A5052063173- Reliability and Maintenance Optimization
- Software Reliability and Analysis Research
- Risk and Safety Analysis
- Probabilistic and Robust Engineering Design
- Fault Detection and Control Systems
- Machine Fault Diagnosis Techniques
- Statistical Distribution Estimation and Applications
- Non-Destructive Testing Techniques
- Multi-Criteria Decision Making
- Power System Reliability and Maintenance
- Quality Function Deployment in Product Design
- Smart Grid Security and Resilience
- Life Cycle Costing Analysis
- Advanced Memory and Neural Computing
- Advanced Battery Technologies Research
- Infrastructure Resilience and Vulnerability Analysis
- Rough Sets and Fuzzy Logic
- Occupational Health and Safety Research
- Energetic Materials and Combustion
- Mass Spectrometry Techniques and Applications
- Optimal Experimental Design Methods
- Advanced Multi-Objective Optimization Algorithms
- Quality and Safety in Healthcare
- Robotic Mechanisms and Dynamics
- Bayesian Modeling and Causal Inference
University of Electronic Science and Technology of China
2018-2025
University of Science and Technology of China
2018
In this article, we develop a mixture of Gaussians-evidential hidden Markov model (MoG-EHMM) to fuse expert knowledge and condition monitoring information for remaining useful life (RUL) prediction under the belief function theory framework. The evidential expectation-maximization algorithm is implemented in offline phase train MoG-EHMM based on historical data. online phase, trained used recursively update health state reliability particular individual system. predicted RUL is, then,...
Abstract Modern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time‐to‐failure data. For a system, different levels’ reliability‐related from monitoring observations provide additional information to update the system's reliability. However, with limitation of sensing and technologies vague judgments experts, inevitably contain epistemic...
ABSTRACT The rapid growth of the Online‐to‐Offline food delivery industry has increased load on road transportation and posed significant challenges in managing traffic risks. existing risk assessment encounters two primary challenges. First, due to inherent randomness uncertainty emergencies, it is difficult for decision‐makers accurately predict risk. Second, scarcity incompleteness accident‐related data exacerbate process. To address these issues, an uncertainty‐based ensemble learning...
The robotic joint module is a typical mechatronic system that inevitably faces uncertainties from various fields. These significantly influence its positioning accuracy. In this article, we develop accuracy analysis o f under such uncertainties. First, comprehensive electromechanical coupling model constructed by considering the information exchange rules between mechanical and electronic components. Subsequently, performed, taking into account different A variance-based sensitivity method...
Improving reliability and eliminating the potential design risk are crucial for product design. Failure mode effect analysis (FMEA), as an effective assurance tool, has been extensively used in However, causalities among failure modes, interactions factors, correlations evaluations were not jointly considered existing FMEA methods. On other hand, cost time caused by occurrence of modes seldom incorporated into factors. Due to lack accurate values and/or information loss customers experts,...
As multistate system (MSS) reliability models can characterize the deteriorating nature of engineering systems, they have received considerable attention in past decade. The states a system/component be distinguished by its performance capacity, which deteriorates over time and restored maintenance activities. On other hand, deterioration is, oftentimes, controllable setting loading strategy. In this article, dynamic load optimization problem for repairable MSSs is investigated to achieve...
The fault tree analysis has been extensively implemented in failure of engineered systems. In most cases, the probabilities basic events, e.g. components’ failures, are represented by crisp values analyses. However, due to lack knowledge, scarcity data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models components/systems, and such model can be quantified epistemic uncertainty. addition, common cause failure, related simultaneous failures...
It is crucial to evaluate reliability measures of a system over time, so that reliability-related decisions, such as maintenance planning and warranty policy, can be appropriately made for the system. However, accurately assessing becomes challenging if only limited amounts data are available. On other hand, imprecise information related collected based on experts’ judgments/experiences, these pieces may be, however, heterogeneous come from multiple sources. By properly fusing information,...
Reliability assessment of complex engineered systems is challenging as epistemic uncertainty and common cause failure (CCF) are inevitable. The probabilistic (PCCF), which characterizes the simultaneous failures multiple components with distinguished chances, a generaliz ed model traditional CCF model. To accurately assess system reliability, it great significance to take both effects PCCF components' state probabilities into account. In this paper, an evidential network proposed reliability...
Abstract Multi-state is a typical characteristic of engineered systems. Most existing studies redundancy allocation problems (RAPs) for multi-state system (MSS) design assume that the state probabilities redundant components are precisely known. However, due to lack knowledge and/or ambiguous judgements from engineers/experts, epistemic uncertainty associated with component states cannot be completely avoided and it befitting represented as belief quantities. In this paper, multi-objective...