Neural Network Models to Anticipate Failures of Airport Ground Transportation Vehicle Doors

This paper describes a case study of the development and testing of a prototype system to support condition-based maintenance of the door systems of airport transportation vehicles. Every door open/close cycle produces a ¿signature¿ that can indicate the current degradation level of the door system. A combined statistical and neural network approach was used. Time, electrical current and voltage signals from the open/close cycles are processed in real-time to estimate, using the neural network, the condition of the door set relative to maintenance needs. Data collection hardware for the vehicle was designed, developed and tested to monitor door characteristics to quickly predict degraded performance, and to anticipate failures. The prototype system was installed on vehicle door sets at the Pittsburgh International Airport and tested for several months under actual operating conditions.