An online model-based fatigue life prediction approach using extended Kalman filter

Kuncham, Eshwar; Sen, Subhamoy; Kumar, Pankaj; Pathak, Himanshu

doi:10.1016/j.tafmec.2021.103143

Cited by 29 publications

(16 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Commonly used fusion methods involve Kalman-filter-based and particle-filter-based approaches. Eshwar et al [8] presented an online fatigue life prediction model based on an extended Kalman filter for civil infrastructure applications, while Guo et al [9] successfully predicted remaining effective flight cycles for aircraft auxiliary power units. It is important to note that equipment maintenance decision-making and optimization technology plays a crucial role in reliability engineering.…”

Section: Literature Reviewmentioning

confidence: 99%

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

View full text Add to dashboard Cite

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Cumulative in‐service fatigue loading is still one of the major causes of unexpected failures, 1 and it represents an important issue for designers. Although fatigue tests are often represented by simplified analyses, complexities such as stress/strain gradients, variable amplitude loading, randomness, and multiaxiality can easily be encountered in real cases 2 . Especially in such circumstances, finite element analysis (FEA) provides a valuable tool able to account for the complex features mentioned above 3–8 .…”

Section: Introductionmentioning

confidence: 99%

“…Although fatigue tests are often represented by simplified analyses, complexities such as stress/strain gradients, variable amplitude loading, randomness, and multiaxiality can easily be encountered in real cases. 2 Especially in such circumstances, finite element analysis (FEA) provides a valuable tool able to account for the complex features mentioned above. [3][4][5][6][7][8] The standard way to approach fatigue analysis consists of investigating the component's critical regions (i.e., considering stress/ strain gradients and multiaxiality) and applying the correct loading history (i.e., accounting for variable amplitude or randomness).…”

Section: Introductionmentioning

confidence: 99%

Closed‐form solution for the Fatemi‐Socie extended critical plane parameter in case of linear elasticity and proportional loading

Chiocca,

Sgamma,

Frendo

2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

Fatigue damage remains a significant issue for both metallic and non‐metallic components, being the main cause of in‐service failures. Among the different assessment methodologies, critical plane methods have gained significance as they enable identifying the critical location and the early crack propagation orientation. However, the standard plane scanning method used for calculating critical plane factors is computationally intensive, and as a result, it is usually applied only when the component's critical region is known in advance. In the presence of complex geometries, loads, or constraints, a more efficient method would be required. This work presents a closed‐form solution to efficiently evaluate a critical plane factor based on the Fatemi‐Socie criterion, in the case of isotropic linear‐elastic material behavior and proportional loading conditions. The proposed algorithm, based on tensor invariants and coordinate transformation laws, was tested on different case studies under various loading conditions, showing a significant reduction in computation time compared to the standard plane scanning method.

show abstract

“…Cumulative in-service fatigue loading is still one of the major causes of unexpected failures [1], and it represents an important issue for designers. Although fatigue tests are often represented by simplified analyses, complexities such as stress/strain gradients, variable amplitude loading, randomness and multiaxiality can easily be encountered in real cases [2]. Especially in such circumstances, finite element analysis (FEA) provides a valuable tool able to account for the complex features mentioned above [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Closed form solution for Fatemi-Socie critical plane method in case of linear elasticity and proportional loading

Chiocca

Sgamma

Frendo

2023

Preprint

View full text Add to dashboard Cite

The fatigue analysis of structural components is a relevant research topic in both scientific and industrial communities. Despite major advances in understanding, fatigue damage remains a significant issue for both metallic and non-metallic components, sometimes leading to unexpected failures of in-service parts. Among the different assessment methodologies, critical plane methods have gained significance as they enable identification of a component’s critical location and direction of early crack propagation. However, the standard plane scanning method for calculating critical plane factors is computationally intensive and, for that, it is only applied when the component critical regions are already known. When critical areas are not easily identifiable due to complex geometries, loads or constraints, a more efficient method for evaluating critical plane factors would be required. This work presents a closed form solution for efficiently evaluating the Fatemi-Socie critical plane factor, in case of linear-elastic material behaviour and proportional loading conditions, based on tensor invariants and coordinates transformation laws. The proposed algorithm was tested on different test cases (i.e. hourglass, notched and welded joint geometries) under different loading conditions (i.e. tensile, bending and torsion) and showed a significant reduction in computation time compared to the standard plane scanning method.

show abstract

An online model-based fatigue life prediction approach using extended Kalman filter

Cited by 29 publications

References 46 publications

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Closed‐form solution for the Fatemi‐Socie extended critical plane parameter in case of linear elasticity and proportional loading

Closed form solution for Fatemi-Socie critical plane method in case of linear elasticity and proportional loading

Contact Info

Product

Resources

About