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The purpose of this paper was to verify, when using an oil debris sensor, that accumulated mass predicts gear pitting damage and to identify a method to set threshold limits for damaged gears. Oil debris data was collected from 8 experiments with no damage and 8 with pitting damage in the NASA Glenn Spur Gear Fatigue Rig. Oil debris feature analysis was performed on this data. Video images of damage progression were also collected from 6 of the experiments with pitting damage. During each test, data from an oil debris sensor was monitored and recorded for the occurrence of pitting damage. The data measured from the oil debris sensor during experiments with damage and with no damage was used to identify membership functions to build a simple fuzzy logic model. Using fuzzy logic techniques and the oil debris data, threshold limits were defined that discriminate between stages of pitting wear. Results indicate accumulated mass combined with fuzzy logic analysis techniques is a good predictor of pitting damage on spur gears. Dempsey, Paula J. Glenn Research Center NASA/TM-2001-210936, E-12789, NAS 1.15:210936
The first book on Prognostics and Health Management of Electronics Recently, the field of prognostics for electronic products has received increased attention due to the potential to provide early warning of system failures, forecast maintenance as needed, and reduce life cycle costs. In response to the subject's growing interest among industry, government, and academic professionals, this book provides a road map to the current challenges and opportunities for research and development in Prognostics and Health Management (PHM). The book begins with a review of PHM and the techniques being developed to enable a prognostics approach for electronic products and systems. building on this foundation, the book then presents the state of the art in sensor systems for in-situ health and usage monitoring. Next, it discusses the various models and algorithms that can be utilized in PHM. Finally, it concludes with a discussion of the opportunities in future research. Readers can use the information in this book to: Detect and isolate faults Reduce the occurrence of No Fault Found (NFF) Provide advanced warning of system failures Enable condition-based (predictive) maintenance Obtain knowledge of load history for future design, qualification, and root cause analysis Increase system availability through an extension of maintenance cycles and/or timely repair actions Subtract life cycle costs of equipment from reduction in inspection costs, down time, and inventory Prognostics and Health Management of Electronics is an indispensable reference for electrical engineers in manufacturing, systems maintenance, and management, as well as design engineers in all areas of electronics.
Gear cracks are typically difficult to diagnose with sufficient warning time. Significant damage must he present before algorithms detect the damage. A new feature extraction and two new detection techniques are proposed. The time synchronous averaging concept was extended from revolution-based to tooth engagement-based. The detection techniques are based on statistical comparisons among the averages for the individual teeth. These techniques were applied to a series of three seeded fault crack propagation tests. These tests were conducted on aerospace quality spur gears in a test rig. The tests were conducted at speeds ranging from 2500 to 7500 revolutions per minute and torque from 184 to 228 percent of design load. The inability to detect these cracks with high confidence may be caused by the high loading required to initiate the cracks. The results indicate that these techniques do not currently produce an indication of damage that significantly exceeds experimental scatter.
Health and Usage Monitoring System research and development involves analysis of the vibration signals produced by a gearbox throughout its life. There are two major advantages of knowing the actual lifetime of a gearbox component: safety and cost. In this report, a technique is proposed to help extract the critical data and present it in a manner that can be easy to understand. The key feature of the technique is to make it independent of speed, torque and prior history for localized, single tooth damage such as gear cracks. This extraction technique is demonstrated on two sets of digitized vibration data from cracked spur gears. Standard vibration diagnostic parameters are calculated and presented for comparison. Several new detection algorithms are also presented. The results of this study indicate that crack detection methods examined are not robust or repeatable. The proposed techniques provide a limited improvement to existing diagnostic parameters. Current techniques show that the cracks progressed at a much faster rate than anticipated which reduced available time for detection.
This Proceedings contains the papers presented at the 14th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2001), held in Manchester, UK, on 4-6 September 2001. COMADEM 2001 builds on the excellent reputation of previous conferences in this series, and is essential for anyone working in the field of condition monitoring and maintenance management.The scope of the conference is truly interdisciplinary. The Proceedings contains papers from six continents, written by experts in industry and academia the world over, bringing together the latest thoughts on topics including: Condition-based maintenance Reliability centred maintenance Asset management Industrial case studies Fault detection and diagnosis Prognostics Non-destructive evaluation Integrated diagnostics Vibration Oil and debris analysis Tribology Thermal techniques Risk assessment Structural health monitoring Sensor technology Advanced signal processing Neural networks Multivariate statistics Data compression and fusion This Proceedings also contains a wealth of industrial case studies, and the latest developments in education, training and certification. For more information on COMADEM's aims and scope, please visit http://www.comadem.com