A hybrid physical model—neural network approach to machine diagnostics

Abstract

Neural network based diagnostic algorithms have been an effective approach to machine diagnostics. A feature extraction routine calculates parameters indicating the relative changes in signal phase along different paths to multiple accelerometers as might be due to localized damage along one path. These parameters were calculated for accelerometer data from the Westland seeded fault helicopter gearbox experiment. Perfect fault discrimination was achieved using a conventional feed forward neural network classifier on only a small training set, with no special treatment for the confounding influence of varying torque levels. A hybrid neural network was designed, which implemented a simple springs and masses mechanical model of a more complex structure, where sensor locations served as the mass points. This approach is able to localize structural damage using only a small training set on data generated by a finite elements model, and exhibits excellent interpolation and extrapolation. Extending the hybrid neural network approach, a method is proposed for solving for the external forces applied to surface-mounted sensors, from vibrating machine components. The derived forces will then serve as inputs to the signal phase change feature extraction routine. It is hoped this method will train well on small data sets and reduce ambiguities due to signal multipath effects. [See NOISE-CON Proceedings for full paper.]