Professional Resources
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Professional Resources
Published online: 15 December 2013 Ó ASM International 2013
First Guidelines Created for Structural Health Monitoring of Commercial Aircraft SAE International has created the first-ever industry guidelines for structural health monitoring (SHM) of commercial aircraft. The document—Aerospace Recommended Practice ARP6461: ‘‘Guidelines for Implementation of Structural Health Monitoring on Fixed Wing Aircraft’’—was produced by an SAE technical committee consisting of the world’s leading aircraft manufacturers, systems and equipment integrators, regulators, airlines, and technical experts. The publication of the guidelines was the culmination of six years of work by SAE committee G-11SHM, which was launched by Stanford University professor Fu-Kuo Chang. The guidelines detail the steps necessary to incorporate built-in sensors on aircraft that can monitor such operating conditions as load and stress, as well as the occurrence and extent of damage. Using advanced sensor technology will enable aircraft operators to improve maintenance practices and streamline inspections. The SAE committee has started adapting ARP6461 for implementing SHM in military aircraft applications. In addition, a new rotorcraft SHM subgroup was formed. For more information: SAE International, 400 Commonwealth Drive, Warrendale, PA 15096-0001; tel: 724/776-4841; fax: 724/776-0790; web: http://standards.sae.org/arp6461.
industrial machines, and the research is detailed in the publication Fault Detection: Classification, Techniques and Role in Industrial Systems, from Nova Science Publishers, Inc. Jun Chen, Michael Gallimore, Chris Bingham, and Yu Zhang, from the University of Lincoln (UK), together with Mahdi Mahfouf, from the University of Sheffield, have developed an algorithm that is more robust and efficient at identifying specific faults in automated mechanical processes. The method combines two existing mathematical models for fault detection: a real-coded genetic algorithm (GA), and a K-means clustering methodology. The team discovered that the combination of these two processes into the G3Kmeans algorithm is more effective in quickly obtaining an optimal solution, requiring only eleven repetitions to detect a certain fault, whereas previous intuitive GA-based clustering methods go through more than 1000. With this improved detection and classification of faults, the time and money wasted on investigating false alarms will be reduced and a machine will be operational for longer periods. For more information: Nova Science Publishers, Inc., 400 Oser Ave., Ste. 1600, Hauppauge, NY 11788-3619; tel: 631/231-7269; fax: 631/231-8175; e-mail: nova.main@ novapublishers.com; web: www.novapublishers.com/ catalog/product_info.php?products_id=45556.
Simulations Enables Lifespan Predictions for Solar Modules Book Describes Streamlined Process for Machinery Fault Detection Academics from the Lincoln School of Engineering have devised a streamlined process that can detect faults in
Part of a project called Reliability of Photovoltaic
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