  This paper presents a framework for estimating the remaining useful life
(RUL) of mechanical systems. The framework consists of a multi-layer perceptron
and an evolutionary algorithm for optimizing the data-related parameters. The
framework makes use of a strided time window to estimate the RUL for mechanical
components. Tuning the data-related parameters can become a very time consuming
task. The framework presented here automatically reshapes the data such that
the efficiency of the model is increased. Furthermore, the complexity of the
model is kept low, e.g. neural networks with few hidden layers and few neurons
at each layer. Having simple models has several advantages like short training
times and the capacity of being in environments with limited computational
resources such as embedded systems. The proposed method is evaluated on the
publicly available C-MAPSS dataset, its accuracy is compared against other
state-of-the art methods for the same dataset.
