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Neural networks are massively parallel computational models for knowledge representation and information processing. The capabilities of neural networks, namely learning, noise tolerance, adaptivity, and parallel structure make them good candidates for application to a wide range of engineering problems including diagnostics problems. The general approach in developing neural network based diagnostic methods is described through a case study. The development of an acoustic wayside train inspection system using neural networks is described. The study is aimed at developing a neural network based method for detection defective wheels from acoustic measurements. The actual signals recorded when a train passes a wayside station are used to develop a neural network based wheel defect detector and to study its performance. Signal averaging and scoring techniques are developed to improve the performance of the constructed neural inspection system.

The US Army uses a light tactical High-Mobility Multi-Purpose Wheeled Vehicle (HMMWV) which, due to the amount of armor added, requires air conditioning to keep its occupants comfortable. The current system uses R134a in a dual evaporator, remote-mounted condenser, engine-driven compressor system. This vehicle has been adapted to use an environmentally friendly refrigerant (carbon dioxide) to provide performance, efficiency, comfort and logistical benefits to the Army. The unusual thermal heat management issues and the fact that the vehicle is required to operate under extreme ambient conditions have made the project extremely challenging. This paper is a continuation of work presented at the SAE Alternate Refrigerants Symposium held in Phoenix last June [1].