| other soa research | Applied Research Laboratory at Penn State |
Advanced Sensors & Controls
Condition-Based Maintenance
Battlefield Surveillance - Ground sensors detect and communicate movements of enemy forces to scout vehicles. Scout vehicles confirm the detections and relay the information to the command post.
| Environmental Sensing Technology - This technology uses sound propagation models to estimate detection range in both day- and night-time situations, incorporating meteorological sensors to model environmental conditions. This work has significantly advanced night-time range detection. Furthermore, it can be used to simulate the performance of air-dropped passive acoustic ground sensors, generating valuable information pertaining to the placement and use of tactical decision aids for soldiers. | |
| Machinery Prognostics - Our hierarchical system architecture involves multiple component-level health monitors (ICHM) that report component operating conditions to a system-level health monitor (SHM). The SHMs, in turn, monitor the ICHMs and alert a top-level management/repair center if an alert condition arises. The SHMs also maintain a library of machine health information for each component. |  |
Roller Bearing Diagnostics - This involves identifying signal processing algorithms and developing new sensor designs and techniques to create a low-cost, high-reliability system to monitor bearing performance without affecting bearing operation or installation. Such a system would allow bearing problems to be identified earlier, reducing operating expenses and unexpected failures and have a wide variety of applications.
Other Applications - Sensor networks for:
Multi-Disciplinary University Research Initiative: Integrated Predictive Diagnostics - The MURI IPD is a multi-year condition-based maintenance research effort sponsored by the Office of Naval Research (ONR). It addresses issues pertaining to diagnosing the current state of mechanical systems and predicting the systems' remaining useful lives. Research will lead to the development of military and commercial monitoring systems, improved system reliability and human safety, extended platform life, and reduced maintenance and lifecycle costs.
NASA Condition-Based Maintenance - The NASA CBM effort involves several of NASA Ames Research Center's wind tunnels. This program focuses on providing an effective CBM system for the large machinery, which is costly to repair and often impossible to replace. The program includes:
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Accelerated capabilities Initiative for Machinery Prognostics and Diagnostics - The ACI is a three-year, ONR-sponsored program to develop sensor/processor systems that can accurately monitor the health of mechanical components and systems and predict their remaining useful life. The ACI will demonstrate the application of sensor hardware, diagnostic techniques, and networked systems on the 501-K34 Ship Service Gas Turbine Generator (SSGTG) Set for the DDG-51 Arleigh Burke class of ships. (See Machinery Prognostics above.)
| Torsional Vibration Studies - This effort focuses on using torsional vibration measurements to detect blade cracking in rotors. The CBM department developed an optimized transduction method to detect vibration signals, demonstrated the viability of using torsional vibration to detect blade cracking, and used Finite Element Modeling to determine the coupling effects between blade bending vibration and rotor torsion. |  |
Diagnostic Effectiveness Metrics - The CBM department and its partner, Impact Technologies, have developed metrics and algorithms capable of impartially evaluating the performance and effectiveness of diagnostic and prognostic technologies. Using an evolving database of in-service, simulated, and test bed data, we performed feature extraction on sensor data and used the extracted features to develop and test the effectiveness metrics. These metrics enable organizations that are interested in integrating diagnostic/prognostic systems into their CBM systems, to quickly and inexpensively verify the effectiveness and costs of such systems.
Large Scale Bearing Diagnostics - This effort provided assessment and recommendations for data acquisition systems and feature extraction techniques used to monitor bearing health on the Mk13 Guided Missile Launching System. The UDLP identified a data collection, processing, archival and management scheme that will generate and maintain a historical database for performing component health assessments and track component failure trends among multiple system platforms. It also investigated various data acquisition systems and examined several time domain and frequency domain analysis methods.
Fuel System Diagnostics - The project team is developing and implementing a prognostics and health management methodology for the fuel system of the General Electric Joint Strike Fighter alternative engine. The project involves developing an approach, using physics-based models, data fusion, and automated reasoning for tracking and predicting LRU degradation. The project is currently in its first phase, which involves developing a model of the turbojet fuel system to provide features for detecting and diagnosing several failure mode symptoms.
| Reduced Ships-crew by Virtual Presence (RSVP) - This ONR-sponsored project will incorporate commercial technologies and demonstrate operational monitoring in four functional areas: Environment, Machinery, Structure, and Personnel. For structural and environmental monitoring, MEMS technology will be leveraged in the form of wireless sensor clusters mounted strategically throughout the space. For machinery health monitoring, RSVP will transition the results of the ACI Program (described above). |  Click on RSVP Illustration to download a full view in PDF format |
A DARPA-developed personnel status monitor, manufactured by Sarcos, will be used in monitoring personnel. All data will be intelligently fused at multiple levels within the wireless network and will be transmitted to an operator at a remote workstation. |
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Defense University Research Instrumentation Program (DURIP): Enhanced Data Collection for Condition-Based Maintenance - A critical element of CBM is the collection of transitional data as machinery evolves from normal operation to failure. This program will provide enhanced borescope imaging to estimate damage, in situ oil analysis for gearbox and engine failure tests, thermal imaging for bearing and engine hotside fault detection, and torsional vibration studies to improve gear/shaft fault tracking and correlation with prognostic models. Co-participants include Rensselaer Polytechnic Institute and Tennessee State University.
