A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data

Wang, Bowen; Sun, Junjie; Chen, Dan; Ning, Shungang

doi:10.1002/qre.3299

Cited by 2 publications

(1 citation statement)

References 29 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qi and Huang 10 proposed a reliability model for systems with dependent competing failure processes and cumulative random shocks, and a conditionbased maintenance policy based on their proposed reliability model. Wang et al 11 introduced a cumulative exposure model for predicting remaining useful lifetime (RUL) of pitot tube based on dynamic operating data. Wei and Liu 12 assessed reliability of series and parallel systems with heterogeneous components under cumulative shocks.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

Modeling and evaluation of a system reliability is a key issue in shock model analysis. In cumulative shock model, a system fails when the total damages caused by shocks exceeds the system failure threshold level. The reliability of a system with random failure threshold level under cumulative shocks is investigated in this work, considering the general dependency between inter‐arrival times of shocks and their damages magnitudes. Clustering, as one of the unsupervised machine learning methods, is utilized here by applying a genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for its implementation. Phase‐type (PH) distributions and their properties are also applied for finding a system failure distribution functions, determining the convolutions, evaluating the integrals, and ultimately modeling the system reliability. An illustrative example is provided to demonstrate the implementation and effectiveness of the proposed modeling procedure and the suggested approach for computing the system reliability and remaining useful lifetime (RUL). Also, an example case study of X‐ray machine in healthcare industry is provided to demonstrate the application of the proposed model. Applying other machine learning methods for patterns recognition is an area for future research.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Safaei,

Taghipour

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations.

show abstract

A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data

Cited by 2 publications

References 29 publications

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Contact Info

Product

Resources

About