Sensor System Selection for Prognostics and Health Monitoring

2008 ◽  
Author(s):  
Shunfeng Cheng ◽  
Michael Azarian ◽  
Michael Pecht
Keyword(s):  
Data Collection ◽  
Health Monitoring ◽  
State Of The Art ◽  
Sensor System ◽  
Sensor Systems ◽  
Operational Parameters ◽  
Emerging Trends ◽  
Selection For ◽  
System Selection ◽  
Prognostics And Health Monitoring

Data collection is an essential part of prognostics and health monitoring, and often requires the use of sensor systems to measure environmental and operational parameters. In this paper, the considerations for sensor system selection for prognostics and health monitoring implementation are discussed and some state-of-the-art sensor systems for prognostics are described. Finally, emerging trends in sensor system technologies are presented.

scholarly journals A Mapping Analysis of Maintenance in Industry 4.0

2021 ◽  
Vol 19 (6) ◽  
pp. 653-675
Author(s):  
Mario Di Nardo ◽  
M. Madonna ◽  
P. Addonizio ◽  
Maryam Gallab
Keyword(s):  
Data Collection ◽  
Literature Review ◽  
Industry 4.0 ◽  
State Of The Art ◽  
Management Strategies ◽  
Industrial Sector ◽  
Maintenance Management ◽  
Emerging Trends ◽  
Mapping Analysis ◽  
Number Of Publications

This paper analyses and reviews the most important literature papers relating to the evolution of maintenance in Industry 4.0 and its applications. The topic's importance is stated by n increasing number of publications in this field, which suggested a systematic literature review. The proliferation of hardware devices in the workplace, such as smartphones and tablets, has caused engineers to develop the industrial sector 's maintenance world.  This review aims to classify the literature published from 2015 to early 2020 to identify the major benefits and areas where it obtained them. This study surveys the latest approaches and emerging trends in maintenance management strategies commonly used in the era of Industry 4.0. It discusses the state-of-the-art of Industry 4.0 technology and the associated use of manufacturing and maintenance management. The data collection was obtained by conducting a systematic search of the literature.

scholarly journals User Fulfilment Density Based Access to Intrusion Detection System Selection for WSN

2019 ◽  
Vol 8 (2S8) ◽  
pp. 728-731
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Sensor Systems ◽  
Safety Device ◽  
Remote Sensor ◽  
Selection For ◽  
System Selection

Interruption Detection System (IDS) is a scheme safety device used in remote sensor systems (WSNs) to identify vulnerability abusses against attacks. The determination of IDS relies upon the WSN engineering and application. It is for the overseer to choose which IDS will be the best answer for the sensor arrange. There is never one arrangement that works for everything so overseer needs to analyse the capacities of every id alongside spending plan and learning. This article gives a weight-based way to deal with a client to deal with IDS assurance for WSN. We initially talk about client WSN IDS prerequisites and WSN IDS measurements, at that point for each WSN IDS necessity we coordinate the worry metric(s). Client records their WSN IDS prerequisites in an incomplete requesting from minimum to generally imperative. Client necessities are typically expressed in a positive shape or changed over to the positive frame. The main prerequisite (i.e. minimum imperative) is relegated the most reduced weight (e.g., one) While the remaining preconditions are assigned to expand weights to their comparative importance. Once weighted, each WSN IDS metric is assigned a weight equivalent to the entire weight of the necessities it adds to.WSN IDS measurements are masterminded in sliding request where metric with the most noteworthy weight is at the best. Proper WSN IDS apparatus might be chosen in the wake of coordinating the measurements weight and IDS highlights.

A Systematic Literature Review of the EFQM Excellence Model from 1991 to 2019

1970 ◽  
Vol 2 (2) ◽  
pp. 11-15
Author(s):  
Muhammad Yousaf ◽  
Petr Bris
Keyword(s):  
Quality Management ◽  
Data Collection ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Quantitative Research ◽  
Web Of Science ◽  
State Of The Art ◽  
Future Research ◽  
European Foundation ◽  
The Subject

A systematic literature review (SLR) from 1991 to 2019 is carried out about EFQM (European Foundation for Quality Management) excellence model in this paper. The aim of the paper is to present state of the art in quantitative research on the EFQM excellence model that will guide future research lines in this field. The articles were searched with the help of six strings and these six strings were executed in three popular databases i.e. Scopus, Web of Science, and Science Direct. Around 584 peer-reviewed articles examined, which are directly linked with the subject of quantitative research on the EFQM excellence model. About 108 papers were chosen finally, then the purpose, data collection, conclusion, contributions, and type of quantitative of the selected papers are discussed and analyzed briefly in this study. Thus, this study identifies the focus areas of the researchers and knowledge gaps in empirical quantitative literature on the EFQM excellence model. This article also presents the lines of future research.

scholarly journals Patch-Type Vibration Visualization (PVV) Sensor System Based on Triboelectric Effect

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3976
Author(s):  
Sun Jin Kim ◽  
Myeong-Lok Seol ◽  
Byun-Young Chung ◽  
Dae-Sic Jang ◽  
Jonghwan Kim ◽  
...  
Keyword(s):  
Signal Processing ◽  
Low Power ◽  
Nuclear Power ◽  
Power Plants ◽  
Computing System ◽  
Sensor System ◽  
Wireless Sensor ◽  
Sensor Systems ◽  
Patch Type ◽  
Triboelectric Effect

Self-powered wireless sensor systems have emerged as an important topic for condition monitoring in nuclear power plants. However, commercial wireless sensor systems still cannot be fully self-sustainable due to the high power consumption caused by excessive signal processing in a mini-electronic computing system. In this sense, it is essential not only to integrate the sensor system with energy-harvesting devices but also to develop simple data processing methods for low power schemes. In this paper, we report a patch-type vibration visualization (PVV) sensor system based on the triboelectric effect and a visualization technique for self-sustainable operation. The PVV sensor system composed of a polyethylene terephthalate (PET)/Al/LCD screen directly converts the triboelectric signal into an informative black pattern on the LCD screen without excessive signal processing, enabling extremely low power operation. In addition, a proposed image processing method reconverts the black patterns to frequency and acceleration values through a remote-control camera. With these simple signal-to-pattern conversion and pattern-to-data reconversion techniques, a vibration visualization sensor network has successfully been demonstrated.

scholarly journals Nanomedicine-Based Strategies Assisting Photodynamic Therapy for Hypoxic Tumors: State-of-the-Art Approaches and Emerging Trends

Biomedicines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 137
Author(s):  
Chun-Yan Shih ◽  
Pei-Ting Wang ◽  
Wu-Chou Su ◽  
Hsisheng Teng ◽  
Wei-Lun Huang
Keyword(s):  
Photodynamic Therapy ◽  
State Of The Art ◽  
Metal Organic Framework ◽  
Basic Knowledge ◽  
Future Perspectives ◽  
Effective Strategies ◽  
Emerging Trends ◽  
Tumor Tissues ◽  
Metal Organic ◽  
Manipulating Hypoxia

Since the first clinical cancer treatment in 1978, photodynamic therapy (PDT) technologies have been largely improved and approved for clinical usage in various cancers. Due to the oxygen-dependent nature, the application of PDT is still limited by hypoxia in tumor tissues. Thus, the development of effective strategies for manipulating hypoxia and improving the effectiveness of PDT is one of the most important area in PDT field. Recently, emerging nanotechnology has benefitted progress in many areas, including PDT. In this review, after briefly introducing the mechanisms of PDT and hypoxia, as well as basic knowledge about nanomedicines, we will discuss the state of the art of nanomedicine-based approaches for assisting PDT for treating hypoxic tumors, mainly based on oxygen replenishing strategies and the oxygen dependency diminishing strategies. Among these strategies, we will emphasize emerging trends about the use of nanoscale metal–organic framework (nMOF) materials and the combination of PDT with immunotherapy. We further discuss future perspectives and challenges associated with these trends in both the aspects of mechanism and clinical translation.

scholarly journals Structural Health Monitoring of 2D Plane Structures

2021 ◽  
Vol 11 (5) ◽  
pp. 2000
Author(s):  
Behnam Mobaraki ◽  
Haiying Ma ◽  
Jose Antonio Lozano Galant ◽  
Jose Turmo
Keyword(s):  
Mechanical Properties ◽  
Health Monitoring ◽  
Mathematical Analysis ◽  
Stiffness Matrix ◽  
State Of The Art ◽  
System Of Equations ◽  
Matrix System ◽  
Structural System ◽  
Detailed Mathematical Analysis ◽  
Structural System Identification

This paper presents the application of the observability technique for the structural system identification of 2D models. Unlike previous applications of this method, unknown variables appear both in the numerator and the denominator of the stiffness matrix system, making the problem non-linear and impossible to solve. To fill this gap, new changes in variables are proposed to linearize the system of equations. In addition, to illustrate the application of the proposed procedure into the observability method, a detailed mathematical analysis is presented. Finally, to validate the applicability of the method, the mechanical properties of a state-of-the-art plate are numerically determined.

scholarly journals Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 864 ◽  
Author(s):  
Ju Wang ◽  
Nicolai Spicher ◽  
Joana M. Warnecke ◽  
Mostafa Haghi ◽  
Jonas Schwartze ◽  
...  
Keyword(s):  
Literature Review ◽  
Health Monitoring ◽  
Smart Home ◽  
State Of The Art ◽  
Home Health ◽  
Environmental Data ◽  
Behavioral Data ◽  
Sensor Technology ◽  
Current Sensor ◽  
Home Health Monitoring

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

scholarly journals Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation

2020 ◽  
Vol 34 (05) ◽  
pp. 8697-8704
Author(s):  
Pengjie Ren ◽  
Zhumin Chen ◽  
Christof Monz ◽  
Jun Ma ◽  
Maarten De Rijke
Keyword(s):  
Local Knowledge ◽  
State Of The Art ◽  
Background Knowledge ◽  
Global Perspective ◽  
Conversational Context ◽  
Human Evaluation ◽  
Text Fragment ◽  
Selection For ◽  
High Degree ◽  
Transition Vector

Background Based Conversation (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background knowledge (a text fragment containing related facts or comments, etc.) based on which to generate the next response. Previous work addresses KS by employing attention and/or pointer mechanisms. These mechanisms use a local perspective, i.e., they select a token at a time based solely on the current decoding state. We argue for the adoption of a global perspective, i.e., pre-selecting some text fragments from the background knowledge that could help determine the topic of the next response. We enhance KS in BBCs by introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp. In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. Experimental results show that the GLKS model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluation. More importantly, GLKS achieves this without requiring any extra annotations, which demonstrates its high degree of scalability.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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