Real-Time Diagnostics, Prognostics and Health Management for Large-Scale Manufacturing Maintenance Systems

2008 ◽  
Author(s):  
Leandro G. Barajas ◽  
Narayan Srinivasa
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Business Processes ◽  
Health Management ◽  
Real Life ◽  
Lessons Learned ◽  
Multidimensional Data ◽  
Design Development ◽  
Maintenance Systems ◽  
Tactical Decisions

Traditional technologies emphasize either experience or model-based approaches to the Diagnostics, Prognostics & Health Management (DPHM) problem. However, most of these methodologies often apply only to the narrow type of machines that they were developed for, and only support strategic level assessments as opposed to real-time tactical decisions. By enabling widespread integration of diagnostics and prognostics into our manufacturing business processes, we have reduced spacio-temporal uncertainties associated with future states and system performance and therefore enabled more informed and effective decisions on manufacturing activities. For large-scale systems, the usual approach is to aggregate multidimensional data into a single-dimensional stream. These methods are generally adequate to extract key performance indicators. However, they only point to observable effects of a failure and not to their root causes. An integrated framework for DPHM requires the availability of bidirectional cause-effect relationships that enable system-wide health management rather than just predicting what its future state would be. This paper summarizes best practices, benchmarks, and lessons learned from the design, development, deployment, and execution of DPHM systems into real-life applications in the automotive industry.

Real-Time Public Transport Operations: Library of Control Strategies

2017 ◽  
Vol 2647 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Mahmood Mahmoodi Nesheli ◽  
Avishai (Avi) Ceder
Keyword(s):  
Real Time ◽  
Public Transport ◽  
Large Scale ◽  
Real Life ◽  
Small Scale ◽  
Control Procedure ◽  
Service Reliability ◽  
Real Time Control ◽  
Time Control ◽  
Control Actions

Modern public transport (PT) operations have evolved into a complex multimodal system in which small-scale disorder can propagate. Large-scale disruptions to passengers and PT agencies result. Various studies have been developed to model PT control at the operational level; however, the main downside of possible real-time control actions is the lack of intelligent modeling and a systematic process that can activate such actions immediately. This study presents a real-time control procedure to increase service reliability and to improve successful coordinated transfers in a complex PT system. The developed method aims at minimizing total travel time for passengers and reducing the uncertainty of meetings between PT vehicles. A library of operational tactics is first built to serve as a basis of the real-time decision-making process. The methodology developed is applied to a real-life case study in Auckland, New Zealand. The results showed improvements in system performance and confirmed the use of real-time control actions to maintain reliable PT service.

scholarly journals Real-time evaluation of compaction quality based on RF- ACGWO with high robustness and generalization ability

2021 ◽  
Vol 9 ◽  
Author(s):  
Weiwei Lin ◽  
◽  
Bo Cui ◽  
Jiajun Wang ◽  
Dong Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Prediction Models ◽  
Evaluation Model ◽  
Real Life ◽  
Three Dimensions ◽  
Engineering Project ◽  
Practical Evaluation ◽  
Project Site ◽  
Time Evaluation

The effective evaluation of compaction quality is a key issue for the safety of earth-rock dams. However, existing prediction models of compaction quality are designed to improve prediction accuracy but generally ignore generalizability and robustness, resulting in deviations from practical evaluation results, making these models inapplicable to complex construction environments. To address these problems, a novel real-time evaluation model for construction unit compaction quality based on random forest optimized by adaptive chaos grey wolf algorithm (RF-ACGWO) is proposed in this article. In RF-ACGWO, RF predicts compaction quality, while ACGWO increases efficiency and accuracy for traditional RF parameter selection and improves the generalizability and robustness of the model. Also, meteorological factors at a project site are also considered to affect the model, thereby improving model accuracy. After embedding the proposed method in a Three-Dimensions (3D) rolling monitoring system, real-time evaluation, guidance and feedback on a project site can be obtained. Compared to the conventional evaluation methods, RF-ACGWO achieves the highest accuracy of 0.838, the best generalizability of 0.793 and the most stable robustness when applied to a large-scale, real-life hydraulic engineering project.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

scholarly journals FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 1495-1501
Author(s):  
Hui Huang ◽  
Zhe Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Real Time ◽  
Large Scale ◽  
Real Life ◽  
Detection Algorithm ◽  
False Alarms ◽  
License Plate ◽  
Embedded Devices ◽  
License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

Making HIS More Agile, Relevant and Public Health Friendly: Lessons from Covid-19 in India

2021 ◽  
Author(s):  
Sundeep Sahay ◽  
Arunima S Mukherjee ◽  
Carolyn K Tauro ◽  
Arijit Sen
Keyword(s):  
Public Health ◽  
Information Systems ◽  
Health Systems ◽  
Large Scale ◽  
Health Management ◽  
Digital Health ◽  
Digital Technologies ◽  
Design Development ◽  
Anthony Giddens ◽  
Public Health Information

Anthony Giddens, the noted sociologist, describes the COVID-19 pandemic as a ‘digidemic,’ emphasizing the inextricable linkages between the pandemic and the digital. As the pandemic has spread globally, countries have adopted different strategies to leverage digital technologies, in their design, development, implementation, and governance to address the pandemic. Some of these strategies have worked well and others have not so. We submitted this paper at the time when India was fighting the first COVID-19 wave and are submitting this revised version as India fights a much tougher second wave. And between these two waves, we have witnessed some flattening of the COVID-19 curve and the onset of a rigorous vaccination drive. This paper aims to try to analyse some experiences of how countries leveraged digital technologies in their information systems response, such as from Sri Lanka, South Korea and anchored in a historical understanding of public Health Information Systems (HIS) in India, build key learnings for strengthening HIS in India, both for pandemic situations and also routine health management. These include i) improving agility, reflecting the ability of the HIS to provide timely information for supporting local action; ii) improving relevance, implying providing required information for supporting the desired action for different stakeholders; and, iii) public friendliness, implying the HIS should help support population health at large in an equitable manner. We argue that these learnings are not only relevant for strengthening the HIS response to pandemic management but also more broadly for strengthening Indian public HIS covering routine systems. These learnings are particularly pertinent in the current ‘digital’ context in India, where large-scale interventions related to the National Digital Health Mission are currently being planned and implemented. For good or bad, the ‘digital’ is inevitable in public health systems globally, and it becomes important for researchers and practitioners to engage with this process of understanding the digital interventions and contribute to strengthening the health systems.

scholarly journals Use of Evidence-Centered Design to Develop Learning Maps-Based Assessments

2020 ◽  
Author(s):  
Amy K. Clark ◽  
Meagan Karvonen
Keyword(s):  
Large Scale ◽  
Test Development ◽  
Design Principles ◽  
Lessons Learned ◽  
Assessment System ◽  
Alternate Assessment ◽  
Evidence Based ◽  
Design Development ◽  
Validity Argument ◽  
Assessment Design

Evidence-based approaches to assessment design, development, and administration provide a strong foundation for an assessment’s validity argument but can be time consuming, resource intensive, and complex to implement. This paper describes an evidence-based approach used for one assessment that addresses these challenges. Evidence-centered design principles were applied to create a task template to support test development for a new, instructionally embedded, large-scale alternate assessment system used for accountability purposes in 18 U.S. states. Example evidence from the validity argument is presented to evaluate the effectiveness of the template as an evidence-based method for test development. Lessons learned, including strengths and challenges, are shared to inform test-development efforts for other programs.

scholarly journals Hardware-in-the-Loop Simulation Using Real-Time Hybrid-Simulator for Dynamic Performance Test of Power Electronics Equipment in Large Power System

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3955
Author(s):  
Jiyoung Song ◽  
Kyeon Hur ◽  
Jeehoon Lee ◽  
Hyunjae Lee ◽  
Jaegul Lee ◽  
...  
Keyword(s):  
Power System ◽  
Power Electronics ◽  
Real Time ◽  
Large Scale ◽  
Performance Test ◽  
Dynamic Performance ◽  
Lessons Learned ◽  
Hardware In The Loop ◽  
Large Power ◽  
Hybrid Simulator

This paper presents the hardware-in-the-loop simulation for dynamic performance test (HILS-DPT) of power electronic equipment replicas using a real-time hybrid simulator (RTHS). The authors developed the procedure of HILS-DPT, and as an actual case example, the results of HILS-DPT of Static VAR Compensator (SVC) replica using RTHS is presented. RTHS is a co-simulation tool that synthesizes real-time simulator (RTS) with transient stability program to perform real-time dynamic simulation of a large power system. As power electronics applications have been increasing, the electric utilities have performed HILS-DPT of the power electronics equipment to validate the performance and investigate interactions. Because inspection tests are limited in their ability to validate its impact on the power system during various contingencies, all power electronics equipment newly installed in the Korean power system should take HILS-DPT using large-scale RTS with replicas since 2018. Although large-scaled RTS offers an accuracy improvement, it requires lots of hardware resources, time, and effort to model and simulate the equipment and power systems. Therefore, the authors performed SVC HILS-DPT using RTHS, and the result of the first practical application of RTHS present feasibility comparing the result of HILS-DPT using large-scale RTS. The authors will discuss the test results and share lessons learned from the industrial experience of HILS-DPT using RTHS.

scholarly journals Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles

2007 ◽  
Vol 347 ◽  
pp. 57-66 ◽  
Author(s):  
Alexander Tessler
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Health Management ◽  
Space Vehicle ◽  
Scale Space ◽  
Distributed Sensors ◽  
Full Field ◽  
Real Time Processing ◽  
Structural Health ◽  
Structural Health Management

Two finite-element-based, full-field computational methods and algorithms for use in Structural Health Management (SHM) systems are reviewed. Their versatility, robustness, and computational efficiency make them well suited for real-time, large-scale space vehicle, structures, and habitat applications. The methods may be effectively employed to enable real-time processing of sensing information, specifically for identifying three-dimensional deformed structural shapes as well as the internal loads. In addition, they may be used in conjunction with evolutionary algorithms to design optimally distributed sensors. These computational tools have demonstrated substantial promise for utilization in future SHM systems.

Experiences With and Lessons Learned From Developing, Implementing, and Evaluating a Support Program for Older Hearing Aid Users and Their Communication Partners in the Hearing Aid Dispensing Setting

2020 ◽  
Vol 29 (3S) ◽  
pp. 638-647 ◽  
Author(s):  
Janine F. J. Meijerink ◽  
Marieke Pronk ◽  
Sophia E. Kramer
Keyword(s):  
Large Scale ◽  
Hearing Aid ◽  
Critical Discussion ◽  
Lessons Learned ◽  
Research Note ◽  
Support Program ◽  
Large Sample Size ◽  
Long Term Effects ◽  
Communication Program ◽  
Communication Partners

Purpose The SUpport PRogram (SUPR) study was carried out in the context of a private academic partnership and is the first study to evaluate the long-term effects of a communication program (SUPR) for older hearing aid users and their communication partners on a large scale in a hearing aid dispensing setting. The purpose of this research note is to reflect on the lessons that we learned during the different development, implementation, and evaluation phases of the SUPR project. Procedure This research note describes the procedures that were followed during the different phases of the SUPR project and provides a critical discussion to describe the strengths and weaknesses of the approach taken. Conclusion This research note might provide researchers and intervention developers with useful insights as to how aural rehabilitation interventions, such as the SUPR, can be developed by incorporating the needs of the different stakeholders, evaluated by using a robust research design (including a large sample size and a longer term follow-up assessment), and implemented widely by collaborating with a private partner (hearing aid dispensing practice chain).

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

Sign in / Sign up

Export Citation Format

Share Document