Application of machine learning methods in multiaxial fatigue life prediction

Pałczyński, Krzysztof; Skibicki, Dariusz; Pejkowski, Łukasz; Andrysiak, Tomasz

doi:10.1111/ffe.13874

Cited by 23 publications

(6 citation statements)

References 56 publications

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“…In the last decades, there has been a great expansion in the application of signal processing techniques. Machine Learning (ML) techniques have been widely used owing to their successful results in predictive problems [15]. However, they are considered black boxes, since it is not possible to establish a relation between their inputs and outputs.…”

Section: Introductionmentioning

confidence: 99%

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

Marichal,

Hernández,

Ávila

et al. 2024

Applied Sciences

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The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built.

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Section: Introductionmentioning

confidence: 99%

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

Marichal,

Hernández,

Ávila

et al. 2024

Applied Sciences

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“…These models enable the learning of physical laws from different data types in the absence of explicit knowledge 6 . For instance, Pałczynski et al 9 compared the results of multiaxial fatigue life estimation using ML methods and classical fatigue models. Considering the nonlinear relationship between variables and fatigue life and the computational burden, Zhou et al 10 proposed a ML method integrating the artificial neural network (ANN) and partial least squares (PLS) algorithm as a framework to identify the genetic features through optimizing fatigue life prediction.…”

Section: Introductionmentioning

confidence: 99%

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Heng

Gao

et al. 2023

Fatigue Fract Eng Mat Struct

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A new algorithm optimization‐based hybrid neural network model is proposed in the present study for the multiaxial fatigue life prediction of various metallic materials. Firstly, a convolutional neural network (CNN) is applied to extract the in‐depth features from the loading sequence composed of the critical fatigue loading conditions. Meanwhile, the multiaxial historical loading information with time‐series features is retained. Then, a long short‐term memory (LSTM) network is adopted to capture the time‐series features and in‐depth features of the CNN output. Finally, a full connection layer is used to achieve dimensional transformation, which makes the fatigue life predictable. Herein, the hyperparameters of the LSTM network are automatically determined using the slime mold algorithm (SMA). The test results demonstrate that the proposed model has pleasant prediction performance and extrapolation capability, and it is suitable for the life prediction of various metallic materials under uniaxial, proportional multiaxial, nonproportional multiaxial loading conditions.

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“…Deep learning is also a method that researchers use to predict fatigue life 18–26 . Deep learning is a tool for learning the inherent laws of sample data aimed at fitting an optimal analytical function between input and output.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning is also a method that researchers use to predict fatigue life. [18][19][20][21][22][23][24][25][26] Deep learning is a tool for learning the inherent laws of sample data aimed at fitting an optimal analytical function between input and output. Unlike life prediction models based on fatigue test results, deep learning is a potential method that is not limited by loading conditions and materials.…”

mentioning

confidence: 99%

A physics‐informed generative adversarial network framework for multiaxial fatigue life prediction

He,

Zhao,

Yan

2023

Fatigue Fract Eng Mat Struct

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Deep learning has achieved great success in multiaxial fatigue life prediction. However, when data‐driven models are used to describe data from physical processes, the relationship between inputs and outputs is agnostic. This paper proposes a deep learning framework combining generative adversarial networks and physical models to predict multiaxial fatigue life. This framework incorporates three life prediction equations in the loss function of generator, respectively. The results show that models with suitable physical constraints outperform neural networks in predicting results. Introducing the Smith–Watson–Topper model as a physical loss degrades the predictive performance of the physics‐informed network. On the contrary, introducing Fatemi–Socie and Shang–Wang model as physical loss improves the predictive performance of physics‐informed network. Learning using physics knowledge can lead to the ability of model to generate data that satisfy the governing equations of physics.

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Application of machine learning methods in multiaxial fatigue life prediction

Cited by 23 publications

References 56 publications

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

A physics‐informed generative adversarial network framework for multiaxial fatigue life prediction

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