Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

Song, Kai; Xu, Peng; Wei, Guo; Chen, Yinsheng; Wang, Qi

doi:10.3390/s18072316

Cited by 12 publications

(7 citation statements)

References 46 publications

(51 reference statements)

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“…In recent years, GST has been applied, among others, to: human resources management [33,34] healthcare management [35], customer satisfaction assessment [36] and labor migration management [37]. A particularly explored area of application of GST is sustainable development issues.…”

Section: Methodsmentioning

confidence: 99%

Measuring the Efficiency of Economic Growth towards Sustainable Growth with Grey System Theory

2020

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In the paper, a new indicator exemplifying the conversion efficiency of expenditures towards economic growth into results pertaining to sustainable development, dubbed the “Synthetic Efficiency Indicator for Economic Growth” (hereinafter: “SEI-EG”) has been proposed. The inspiration for proposing such an indicator was the identification of the lack of connections between research on economic convergence and the research area connected with sustainable growth category. It was assumed that, in the first place, outcomes of the proposed convergence will be visible in developed economies, represented by EU15 member states. The set goal was to provide an answer to the question of difference between EU15 member states with respect to efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable growth. The research was conducted for 2016–2018 using Grey System Theory. With the use of the elaborated indicator, the authors created a ranking list of countries based on the efficiency of economic growth towards sustainable growth criterion. The conducted research proved that, in general, the smaller EU member states are characterized by significantly higher efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable development in the researched area. Among the countries with large economies, only Germany showed efficiency comparable to smaller ones.

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Section: Methodsmentioning

confidence: 99%

Measuring the Efficiency of Economic Growth towards Sustainable Growth with Grey System Theory

2020

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“…This is indeed a new research trend with some pioneering reports in the early of 1990s [59]. Bayesian approaches [60,61], Fuzzy set theory [62,63], Dempster-Shafer (D-R) evidence theory [64][65][66], and Grey Group Decision-Making [67], among other methods, have recently been explored to address the reliability of sensors using Artificial Intelligence. However, many issues related with sensor reliability in cyber-physical systems using Internet-of-Things data remain unsolved, such as efficient conditioning and pre-filtering of big sensor data, high computational cost and limited parametrization of machine learning methods.…”

Section: Reliability Of Sensors In Cyber-physicals Systems a Brief Smentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

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“…Based on some previous work on hydrogen detection [29], [30], sensor fault detection, diagnosis and recovery [31]- [33], the objective of this paper is to achieve quantitative measurement of hydrogen concentration at high accuracy, and especially at high reliability. In Section II, the fundamental principle of ELM and MWPCA is analyzed.…”

Section: The Rapid and Quantitative Hydrogen Concentrationmentioning

confidence: 99%

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

Song

Chen

et al. 2019

IEEE Access

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The flammable and explosive property of hydrogen is the main danger in its safe use, storage and transportation. In this paper, a novel hydrogen monitoring system is designed based on the principle of semiconductor, catalytic combustion and heat-conducting gas sensors. Also, the gas sensor will inevitably fail due to the nature of gas sensitive materials in the long-time monitoring process. To ensure the accuracy and reliability of hydrogen concentration measurement, a novel fault diagnosis and reconfiguration strategy for hydrogen sensor array based on moving window principle component analysis and extreme learning machine (MWPCA-ELM) is proposed. Firstly, online multiple faults detection is carried out by using MWPCA. Once one or multiple faults are detected, the measured values of other fault-free sensors will be used to recover the faulty data in real-time by using ELM predictor according to the relevancy among the hydrogen sensors. Secondly, the hydrogen concentration is reconfigured seamlessly and accurately based on ELM under the condition of small calibration data sample. Finally, fault diagnosis is conducted by MWPCA feature extraction coupled with ELM multi-classifier. In order to illustrate the effectiveness and feasibility of the proposed fault diagnosis and reconfiguration strategy, a hydrogen concentration monitoring experimental system was established. The average relative error (ARE) of hydrogen concentration estimation is declined from 1.18% to 0.82% compared with the traditional regression methods. Particularly, the proposed fault reconfiguration model can recover the fault data even if the concentration is changed, and the accuracy of fault diagnosis is 100% within 250 samples. INDEX TERMS Fault diagnosis, reconfiguration, hydrogen sensor, extreme learning machine, moving windows principle component analysis.

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Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

Cited by 12 publications

References 46 publications

Measuring the Efficiency of Economic Growth towards Sustainable Growth with Grey System Theory

Measuring the Efficiency of Economic Growth towards Sustainable Growth with Grey System Theory

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

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