Signal processing by neural networks to create "virtual" sensors and model-based diagnostics

Abstract

This paper discusses the application of advanced neural network methods to the development of diagnostics for complex, nonlinear dynamical systems, for which accurate, first-principles models either do no exist or are difficult to derive. We consider two approaches to detect and identify failures in these systems. First, neural networks are trained to act as virtual sensors that emulate the performance of laboratory-quality sensors; this approach provides higher quality diagnostic information than is available directly from production sensors. Second, neural networks are trained to emulate nominal (fault-free) system behavior; model-based fault diagnosis is subsequently achieved by detecting significant deviations between actual and predicted system performance. We present experimental evidence of the viability of both approaches for a difficult automotive diagnostic task.