Full-scale field evaluation of MEMS-based biaxial strain transducer and its applications in rail fatigue analysis.

摘要

The objective of this research is to evaluate an intelligent micro electromechanical system (MEMS) sensor in predicting railroad fatigue life based on strain history. A prototype Bi-Axial Strain Transducer (BiAST) was manufactured by Sarcos Research and deployed to collect real-time strain data from the full-scale test track at the Transportation Technology Center (TTCI) near Pueblo, Colorado.; Static and dynamic testing was performed at the 2.7-mile Facility for Accelerated Service Testing (FAST) loop using both TTCI's 605-calibration car and heavy axle loading trains respectively. The collected strain data were analyzed using the fatigue analysis program for counting the load cycles and estimating a fatigue life of a rail structure.; A three dimensional finite element model of the rail structure was developed to validate field results and to determine critical strain locations caused by train loading on the rail. In the field, the BiAST sensors were then mounted at these critical locations. The strain histories computed using the finite element model compared well with the field BiAST strain data collected at FAST tracks for both static and dynamic testing.; Fatigue life estimation utilized the strain-life approach. Rainflow cycle counting method was used for identifying damaging cycles. Morrow's fatigue life model was used for determining life estimation for each category of cycles and Palmgren-Miner's damage theory was used to estimate damage and estimate remaining fatigue life of rail.; Field-testing results of the BiAST were used to evaluate the prototype BiAST with respect to its repeatability, accuracy, and hybridization. BiAST was effective in detecting the dynamic response of a particular wheel and spurious overload events. BiAST can be used to detect passing wheels, the train speed, and track condition in addition to estimation of remaining fatigue life at critical locations.; In the future, the fatigue analysis software will be integrated into the programmable chip of the BiAST™ for automatically estimating the remaining service life of railroad track structure through an autonomous operation in remote locations.