Prediction of Technical State of Mechanical Systems Based on Interpretive Neural Network Model
Year: 2023
Type: article
Abstract: A classic problem in prognostic and health management (PHM) is the prediction of the remaining useful life (RUL). However, until now, there has been no algorithm presented to achieve perfect performance in this challenge. This study implements a less explored approach: binary classification of the state of mechanical systems at a given forecast horizon. To prove the effectiveness of the proposed approach, tests were conducted on the C-MAPSS sample dataset. The obtained results demonstrate the achievement of an almost maximal performance threshold. The explainability of artificial intelligence (XAI) using the SHAP (Shapley Additive Explanations) feature contribution estimation method for classification models trained on data with and without a sliding window technique is also investigated.
Source: Sensors
DOI: 10.3390/s23041892
Authors: Evgeniy Kononov, Andrey Klyuev, Mikhail Tashkinov
Institution: Perm National Research Polytechnic University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,669
OpenAlex ID: W4319600259