# A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems

## TL;DR

This paper introduces a neural network and evolutionary algorithm-based framework for estimating the remaining useful life of mechanical systems, emphasizing automatic data reshaping and low-complexity models for efficiency.

## Contribution

It presents a novel framework combining neural networks and evolutionary algorithms with automatic data reshaping for RUL estimation, suitable for resource-limited environments.

## Key findings

- Achieves competitive accuracy on C-MAPSS dataset
- Uses simple neural networks for faster training
- Demonstrates effectiveness in resource-constrained settings

## Abstract

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.