SCUBA: Focus and Context for Real-Time Mesh Network Health Diagnosis

2008 ◽  
pp. 162-171 ◽  
Author(s):  
Amit P. Jardosh ◽  
Panuakdet Suwannatat ◽  
Tobias Höllerer ◽  
Elizabeth M. Belding ◽  
Kevin C. Almeroth
Keyword(s):  
Real Time ◽  
Mesh Network ◽  
Health Diagnosis

Image based portable wear debris analysis tool

2015 ◽  
Vol 67 (4) ◽  
pp. 389-398 ◽  
Author(s):  
Muhammad Ali Khan ◽  
Ahmed Farooq Cheema ◽  
Sohaib Zia Khan ◽  
Shafiq-ur-Rehman Qureshi
Keyword(s):  
Real Time ◽  
Wear Debris ◽  
Machine Component ◽  
Analysis Tool ◽  
Content Type ◽  
Real Time Analysis ◽  
Distribution Shape ◽  
Basic Features ◽  
Health Diagnosis ◽  
Wear Debris Analysis

Purpose – The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color. Design/methodology/approach – It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis. Findings – The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron. Originality/value – Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

A Design for Fault-Tolerant Communication Middleware Based on Time-Triggered

2014 ◽  
Vol 548-549 ◽  
pp. 1326-1329
Author(s):  
Juan Jin ◽  
Qing Fan Gu
Keyword(s):  
Fault Tolerance ◽  
Real Time ◽  
Fault Location ◽  
Fault Tolerant ◽  
Application Software ◽  
Tolerance Mechanisms ◽  
The Real ◽  
Communication Middleware ◽  
Fault Mechanism ◽  
Health Diagnosis

Against to the unsustainable problems of health diagnosis, fault location and fault tolerance mechanisms that existing in the current avionics applications, we proposed a fault-tolerant communication middleware which is based on time-triggered in this paper. This middleware is designed to provide a support platform for applications of the real-time based on communication middleware. From the communication middleware level and also combined with time-triggered mechanism and fault-tolerant strategy, it diagnoses the general faults first, and then routes them to the appropriate fault mechanism to process it. So the middleware completely separates fault-tolerant process from the application software functions.

Developing a public information and engagement portal of urban waterways with real-time monitoring and modeling

2011 ◽  
Vol 63 (2) ◽  
pp. 248-254 ◽  
Author(s):  
T. A. Cochrane ◽  
D. Wicke ◽  
A. O’Sullivan
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Mesh Network ◽  
Web Portal ◽  
Alert System ◽  
Urban Waterways ◽  
Effective Manner ◽  
Urban Catchments

Waterways can contribute to the beauty and livelihood of urban areas, but maintaining their hydro-ecosystem health is challenging because they are often recipients of contaminated water from stormwater runoff and other discharges. Public awareness of local waterways’ health and community impacts to these waterways is usually poor due to of lack of easily available information. To improve community awareness of water quality in urban waterways in New Zealand, a web portal was developed featuring a real-time waterways monitoring system, a public forum, historical data, interactive maps, contaminant modelling scenarios, mitigation recommendations, and a prototype contamination alert system. The monitoring system featured in the web portal is unique in the use of wireless mesh network technology, direct integration with online modelling, and a clear target of public engagement. The modelling aims to show the origin of contaminants within the local catchment and to help the community prioritize mitigation efforts to improve water quality in local waterways. The contamination alert system aims to keep managers and community members better informed and to provide a more timely response opportunity to avert any unplanned or accidental contamination of the waterways. Preliminary feedback has been positive and is being supported by local and regional authorities. The system was developed in a cost-effective manner providing a community focussed solution for quantifying and mitigating key contaminants in urban catchments and is applicable and transferable to other cities with similar stormwater challenges.

A Hybrid Inference Approach for Health Diagnostics With Unexampled Faulty States

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Power Transformer ◽  
Roller Bearing ◽  
Health State ◽  
Health States ◽  
System Failures ◽  
State Classification ◽  
Testing Stage ◽  
Health Diagnosis

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.

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