Study on Robust Loss Function for Artificial Neural Networks Models in Reliability Analysis

Abstract

This paper presents a robust artificial neural network (ANN) loss function in solving samples with questionable data. The Latin hypercube sampling and the uniform experimental design are used as sample generation while ANN with various loss functions is the limit state function approaching method and Monte Carlo simulation is the failure probability computing technique. A marine lubricating oil cooler under a plus-minus triangular wave and internal pressure (1Mpa) is considered as representative of the dynamic implicit model. Three kinds of questionable data are considered: tiny vibrations(±δ₁) caused by the surrounding environment, small deviations caused by equipment error(±δ₂), and large deviations(±Δ) caused by accidental events. And two small probability events are considered: 1. Surrounding error + positive equipment error + a large shock (±Δ) in partial samples; 2. Surrounding error + negative equipment error + a large shock (±Δ) in partial samples. ANN with advanced loss function performs better than traditional loss function in dealing with samples including incorrect data.