Terrain Classification for Conditioned Based Maintenance
Open Access
- Author:
- Taylor, R Troy
- Graduate Program:
- Acoustics
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- September 02, 2014
- Committee Members:
- Karl Martin Reichard, Thesis Advisor/Co-Advisor
- Keywords:
- Conditioned based maintenance
acoustics
terrain classification
vibration - Abstract:
- Reducing the total cost of owning and operating equipment is an important goal for both the U.S. military and private industry. Key goals for logistics support equipment are increasing fuel efficiency and reducing maintenance costs. This research addresses techniques to classify terrain in order to better predict maintenance schedules, reduce subsystem damage and improve fuel efficiency in ground vehicles. The United States Army Material Systems Analysis Activity (AMSAA) has developed a measurement and processing approach algorithm for Terrain Regime Identification and Classification (TRIC) that classifies the harshness of the vehicle’s operating conditions based on the roughness (z-axis motion), bumpiness (magnitudes of vehicle pitch and roll), steepness of the terrain, and the vehicle’s speed. The terrain classification can contribute to maintenance prediction algorithms, as rates of damage to the various sub-systems of the vehicle should be correlated to the harshness of terrain traversed. The algorithm uses inputs from sensors measuring vehicle pitch and roll mounted on the vehicle body, vertical acceleration from a sensor mounted on the axle, vehicle speed, and position (via GPS). Spectra from a z-axis accelerometer mounted in the body of the vehicle have been compared to spectra from a z-axis accelerometer mounted on the axle of the vehicle to see if the same information regarding the roughness can be extracted in both cases, or if a translation model can be developed to produce comparable results, and potentially reduce instrumentation costs. The TRIC algorithm has been reproduced in MATLAB to see what additional information and uses can be obtained. The classifications from TRIC were compared to vehicle operating mode classifications previously developed by the Applied Research Laboratory (ARL) at Penn State based on vehicle operating data such as engine speed, vehicle speed and accelerator position. Color-coded classifications overlaid on maps of the test courses show operating modes as a function of the vehicle’s path. This overlay technique is also useful for comparing vehicle mode classification to terrain classification.