A Software Application for the Selection of Temperature Measuring Sensors Using the Analytic Hierarchy Process (AHP)

This study presents a software application that applies the Analytic Hierarchy Process (AHP) to objectively select the best temperature sensors. Three industrial applications and seven sensor alternatives are considered. The developed application performs the selection process in a computerised, easy–to–use graphical user interface. The underlying decision method ranks temperature sensors with scores resulting from the synthesis of relative preferences of each alternative at different levels considering independent evaluation criteria. Pair–wise relative comparison matrices collected from experts are embedded and are retrieved according to user specifications. A case study is conducted which involves selecting the best sensor for an automotive catalytic converter. The thermocouple is found to be the most preferred sensor with the largest score of 0.37849, the second ranked sensor is the RTD with a score of 0.34589, and the least preferred sensor is the thermister with a score of 0.27560. Sensitivity analysis shows that the selection of the best sensor is dependent on the relative weights of the criteria as well as the chosen application. AHP is shown to provide a quantitative evaluation method which is simpler, easier and more organised than subjective opinions

A Software Application For The Selection Of Temperature Measuring Sensors Using The Analytic Hierarchy Process (AHP)

This study presents a computer program that applies analytic hierarchy process (AHP) method to objectively select the best temperature sensors for various applications from multiple nominated alternatives. The underlying decision method based on AHP methodology, ranks temperature sensors with different features with a score resulting from the synthesis of relative preferences of each alternative to the others at different levels considering independent evaluation criteria. At each level, relative preferences of each candidate alternative with respect to the upper immediate level are calculated from pair-wise comparisons among the candidate alternative sensors based on the specifications of sensors with respect to a selected application. These pair-wise relative comparison weights are embedded in the computer software and are retrieved whenever the user specifies the application, the restrictions, and the available alternative sensors that meet these restrictions. AHP method proves to provide a quantitative and rational alternative performance evaluation method; it permits simpler, easier and more organized decision making process than subjective opinions that are subject to erroneous judgments. In this study, the application of AHP method in selecting the best temperature sensor for a particular application is embedded via the use of a computer program built using C# programming language to help perform the selection process in an easy graphical user interface GUI, ready-to-use, and computerized way and thus provides aid to those working in industry and in need of such a software tool. The proposed computer program is versatile and applicable to multitude of temperature sensors selection situations. A case study from the automotive industry which is the catalytic convertor application is presented. This application demands the use of temperature sensors capable of monitoring high temperatures in the order of 500°C-750°C, with a maximum temperature of ~870°C [1]. The selection process is conducted from among three alternative sensor categories, these are: thermocouples, thermisters, and RTD thermometers. The computer program is robust and applicable to a wider range of temperature sensors selection situations with a variety of applications and different arrays of candidate sensors

Selection of Temperature Measuring Sensors Using the Analytic Hierarchy Process

This study presents an analytic hierarchy process (AHP) method to objectively select the best temperature sensor from among different alternative sensors in a certain industrial application. The underlying decision method based on AHP methodology, ranks temperature sensors with different features with a score resulting from the synthesis of relative preferences of each alternative with respect to the others at different levels considering independent evaluation criteria and sub-criteria. At each level, relative preferences of each candidate alternative with respect to the upper immediate level are calculated from pairwise comparisons among the candidate alternative sensors with respect to a selected application. Pair-wise comparison matrices are compiled based on views of experts in this field. Seven alternative sensors were considered: the thermocouple, the thermister, the resistance temperature detector (RTD), the bimetallic strip thermometer, the mercury-in-glass thermometer, the optical disappearing filament pyrometer, and the liquid crystal display semi conductor thermometer (LCD). Three industrial applications were also considered: Automotives, Chemical Processes, and Heating, Ventilating and Air Conditioning. A case study is conducted which involves selecting the best sensor for an automotive catalytic converter. The thermocouple is found to be the most preferred sensor for this application with the largest score of 0.37849, the second ranked sensor is the RTD with a score of 0.34589, and the least preferred sensor is the thermister with a score of 0.27560. To test the robustness of the proposed work, a sensitivity analysis was conducted in which variations in the relative preferences of the alternative sensors against sub-criteria and criteria were employed

Selection of Temperature Measuring Sensors Using the Analytic Hierarchy Process

This study presents an analytic hierarchy process (AHP) method to objectively select the best temperature sensor from among different alternative sensors in a certain industrial application. The underlying decision method based on AHP methodology, ranks temperature sensors with different features with a score resulting from the synthesis of relative preferences of each alternative with respect to the others at different levels considering independent evaluation criteria and sub-criteria. At each level, relative preferences of each candidate alternative with respect to the upper immediate level are calculated from pair-wise comparisons among the candidate alternative sensors with respect to a selected application. Pair-wise comparison matrices are compiled based on views of experts in this field. Seven alternative sensors were considered: the thermocouple, the thermister, the resistance temperature detector (RTD), the bimetallic strip thermometer, the mercury-in-glass thermometer, the optical disappearing filament pyrometer, and the liquid crystal display semi conductor thermometer (LCD). Three industrial applications were also considered: Automotives, Chemical Processes, and Heating, Ventilating and Air Conditioning. A case study is conducted which involves selecting the best sensor for an automotive catalytic converter. The thermocouple is found to be the most preferred sensor for this application with the largest score of 0.37849, the second ranked sensor is the RTD with a score of 0.34589, and the least preferred sensor is the thermister with a score of 0.27560. To test the robustness of the proposed work, a sensitivity analysis was conducted in which variations in the relative preferences of the alternative sensors against sub-criteria and criteria were employed

Choosing Wearable Internet of Things Devices for Managing Safety in Construction Using Fuzzy Analytic Hierarchy Process as a Decision Support System

Many safety and health risks are faced daily by workers in the field of construction. There is unpredictability and risk embedded in the job and work environment. When compared with other industries, the construction industry has one of the highest numbers of worker injuries, illnesses, fatalities, and near-misses. To eliminate these risky events and make worker performance more predictable, new safety technologies such as the Internet of Things (IoT) and Wearable Sensing Devices (WSD) have been highlighted as effective safety systems. Some of these Wearable Internet of Things (WIoT) and sensory devices are already being used in other industries to observe and collect crucial data for worker safety in the field. However, due to limited information and implementation of these devices in the construction field, Wearable Sensing Devices (WSD) and Internet of Things (IoT) are still relatively underdeveloped and lacking. The main goal of the research is to develop a conceptual decision-making framework that managers and other appropriate personnel can use to select suitable Wearable Internet of Things (WIoT) devices for proper application/ implementation in the construction industry. The research involves a literature review on the aforementioned devices and the development and demonstration of a decision-making framework using the Fuzzy Analytic Hierarchy Process (FAHP)

Simulation-Based Decision Support System for Energy Efficiency in Buildings Retrofitting

Funding Information: This research was developed under project EnPROVE (Energy Consumption Prediction with Building Usage Measurements for Software-Based Decision Support) funded by the European Union’s Seventh Framework Programme (Grant agreement ID: 248061). Partial support was also given by the Portuguese “Fundação para a Ciência e Tecnologia (FCT)” through the project UIDB/00066/2020 (Center of Technology and Systems, CTS). Publisher Copyright: © 2022 by the authors.The implementation of building retrofitting processes targeting higher energy efficiency is greatly influenced by the investor’s expectations regarding the return on investment. The baseline of this work is the assumption that it is possible to improve the predictability of the post-retrofit scenario, both in energy and financial terms, using data gathered on how a building is being used by its occupants. The proposed approach relies on simulation to estimate the impact of available energy-efficient solutions on future energy consumption, using actual usage data. Data on building usage are collected by a wireless sensor network, installed in the building for a minimum period that is established by the methodology. The energy simulation of several alternative retrofit scenarios is then the basis for the decision support process to help the investor directing the financial resources, based on both tangible and intangible criteria. The overall process is supported by a software platform developed in the scope of the EnPROVE project. The platform includes building audit, energy consumption prediction, and decision support. The decision support follows a benefits, opportunities, costs, and risks (BOCR) analysis based on the analytic hierarchy process (AHP). The proposed methodology and platform were tested and validated in a real business case, also within the scope of the project, demonstrating the expected benefits of alternative retrofit solutions focusing on lighting and thermal comfort.publishersversionpublishe

AHP-based design method of a lightweight, portable and flexible air-based PV-T module for UAV shelter hangars

The use of renewable energy is spreading also to the military field. Its implementation in army forward bases has three clear advantages: an economic benefit lowering consumptions, an environmental profit reducing emissions, and a strategic interest minimizing risks in supplies. This paper presents a methodology for the design of a photovoltaic-thermal system (PV-T) to supply heat and electricity to military tents in forward facilities. UAV shelter hangars used by infantry forces have been chosen to implement this equipment. Analytic Hierarchy Process (AHP) has been chosen to explain its application to designing the PV-T system. A CFD analysis of different design alternatives was performed in order to quantify decision making criteria and subcriteria. The best performance design was used to build a test bench of the system, using an Arduino-based platform. Telemetry is used to remotely register PV-T module parameters. Experimental data obtained was implemented as boundary conditions to validate the CFD model of the PV-T system, and heat exchange models were implemented using UDF (user defined functions) in ANSYS® FLUENT®. A making decision method was successfully applied to define a methodology for geometrical design, using CFD simulation to determine necessary parameters to quantify criteria and subcriteria defined in the AHP

Wireless Sensor Technology Selection for I4.0 Manufacturing Systems

The term smart manufacturing has surfaced as an industrial revolution in Germany known as Industry 4.0 (I4.0); this revolution aims to help the manufacturers adapt to turbulent market trends. Its main scope is implementing machine communication, both vertically and horizontally across the manufacturing hierarchy through Internet of things (IoT), technologies and servitization concepts. The main objective of this research is to help manufacturers manage the high levels of variety and the extreme turbulence of market trends through developing a selection tool that utilizes Analytic Hierarchy Process (AHP) techniques to recommend a suitable industrial wireless sensor network (IWSN) technology that fits their manufacturing requirements.In this thesis, IWSN technologies and their properties were identified, analyzed and compared to identify their potential suitability for different industrial manufacturing system application areas. The study included the identification and analysis of different industrial system types, their application areas, scenarios and respective communication requirements. The developed tool’s sensitivity is also tested to recommend different IWSN technology options with changing influential factors. Also, a prioritizing protocol is introduced in the case where more than one IWSN technology options are recommended by the AHP tool.A real industrial case study with the collaboration of SPM Automation Inc. is presented, where the industrial systems’ class, communication traffic types, and communication requirements were analyzed to recommend a suitable IWSN technology that fits their requirements and assists their shift towards I4.0 through utilizing AHP techniques. The results of this research will serve as a step forward, in the transformation process of manufacturing towards a more digitalized and better connected cyber-physical systems; thus, enhancing manufacturing attributes such as flexibility, reconfigurability, scalability and easing the shift towards implementing I4.0

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology