A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems
This work addresses the challenge of efficient RUL estimation for mechanical systems, particularly in resource-constrained environments like embedded systems, though it appears incremental as it builds on existing neural network and evolutionary approaches.
The paper tackles the problem of estimating the remaining useful life of mechanical systems by proposing a framework that combines a multi-layer perceptron with an evolutionary algorithm to optimize data parameters, achieving competitive accuracy on the C-MAPSS dataset compared to state-of-the-art methods.
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.