  Prognostics and Health Management (PHM) is an emerging engineering discipline
which is concerned with the analysis and prediction of equipment health and
performance. One of the key challenges in PHM is to accurately predict
impending failures in the equipment. In recent years, solutions for failure
prediction have evolved from building complex physical models to the use of
machine learning algorithms that leverage the data generated by the equipment.
However, failure prediction problems pose a set of unique challenges that make
direct application of traditional classification and prediction algorithms
impractical. These challenges include the highly imbalanced training data, the
extremely high cost of collecting more failure samples, and the complexity of
the failure patterns. Traditional oversampling techniques will not be able to
capture such complexity and accordingly result in overfitting the training
data. This paper addresses these challenges by proposing a novel algorithm for
failure prediction using Generative Adversarial Networks (GAN-FP). GAN-FP first
utilizes two GAN networks to simultaneously generate training samples and build
an inference network that can be used to predict failures for new samples.
GAN-FP first adopts an infoGAN to generate realistic failure and non-failure
samples, and initialize the weights of the first few layers of the inference
network. The inference network is then tuned by optimizing a weighted loss
objective using only real failure and non-failure samples. The inference
network is further tuned using a second GAN whose purpose is to guarantee the
consistency between the generated samples and corresponding labels. GAN-FP can
be used for other imbalanced classification problems as well.
