problems with the data fusion: (1) How to efficiently fuse the response signals obtained by different types of sensors, and (2) how to ensure the fused information present better performance than the single usage of e-nose or e-tongue.…”

Section: Data Fusion Techniquesmentioning

confidence: 99%

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Zhang

Shao

Qiu

et al. 2020

Sensors

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Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).

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“…Sensors are important elements in automation and the new concept of industry 4.0 because they allow the monitoring and assessment of the physical conditions of equipment, in order to achieve a better capacity for the control, reliability and integrity of industrial equipment [1]. In recent years, sensing has become more important in the research field, resulting from technological advances in detection that allow multivariate sensor monitoring [2], generating significant improvement in the observability of industrial systems. However, multivariate monitoring in engineering systems provides a large amount of varied data, generating a challenge for data processing [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

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Recent Advances in Multifunctional Sensing Technology on a Perspective of Multi-Sensor System: A Review

Cited by 70 publications

References 117 publications

Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

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