  For health prognostic task, ever-increasing efforts have been focused on
machine learning-based methods, which are capable of yielding accurate
remaining useful life (RUL) estimation for industrial equipment or components
without exploring the degradation mechanism. A prerequisite ensuring the
success of these methods depends on a wealth of run-to-failure data, however,
run-to-failure data may be insufficient in practice. That is, conducting a
substantial amount of destructive experiments not only is high costs, but also
may cause catastrophic consequences. Out of this consideration, an enhanced RUL
framework focusing on data self-generation is put forward for both non-cyclic
and cyclic degradation patterns for the first time. It is designed to enrich
data from a data-driven way, generating realistic-like time-series to enhance
current RUL methods. First, high-quality data generation is ensured through the
proposed convolutional recurrent generative adversarial network (CR-GAN), which
adopts a two-channel fusion convolutional recurrent neural network. Next, a
hierarchical framework is proposed to combine generated data into current RUL
estimation methods. Finally, the efficacy of the proposed method is verified
through both non-cyclic and cyclic degradation systems. With the enhanced RUL
framework, an aero-engine system following non-cyclic degradation has been
tested using three typical RUL models. State-of-art RUL estimation results are
achieved by enhancing capsule network with generated time-series. Specifically,
estimation errors evaluated by the index score function have been reduced by
21.77%, and 32.67% for the two employed operating conditions, respectively.
Besides, the estimation error is reduced to zero for the Lithium-ion battery
system, which presents cyclic degradation.
