Yan-Fu Li

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• PhD National University of Singapore
• Professor (Full) at Tsinghua University

This study presents an effective solution for detecting faults early on in smart meters. The proposed method involves using a variational auto-encoder to evaluate the operational conditions of the meters by creating a variational feature state space matrix from the power consumption data. The use of a variational auto-encoder automates the generati...

Industrial visual monitoring (IVM) is crucial in enhancing the reliability and efficiency of manufacturing processes. Recently, large vision-language models (LVLMs) have demonstrated remarkable semantic understanding and natural language interaction capabilities, which provide a novel solution to IVM. However, LVLMs pretrained on common domains lac...

The challenges of class-imbalance and partially unknown training labels often arise in fault detection tasks. When these two problems occur simultaneously, existing imbalanced classification methods cannot be directly used due to the absence of the label, and the class-imbalance would lead to severe bias prediction. In this study, we proposed a nov...

The wheel wear status of high-speed trains (HSTs) is an essential indicator of their safety and reliability. However, due to the time-varying operating state of HSTs, noisy and complex non-stationary signals are collected. This makes it difficult for data-driven algorithms to learn valuable discriminative features from data. Therefore, this inspire...

Machine intelligence fault prediction (MIFP) is crucial for ensuring complex systems’ safe and reliable operation. While deep learning has become the mainstream tool for MIFP due to its excellent learning abilities, its interpretability is limited, and it struggles to learn frequencies, making it challenging to understand the physical knowledge of...

Prognostics and health management (PHM) technology, by monitoring the faults and degradation of railway systems, predicting the remaining useful life of equipment, and providing maintenance recommendations, can effectively improve the safety and reliability of railway systems. In recent years, large-scale language models (LLMs) like ChatGPT have ac...

Although the envelope-spectrum-based methods for bearing fault diagnosis have been widespread in the scientific community, their application to autonomous diagnosis is hindered by the specified selection of informative frequency bands and the threshold calculation. This paper therefore proposes a novel autonomous diagnosis method via Fault-Induced...

Industrial visual monitoring (IVM) is crucial for operation and maintenance, and artificial intelligence (AI) has excelled in this domain. As a revolutionary breakthrough in AI, large models are set to revolutionize IVM by advancing comprehensive automation and intelligence. This paper proposes an intelligent IVM and maintenance framework (IVMMF) e...

With the increasing demand for high-quality telecommunication services, cellular KPI prediction becomes crucial for telecommunication network monitoring and management. In this work, we propose a novel framework for cellular KPI prediction, which considers its distribution discrepancy under different network operation scenarios. In particular, thre...

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies heavily on a reliable health indicator (HI). Conventional indicators are often constructed by directly concatenating features from multiple sources. It overlooks significant non-linear and correlative information inherent in raw signals. To address this limit...

Lithium-ion batteries (LIBs) are prevalent energy storage devices in industrial fields and modern life, but are subjected to capacity degradation during operation due to the varying internal states. To ensure the efficiency, safety and reliability of LIBs, LIBs diagnostics by analyzing the internal states and estimating the capacity is crucial. How...

Similarity based methods predict remaining useful life (RUL) by directly measuring the similarities between degradation trajectories, which can be applied when the exact time of equipment operation beginning is unknown and the degradation models cannot be established. However, the similarity measures are usually determined based on experience and e...

High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a subs...

Prognostics and health management (PHM) technology plays a critical role in industrial production and equipment maintenance by identifying and predicting possible equipment failures and damages, thereby allowing necessary maintenance measures to be taken to enhance equipment service life and reliability while reducing production costs and downtime....

Automated Driving Systems ( ADS ) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS...

Lithium-ion (Li-ion) batteries have gained widespread usage in numerous consumer electronic products and have significantly contributed to the growth of related industries. Due to the instability issues which might cause explosion or fire, it is critical to ensure the safety and reliability of Li-ion batteries via health monitoring. While artificia...

We propose a novel method for weakly-supervised anomaly detection, where a limited number of labeled normal samples and a sufficient number of unlabeled samples are available for modeling. In particular, we seamlessly integrate label propagation with manifold graph learning into a support vector data description model. Consequently, the estimated m...

High-level maintenance is one of the key practices to ensure the safe operation of high-speed trains (HSTs). However, it is usually scheduled manually, and there has been a lack of systematic study on how to optimize maintenance scheduling considering a dynamic time interval. This article discusses the state-of-the-art high-level maintenance schedu...

The wheels are among the most critical components which largely influence the safe operation of high-speed trains. The existing research in reliability modeling typically assumes the wheel degradation to be a one-dimensional degradation process. This could incur a deficiency in practice, as the wheel degradation is in fact the superposition of mult...

Government bodies, companies, and social agencies typically act as defenders and apply the game theory method to protect their assets targeted by malicious attackers. However, for developed transregional or even transnational systems, the upper-level administrator fails to coordinate the lower-level agents (sub-system defenders) due to the organiza...

Pre-disaster planning and post-disaster scheduling are common strategies to increase the resilience of critical infrastructure. In this paper, we propose a new two-stage stochastic optimization model to simultaneously determine the locations for building stations of restoration teams before disasters and their routing of conducting restoration task...

In this paper, we consider a redundancy allocation problem in a series‐parallel system with uncertain component lifetimes of multiple states, which involves multi‐type components and mixed redundancy strategy. We develop a distributionally robust redundancy allocation model using state‐dependent ambiguity set which describes the mean, expected cros...

Recent intelligent fault diagnosis technologies can effectively identify the machinery health condition, while they are learnt based on a closed-world assumption, i.e., the training and testing data follow independently identically distribution (IID). However, in real-world diagnosis, the monitored samples are often from unknown distributions, such...

The papers in this special section focus on increasing interests in the development and implementation of advanced artificial intelligence (AI) and machine learning (ML) methods for tackling the reliability and system health prognostics challenges in various industrial applications.

We find the loss of demands caused by a severe fault or even failure of cells in the urban telecommunication network could be compensated by two types of resilience, namely intra-sector resilience and inter-sector resilience. Intra-sector resilience is stemmed from the multi-cell design of base stations. Inter-sector resilience manifests as the par...

Lithium-ion batteries have been widely utilized in increasing number of industrial and household domains. The accuracy of the terminal voltage estimation in the discharge processes of lithium-ion batteries is crucial to ensure the availability and safety of battery-powered facilities. In prior studies, the priority of influencing factors of dischar...

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning and control, which brings together the latest advances in multiple domains. Despite these achievements, safety assurance of the sys...

Prognostics and health management (PHM) is developed to guarantee the safety and reliability of Lithium-ion (Li-ion) battery during operations. Due to the advantages of deep learning on nonlinear modeling and representation learning, it gains considerable attentions in the PHM of Li-ion battery. To provide a comprehensive view of deep learning-base...

Reliability-redundancy allocation problem (RRAP), a challenging reliability optimization problem, aims to optimize the redundancy level and the component reliability simultaneously for each stage (i.e. subsystem) of the system. The RRAP was mainly studied under binary-state setting on different redundancy strategies, methodologies, and multi-object...

The rapid development of high-speed trains has brought a significant demand to increase the reliability and optimize the maintenance of train wheels. As the state-of-the-art practice in high-speed trains, the maximal radial run-out and equivalent conicity are two leading health indicators (HIs) to assess the health status of the wheels. However, th...

Context Project planning is a crucial part of software engineering, it involves selecting requirements to develop for the next release. How to make a good release plan is an optimization problem to maximize the goal of revenue under the condition of cost, time, or other aspects, namely Next Release Problem (NRP). Genetic and exact algorithms are us...

Redundancy allocation problems (RAPs) is a classical family of reliability optimization problems. RAP for multi-state systems (MSSs) is among the most difficult RAPs. Multi-state series-parallel system (MSSPS) is among the most commonly-applied structure of MSSs. To the knowledge of the authors, in literature there is no exact approach to solve MSS...

We propose a probabilistic model for clustering spatially correlated functional data with multiple scalar covariates. The motivating application is to partition the 29 provinces of the Chinese mainland into a few groups characterized by the epidemic severity of COVID-19, while the spatial dependence and effects of risk factors are considered. It ca...

The recent social trends and accelerated technological progress culminated in the development of autonomous vehicles (AVs). Reliability assessment for AV systems is in high demand before its market launch. In safety-critical systems (SCSs) such as AV systems, the reliability concept should be broadened to consider more safety-related issues. In thi...

With the lack of failure data, class imbalance has become a common challenge in the fault diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced problem by generating the minority samples to balance the training set. However, one of the main challenges of the existing oversampling methods is how to generate high-q...

The wheel wear status of high-speed trains (HSTs) is an essential indicator for their safety and reliability. When the wheel wear exceeds the warning value without timely maintenance, it will seriously affect the dynamic performance of the HST and even cause a derailment accident. With HSTs and sensor technology development, massive operation data...

Deep neural network (DNN) is an effective technology for machinery fault diagnosis. The good performance of DNN is based on the assumption that all labels are completely correct. However, mislabeled data are common in actual industrial applications, which will cause severe performance degradation. This article explores the performance of DNN under...

Fault diagnosis is an essential means to ensure the regular operation of mechanical systems. The existing data-driven algorithms are developed based on the assumption that the given label is entirely correct. However, mislabeling is common, which often occurs in industrial applications. These methods will overfit these mislabeled samples, resulting...

Opportunistic maintenance (OM), which shows its superiority on complex multi-component systems by integrating the maintenance activities of multiple components to reduce the maintenance cost, has been widely studied over the past decade. To our knowledge, most of the existing OM works are developed based on fixed maintenance thresholds without full...

In practical industrial applications, the need for a large number of accurately labeled training samples is a significant challenge for fault detection tasks. However, labeling all training samples is expensive and prone to labeling errors, especially for early fault detection of wind turbine blades. This paper proposes a labeling bias (LB) hypothe...

Class-imbalance is a prevalent and challenging problem in the field of fault detection. The undersampling ensemble framework is an effective method to deal with imbalance problems. However, designing a suitable sampling strategy to generate effective and divergent subsets is a major difficulty of this type of method. Hence, we propose a novel adapt...

Intelligent fault detection methods based on deep learning have been developed rapidly in recent years. However, most of these methods are based on supervised learning which requires a fully labeled training set. It is difficult to obtain massive labeled samples in real applications incredibly accurately labeled fault samples from an operating syst...

Cyber vulnerabilities become ever more critical in modern industrial systems since the attacker can utilize the vulnerabilities to degrade their performance or even cause disasters. In 2015, a series of sequential and well‐organized cyber attacks intruded into the Ukrainian power grid, compromised access to the control system, and interrupted the p...

In this paper, we consider the redundancy allocation problem (RAP) with uncertainties in component parameters for multi-state series-parallel system (MSSPS) and continuous-state series-parallel system (CSSPS). In real-world cases, the component parameters such as costs and reliabilities are often uncertain due to epistemic uncertainty. The existing...

Power grids deliver energy, and telecommunication networks transmit information. These two facilities are critical to human society. In this study, we conduct a comprehensive overview of the development of reliability metrics for power grids and telecommunication networks. The main purpose of this review is to promote and support the formulation of...

As autonomous vehicle (AV) intelligence for controllability continues to develop, involving increasingly complex and interconnected systems, the maturity level of AV technology increasingly depends on the systems reliability level, also considering the interactions among them. Hazard analysis is typically used to identify potential system risks and...

We propose a game attack–defense graph (GADG) approach that integrates the attack–defense graph and the game theory to model and analyze cyberattacks and defenses in the local metering system (LMS). Different from previous studies concentrating on static analyses of cybervulnerabilities, the GADG method considers correlations among these vulnerabil...

The data-driven methods in machinery fault diagnosis have become increasingly popular in the past two decades. However, the wide applications of this scheme are generally compromised in real-world conditions because of the discrepancy between the training data and testing data. Although the recently emerging transfer fault diagnosis can learn trans...

Internet backbone router is essentially the core router of Internet backbone and its performance is mainly relevant to the reliability of its mainboard. The mainboard is an embedded system consisting of hardware and software. Its reliability testing involves executing a number of test cases, which are designed to expose potential defects, under har...

Process monitoring using profile data remains an important and challenging problem in various manufacturing industries. Motivated by an application case of motherboard testing processes, we develop a novel modeling and monitoring framework for heterogeneous multivariate profiles. In this framework, a heterogeneous graphical model is constructed to...

The structure strength abruptly decreases due to the propagation of multiple cracks. The crack interaction renders the multiple crack reliability problem being a challenging one. Most of the existing methods cannot fit the whole multiple crack growth process and quantify the crack linkup effects on reliability estimation. To address this problem, w...

With the development of sensor and communication technology, condition-based maintenance (CBM) attracts increasing attention, especially for multi-component systems. This paper aims to investigate the optimal CBM policy under periodic inspection for a K-out-of-N: G system, where economic dependency, stochastic dependency and imperfect maintenance a...

Power utility allocates defense resources to prevent unscheduled load shedding due to transmission line failure caused by the malicious physical attacks. Game theory explains the interaction between the defender and the attacker, overcoming the shortage of unilateral vulnerability analysis. Different from previous researches typically assuming the...

With the abundance of industrial datasets, imbalanced classification has become a common problem in several application domains. Oversampling is an effective method to solve imbalanced classification. One of the main challenges of existing oversampling methods is to accurately label the new synthetic samples. Inaccurate labels of synthetic samples...

Polygonal wear is one of the most critical failure modes of high-speed train wheels that would significantly compromise the safety and reliability of high-speed train operation. However, the mechanism underpinning wheel polygon is complex and still not fully understood, which makes it challenging to track its evolution of the polygonal wheel. The l...

Test-suite minimization is one key technique for optimizing the software testing process. Due to the need to balance multiple factors, multi-criteria test-suite minimization (MCTSM) becomes a popular research topic in the recent decade. The MCTSM problem is typically modeled as integer linear programming (ILP) problem and solved with weighted-sum s...