Article
Computer Science, Artificial Intelligence
Myeongsun Kwak, Jongsoo Lee
Summary: Research is being actively conducted on prognosis and health management (PHM) technology. Current diagnostic technologies mainly focus on anomaly detection and classification for maintenance, so there is a need for quantified diagnostic solutions that allow users to take clear actions in advance. In this paper, a diagnostic design solution methodology is proposed from a system design perspective, considering the degradation and uncertainty of the system through combined data-driven and model-based approaches. The proposed method is validated through a case study using mathematical and Modelica-based physical system models, showing that the system response obtained from the estimated design solution is more likely to belong to the normal class than to the initial design.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Adalberto Polenghi, Irene Roda, Marco Macchi, Alessandro Pozzetti
Summary: In smart factories, the use of ontology-based solutions can support joint maintenance and production management decisions by considering the health state of assets, thereby fulfilling production requirements.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Engineering, Industrial
Michele Compare, Federico Antonello, Luca Pinciroli, Enrico Zio
Summary: This work proposes a general modelling approach to estimate the life cycle cost of a system equipped with PHM capabilities and undergoing a CBM policy. The approach is based on the Markov Chain framework and incorporates transition probabilities linked to PHM performance metrics and a novel metric. The model can guide economic decisions about CBM development regardless of the specific PHM algorithm, as long as its performance metrics can be estimated. The model is validated through a case study on a mechanical component affected by fatigue degradation, considering two different prognostic algorithms: Particle Filtering and a Model-Based approach.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Review
Engineering, Multidisciplinary
Fei Peng, Li Zheng, Yongdong Peng, Congcong Fang, Xianghui Meng
Summary: This paper introduces the potential of digital twin technology in the field of rolling bearings and reviews its development history. Through a literature survey, this paper investigates the core technologies of digital twin construction for rolling bearings and analyzes the challenges that this technology faces in future research.
Article
Engineering, Industrial
Chaoqun Duan, Yifan Li, Huayan Pu, Jun Luo
Summary: This paper proposes an adaptive monitoring scheme for predicting faults of systems with hidden degradation processes. A hidden Markov model is used to describe the degradation process, and an expectation maximization algorithm is applied to estimate the model parameters. An adaptive Bayesian control scheme is also developed to monitor the potential risk of the system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Review
Automation & Control Systems
Prashant Kumar, Izaz Raouf, Heung Soo Kim
Summary: This article provides a comprehensive study of fault prognosis and health management (PHM) strategies in smart factories, including both traditional and deep learning perspectives. The paper also discusses conventional health management methods and the latest trends in the PHM field in smart factories.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Green & Sustainable Science & Technology
Meiling Yue, Samir Jemei, Noureddine Zerhouni, Rafael Gouriveau
Summary: This paper provides a comprehensive review of existing prognostics research on proton exchange membrane fuel cells (PEMFC), highlighting key issues that have not been fully addressed and proposing four principal directions of post-prognostics decision-making. Research challenges and development perspectives in the aspects of data, prognostics, and decision-making are discussed based on the findings.
Article
Automation & Control Systems
Chaoqun Duan, Peiwen Chen
Summary: This article introduces a new degradation-integrated failure model for deteriorating systems subject to dynamic conditions and random failures. An adaptive maintenance scheme is proposed based on this model, which can monitor the system conditions and adjust the monitoring frequency according to the hazard level. The monitoring frequencies are optimized using a computational algorithm formulated in a semi-Markov decision process framework. The particularity of this work is the consideration of a changeable monitoring scheme under dynamic conditions, effectively reducing false alarms caused by environmental shocks. The proposed approach is demonstrated through a case study of power devices and comparisons with other advanced approaches are presented.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
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
Computer Science, Information Systems
Chaoqun Duan, Yifan Li, Dongdong Kong, Huayan Pu, Jun Luo
Summary: This paper proposes a bi-level Bayesian control scheme for detecting faults of complex devices under partial observations. The scheme models the deterioration of an operational system as a hidden three-state multivariate Markov process and uses two levels of sampling frequency to dynamically detect impending failures. The decision variables of the bi-level fault detection scheme are optimized and solved in a semi-Markov decision process (SMDP) framework.
INFORMATION SCIENCES
(2022)
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)
Article
Green & Sustainable Science & Technology
M. Hung Do, Dirk Soffker
Summary: The main development in wind energy technology is the growth of wind turbine size driven by economic factors. The bigger size helps to increase power output and energy efficiency, but also presents challenges in operation and maintenance.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
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
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
Engineering, Electrical & Electronic
Jingjing Zhong, Dong Wang, Ju'e Guo, Diego Cabrera, Chuan Li
Summary: The article explores the use of kurtosis and negative entropy as indices for characterizing impulsive transients in System health monitoring. It proposes a new weighted residual regression-based index to provide more reliable trend assessment. Theoretical and experimental results show that this method has better fault detection and degradation assessment capabilities compared to traditional kurtosis and negative entropy indices.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(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)