Remaining useful life prediction for auxiliary power unit based on particle filter

Guo, Jiachen; Cai, Jing; Jiang, Heng; Li, Xin

doi:10.1177/0954410020940882

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…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%

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

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

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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.

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Section: Literature Reviewmentioning

confidence: 99%

“…. N , and particle filtering uses a combination of both to represent the state posterior probability distribution [9]:…”

Section: Rul Prediction Based On Particle Filtering Algorithmmentioning

confidence: 99%

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

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

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“…For instance, as illustrated in Figure 1, the two APUs had already failed when their Exhaust Gas Temperature (EGT) reached the limit. 3 There are obvious scratches on the turbine rotor blades of the APU in Figure 1(a) and the repair cost was US$ 299,759. Corrosion is found obviously on the turbine of the APU in Figure 1(b) and the repair cost was US$ 366,280.…”

Section: Introductionmentioning

confidence: 99%

“…1 Auxillary Power Unit has not received sufficient attention in maintenance due to its relatively low cost and safety requirements compared to main engines. 3 Unlike the main engines, APUs are designed to operate at lower altitudes as per the operational requirements. Therefore, the failure modes of the APU and the monitoring parameters when it fails are not exactly the same as those of main engines.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Liu et al 2 proposed a performance degradation prediction method for APU based on the improved Support Vector Regression (SVR). Guo et al 3 proposed a remaining useful life prediction method based on particle filter. In order to set up numeric models and study cycle modifications, validation data with well-defined boundary conditions are needed.…”

Section: Introductionmentioning

confidence: 99%

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A fault detection method for Auxiliary Power Unit based on monitoring parameters selection

Jiang

Cai

Zhong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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Auxiliary Power Unit (APU) is an indispensable component utilized in modern aircraft, which provides electrical and pneumatic power to the aircraft independently. What’s more, APU can help the main engines restart in case of main engine failure during flight. Thus there exists the need of APU monitoring. However, APU has not received sufficient attention in maintenance due to its relatively low cost and safety requirements compared to main engines. Line maintenance shows that APU is likely to fail in the start stage. Based on the Quick Access Recorder data supported by the airline, a fault detection method for APU is proposed in this paper. In order to improve the accuracy of fault detection, feature selection is carried out firstly to determine the optimal subset of monitoring parameters by Recursive Feature Elimination. Then fault detection is carried out by Support Vector Machine. Results show that Exhaust Gas Temperature is the most effective index among all monitoring parameters, and feature selection has a significant improvement on the accuracy of fault detection, which means higher accuracy with less monitoring cost. The method proposed in this paper also disposes that some monitoring parameters should be paid more attention in terms of the condition monitoring of APU.

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Foreign object monitoring method in aero-engines based on electrostatic sensor

Guo

Zuo

Zhong

et al. 2022

Aerospace Science and Technology

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Remaining useful life prediction for auxiliary power unit based on particle filter

Cited by 5 publications

References 23 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

A fault detection method for Auxiliary Power Unit based on monitoring parameters selection

Foreign object monitoring method in aero-engines based on electrostatic sensor

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