A probabilistic estimation approach for the failure forecast method using Bayesian inference

O’Dowd, Niall M.; Madarshahian, Ramin; Leung, Michael Siu Hey; Corcoran, Joseph; Todd, Michael D.

doi:10.1016/j.ijfatigue.2020.105943

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The posterior predictive distribution measures the possible unobserved values distribution on observed values conditional in Bayesian statistics [44][45][46][47][48].…”

Section: Bayesian Posterior Predictive Distributionmentioning

confidence: 99%

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Alkahtani

2022

Sustainability

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Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was proposed to increase the efficiency of the SCM analysis. The key objective of an efficient supply chain is to find the best flow patterns for the best products in order to select the suppliers to different customers. Hence, the focus of this research is on developing an efficient multi-echelon supply chain using factors such as cost, time, and risk. In the convex case, the proposed method has the advantage of a weakly convergent sequence of iterates to a point in the set of minimizers with probability one. The developed method achieves strong sequence convergence to the unique optimum, with probability one. The SCM dataset was utilized to assess the proposed method’s performance. The proposed PSG method has the advantage of considering the holding cost in the profit analysis of the company. The results of the developed PSG method are analyzed according to the product’s profit, stock, and demand. The proposed PSG method also provides the prediction of demand to increase profit.

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“…The posterior predictive distribution measures the possible unobserved values distribution on observed values conditional in Bayesian statistics [44][45][46][47][48].…”

Section: Bayesian Posterior Predictive Distributionmentioning

confidence: 99%

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Alkahtani

2022

Sustainability

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A Physics-Constrained Bayesian neural network for battery remaining useful life prediction

Najera-Flores

Chadha

et al. 2023

Applied Mathematical Modelling

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Machine Learning and Anomaly Detection Algorithms for Damage Characterization From Compliance Data in Three-Point Bending Fatigue

Kalia

Zeitler

Mohan

et al. 2021

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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Three-point bending fatigue compliance datasets of multi-layer fiberglass-weave/epoxy test specimens, including five and ten mil interlayers, were analyzed using Artificial Intelligence (AI) methods along with statistical analysis, revealing the existence of three different compliance-based damage modes. Anomaly detection algorithms helped discover damage indicators observable in short intervals (of 50 cycles) in the compliance data, whose patterns vary with the material and the number of load cycles to which the material is subjected. Machine learning algorithms were applied using the compliance features to assess the likelihood that material failure may occur within a certain number of future loading cycles. High accuracy, precision, and recall rates were achieved in the classification task, for which we evaluated several algorithms, including various variations of neural networks and support vector machines. Thus our work demonstrates the utility of AI algorithms for discovering a diversity of damage mechanisms and failures.

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A probabilistic estimation approach for the failure forecast method using Bayesian inference

Cited by 3 publications

References 32 publications

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

A Physics-Constrained Bayesian neural network for battery remaining useful life prediction

Machine Learning and Anomaly Detection Algorithms for Damage Characterization From Compliance Data in Three-Point Bending Fatigue

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