  This paper concerns the use of neural networks for predicting the residual
life of machines and components. In addition, the advantage of using
condition-monitoring data to enhance the predictive capability of these neural
networks was also investigated. A number of neural network variations were
trained and tested with the data of two different reliability-related datasets.
The first dataset represents the renewal case where the failed unit is repaired
and restored to a good-as-new condition. Data was collected in the laboratory
by subjecting a series of similar test pieces to fatigue loading with a
hydraulic actuator. The average prediction error of the various neural networks
being compared varied from 431 to 841 seconds on this dataset, where test
pieces had a characteristic life of 8,971 seconds. The second dataset was
collected from a group of pumps used to circulate a water and magnetite
solution within a plant. The data therefore originated from a repaired system
affected by reliability degradation. When optimized, the multi-layer perceptron
neural networks trained with the Levenberg-Marquardt algorithm and the general
regression neural network produced a sum-of-squares error within 11.1% of each
other. The potential for using neural networks for residual life prediction and
the advantage of incorporating condition-based data into the model were proven
for both examples.
