Article
Engineering, Multidisciplinary
Yang Xu, Zhixiong Li, Shuqing Wang, Weihua Li, Thompson Sarkodie-Gyan, Shizhe Feng
Summary: This study proposes a hybrid deep learning model based on CNN and gcForest to improve the detection accuracy of bearing faults. By converting vibration signals into time-frequency images using continuous wavelet transform and extracting fault features from them, the method achieves higher performance compared to conventional CNN and gcForest models.
Article
Automation & Control Systems
Georgios Koutroulis, Belgin Mutlu, Roman Kern
Summary: In prognostics and health management (PHM), constructing comprehensive health indicators (HI) from condition monitoring data is crucial for accurate remaining useful life (RUL) prediction and system degradation assessment. Existing methods often oversimplify the degradation law of machinery, which may not reflect the reality. To address these challenges, this research proposes an anticausal-based framework that predicts the cause from the effects of causal models, reducing model complexity. The framework outperforms existing deep learning architectures in predicting HIs, reducing the average root-mean-square error (RMSE) by nearly 65%.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Review
Mathematics
Prashant Kumar, Salman Khalid, Heung Soo Kim
Summary: The availability of computational power in Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots, which consist of rotating machinery, require PHM strategies to minimize downtime. Deep learning has shown its effectiveness in various fields and is rapidly growing in PHM. This paper provides a review of PHM strategies with DL algorithms for industrial robots and their rotating machinery, discussing advancements and challenges associated with current approaches.
Article
Computer Science, Theory & Methods
Abdus Salam, Rolf Schwitter, Mehmet A. Orgun
Summary: This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains, highlighting their usefulness and human-friendly output.
ACM COMPUTING SURVEYS
(2021)
Review
Automation & Control Systems
Samir Khan, Seiji Tsutsumi, Takehisa Yairi, Shinichi Nakasuka
Summary: Prognostic and systems Health Management (PHM) is crucial for solving reliability issues due to system complexities. The rapid advancement of AI technologies has allowed data-driven methods to be applied in constructing process models, but challenges arise in testing and evaluating model errors, unknown phenomena, and robustness in safety-critical applications amidst increasing system complexity and connectivity.
ANNUAL REVIEWS IN CONTROL
(2021)
Article
Physics, Multidisciplinary
Magnus Gribbestad, Muhammad Umair Hassan, Ibrahim A. Hameed, Kelvin Sundli
Summary: Anomaly detection aims to identify data points, events, or behavior that deviate from expected or normal patterns. Challenges include limited labeled data and run-to-failure examples, leading to difficulties in developing reliable fault detection systems. Various reconstruction-based deep learning methods are explored and proposed to enhance transparency and explainability in anomaly detection.
Article
Engineering, Aerospace
Antonio Carlo Bertolino, Andrea De Martin, Giovanni Jacazio, Massimo Sorli
Summary: The evolution towards more electric aircraft is driven by environmental concerns and new market segments. The adoption of electromechanical actuators (EMAs) in flight control systems is limited due to the risk of mechanical jams. Developing a reliable PHM system for EMAs can mitigate this risk and enhance their acceptance in commercial aircraft.
Article
Computer Science, Artificial Intelligence
Victor Verreet, Luc De Raedt, Jessa Bekker
Summary: The paper investigates how to leverage the strengths of probabilistic logic programming (PLP) to formulate and integrate more realistic assumptions for learning better classifiers. It proposes a PLP-based general method, called PU ProbLog, that allows for partial modeling of the labeling mechanism and supports PU learning in relational domains. The empirical analysis demonstrates that partially modeling the labeling bias improves the performance of the learned classifiers.
Article
Computer Science, Artificial Intelligence
Zheng Chai, Chunhui Zhao, Biao Huang, Hongtian Chen
Summary: This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression framework, which effectively transfers knowledge from a related source to enhance soft sensing performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Kihoon Lee, Soonyoung Han, Van Huan Pham, Seungyon Cho, Hae-Jin Choi, Jiwoong Lee, Inwoong Noh, Sang Won Lee
Summary: Transfer learning can improve the diagnostic performance of the target domain when dealing with large domain discrepancies, but may lead to negative transfer effects in cases of significant discrepancies. A multi-objective instance weighting-based transfer learning network has been proposed and successfully applied to fault diagnosis, which adjusts the influence of domain data on model training and maximizes the performance of transfer learning.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Physical
Sangwook Kim, Zonggen Yi, Bor-Rong Chen, Tanvir R. Tanim, Eric J. Dufek
Summary: This study presents a synthetic-data-based deep learning modeling framework for rapid classification and quantification of battery aging modes, with experimental validation of the technique.
ENERGY STORAGE MATERIALS
(2022)
Article
Engineering, Mechanical
Soheil Sadeghi Eshkevari, Liam Cronin, Soheila Sadeghi Eshkevari, Shamim N. Pakzad
Summary: The study introduces a machine learning approach for input estimation in nonlinear dynamic systems, showing promise in various applications. Data-driven methods have the potential to capture hidden and subtle nonlinearities in different domains. Experimental results confirm the efficacy of input estimations in real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Ziyu Gan, Wei Sun, Kaimin Liao, Xuan Yang
Summary: This article proposes a probabilistic framework using compact support radial basis functions (CSRBFs) to estimate cardiac motion. The framework incorporates variational inference-based generative models and convolutional neural networks (CNNs) to learn the probabilistic coefficients of CSRBFs used in image deformation. Experimental results demonstrate that the proposed framework outperforms state-of-the-art registration methods in terms of deformation smoothness and registration accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Anass Akrim, Christian Gogu, Rob Vingerhoeds, Michel Salaun
Summary: With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. This paper investigates the application of Self-Supervised Learning to overcome the lack of labelled data for DL techniques in RUL estimation. Results show that self-supervised pre-trained models significantly outperform non-pre-trained models in RUL prediction tasks with limited labelled data.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Industrial
Juseong Lee, Mihaela Mitici
Summary: This study proposes a framework that integrates data-driven probabilistic Remaining-Useful-Life (RUL) prognostics with predictive maintenance planning, using aircraft turbofan engines as an example. By employing this framework, the total maintenance cost can be reduced, unscheduled maintenance can be prevented, and the wasted life of engines can be limited.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Public, Environmental & Occupational Health
Andres Ruiz-Tagle, Enrique Lopez Droguett, Katrina M. Groth
Summary: Bayesian networks have been widely used in engineering risk assessment for causal reasoning and intervention reasoning, helping identify risk-contributing factors and enabling scenario analysis. Expanding the scope of BN models in risk assessment can provide more robust decision support.
Article
Engineering, Multidisciplinary
Stephen Thomas, Katrina M. Groth
Summary: This article analyzes the limitations of current safety standards and methodologies in the AV industry and proposes a new AV safety framework based on Hybrid Causal Logic. The framework combines Event Sequence Diagrams, Fault Tree Analysis, and Bayesian Networks, providing an integrated approach that has the potential to more completely satisfy fundamental requirements.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2023)
Article
Engineering, Industrial
Roohollah Heidary, Katrina M. Groth
Summary: This paper presents a novel algorithm for developing a population-based corrosion degradation model for oil and gas pipelines. It eliminates the need for defect-matching procedures for non-critical pits and uses a hierarchical Bayesian model to combine uncertain in-line inspection data and physics of failure knowledge. The algorithm successfully predicts pipeline degradation levels with high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Multidisciplinary
Ramin Moradi, Andres Ruiz-Tagle Palazuelos, Enrique Lopez Droguett, Katrina M. Groth
Summary: A mathematical architecture is developed for system-level condition monitoring using fault trees and deep learning. The architecture computes the operation health states of the system and its components based on streaming monitoring data. The applicability of this architecture is demonstrated on a real-world mining stone crusher system and it can be extended to dynamic risk assessment of complex engineering systems. However, caution should be taken when using deep learning models for safety-critical applications.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2023)
Review
Chemistry, Physical
Madison West, Ahmad Al-Douri, Kevin Hartmann, William Buttner, Katrina M. Groth
Summary: This article discusses the importance of quantitative risk assessment in the hydrogen energy sector and highlights the current limitations in hydrogen QRA due to the lack of reliability data. The article critically evaluates and analyzes four tools for collecting hydrogen system safety data and provides recommendations for improvement, serving as a reference for adequate reliability data collection for hydrogen systems.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Engineering, Industrial
Ramin Moradi, Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres, Katrina M. Groth
Summary: This paper presents a novel mathematical architecture for risk and reliability analysis of complex engineering systems, addressing both the complexity of operational data and system complexity using Bayesian networks and Bayesian deep learning models, providing system-level insights.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Vincent P. Paglioni, Katrina M. Groth
Summary: This paper explores the concept of dependency in human reliability analysis (HRA) and proposes a standardized library of key terms and mathematics to lay the foundation for the development of a dependency framework.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Austin D. Lewis, Katrina M. Groth
Summary: This paper introduces a new taxonomy of metrics to assess and compare the performance of system-level health monitoring models. It also describes a verification process and provides an illustrative example for applying these metrics in model design decision. The comprehensive set of metrics enables both PRA and PHM communities to rigorously evaluate system-level health monitoring models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Andres Ruiz-Tagle, Austin D. Lewis, Colin A. Schell, Ernest Lever, Katrina M. Groth
Summary: This paper presents the development and results of BaNTERA, a probabilistic Bayesian network model for third-party excavation risk assessment in the U.S. The capabilities of BaNTERA for risk-informed decision support are demonstrated through model performance verification, damage rate prediction validation, and application in multiple case study scenarios.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Chemical
Ahmad Al-Douri, Mahmoud M. El-Halwagi, Katrina M. Groth
Summary: Unplanned shutdown events in chemical process plants lead to revenue losses, repair costs, and environmental and safety impacts. This study analyzes the causes of these events and models the failure probability of process plants, providing recommendations for reducing the occurrence and mitigating the consequences of shutdown events.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Chemistry, Physical
Ahmad Al-Douri, Andres Ruiz-Tagle, Katrina M. Groth
Summary: In this study, a quantitative risk assessment (QRA) was conducted to analyze the potential risks and reliability of hydrogen fuel cell forklifts. The results showed that the fatal accident rate of hydrogen forklifts is significantly higher than current rates for industrial truck operators. However, the individual risk for forklift drivers was found to be tolerable. Suggestions for improving design and reliability were also provided to mitigate these risks.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Nuclear Science & Technology
Vincent Philip Paglioni, Katrina M. Groth
Summary: This paper reviews the current conceptualization of dependency and demonstrates that current research is not addressing all the technical gaps in HRA. To address the technical gaps in HRA dependency, a set of fundamental dependency structures are proposed, which provide a robust logical structure emphasizing causality.
NUCLEAR SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Industrial
Ahmad Al-Douri, Camille S. Levine, Katrina M. Groth
Summary: In recent years, advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been made, driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain, particularly in the area of risk-significant human actions and human-plant interactions. This study evaluates the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions and identifies sources of uncertainty to make this method suitable for XHPRA.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Proceedings Paper
Engineering, Multidisciplinary
Austin D. Lewis, Katrina M. Groth
Summary: Prognostic and health management (PHM) techniques are being developed to support health assessments of increasingly diverse and complex systems. Causal models like Dynamic Bayesian Networks (DBNs) visualize dependencies between system components and update system health assessments. A new approach is proposed to convert continuous operational data feeds into discrete intervals for building DBNs in PHM.
67TH ANNUAL RELIABILITY & MAINTAINABILITY SYMPOSIUM (RAMS 2021)
(2021)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)