http://yetl.yabesh.ir/yetl1/handle/yetl/19041 Thu, 30 Apr 2026 10:07:15 GMT 2026-04-30T10:07:15Z http://localhost:80/yetl1/bitstream/id/184261/ http://yetl.yabesh.ir/yetl1/handle/yetl/19041 http://yetl.yabesh.ir/yetl1/handle/yetl/4310513 Uncertainty Quantification in Fault and Degradation Analysis of Rolling Element Bearings Ajayi, Omodolapo David; Ekwaro-Osire, Stephen; Belli, Olympio; Gandur, Nazir Laureano; Lopez-Salazar, Camilo Alberto This study presents an innovative framework for deriving degradation models of rolling element bearings through uncertainty quantification. Natural open-source run-to-failure experimental datasets, XJTU-SY and PRONOSTIA, were used in this research to investigate the uncertainty at the incipient fault point (IF) and the end-of-life point (EOL) among identical ball bearings under the same operational conditions. This study answers the research question: Can data-driven analysis based on entropy and uncertainty quantification enhance explainability in degradation model determination? The objectives of this paper are to (1) identify the unknown fault types, (2) quantify the uncertainty of the IFs and EOLs, and (3) determine the degradation model considering uncertainty. Fault diagnosis was achieved using a wavelet entropy-based approach integrated with power spectral analysis and clustering via K-means to identify and classify fault types probabilistically. Sensitivity analysis and feature selection were applied in a recursive method to reduce the dimensionality, enhancing model accuracy to 90%. Fault diagnosis contributes to quantifying the uncertainty of the IF and EOL for similar fault-induced bearings using the maximum entropy (MaxEnt) principle. This translated these critical parameters into deterministic descriptors with probability rather than fixed deterministic values. Due to limited data from both datasets, the study employs MaxEnt again to define probability density functions used to generate the degradation model. The results demonstrated that the probabilistic degradation model effectively captures the inherent variability in degradation processes. This methodology is extendable to other engineering systems, offering a versatile tool for predictive maintenance and remaining useful life estimation. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4310513 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4310511 Influence of Potential Parameters on the Bistable Energy Harvester Under Random Excitation Xu, Haitao; Ma, He; Zhou, Shengxi Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4310511 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4310510 Probabilistic Deep Learning With Bayesian Networks for Predicting Complex Engineering Systems' Remaining Useful Life: A Case Study of Unmanned Surface Vessel Weiner, Matthew J.; Yang, Ruochen; Groth, Katrina; Azarm, Shapour Remaining useful life (RUL) serves as a key indicator of system health, and its accurate and timely prediction supports informed decision-making for efficient operation and maintenance. This is essential for complex engineering systems (CESes) such as unmanned surface vessels (USVs), where the human operators have limited opportunity to intervene during the operation. This paper proposes a framework for predicting the RUL of the CESes. The proposed framework employs a probabilistic deep learning (PDL) approach to predict the component's RUL and an equation node-based Bayesian network (BN) to predict system RUL (SRUL) at any future time-step. The component-level RUL method is validated using the NASA's Commercial Modular Aero-Propulsion System Simulation (c-mapss) dataset, and then the proposed framework is demonstrated with a USV case study. The results are evaluated using a set of quality metrics. By making use of the condition-monitoring sensor data, component reliability data, and models that account for the complex causal relationships between components, the proposed framework can provide near real-time predictions of the RUL with uncertainty of a CES, thus supporting its informed decision-making during the operation. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4310510 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4310508 A Flexible Distribution Based on L-Moments and Its Application in Structural Reliability Tong, Ming-Na; Zhao, Yan-Gang; Lu, Zhao-Hui The distribution information of random variables is essential for reliability engineering analysis. Distribution characteristics are generally described by traditional central moments (C-moments). However, C-moments may not be accurate, especially when the sample size of the data is small. Under these circumstances, linear moments (L-moments) are increasingly used to characterize random variables as they are less influenced by outliers and are more stable than C-moments. In this paper, a cubic normal distribution defined by L-moments is suggested. This distribution is divided into six types under different combinations of third and fourth L-moment ratios. In addition, the applicable range of the proposed distribution is investigated. This distribution is then applied to structural reliability, including statistical data analysis, marginal distribution of non-Gaussian stochastic processes, and reliability index calculation. The cubic normal distribution based on L-moments has a wider application range and in the presence of extreme values, which can fit the histogram better than the one based on C-moments. Several examples are presented to demonstrate the effectiveness of the distribution in reliability engineering practices mentioned previously. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4310508 2025-01-01T00:00:00Z