Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

Swarm systems have received increasing research attention in recent years and they can be found in many application areas as diverse as unmanned vehicle formation systems, multi-robot systems, sensor networks, etc. Reliability and safety are important issues in swarm systems, which can be improved by the fault diagnosis technology. In this paper, recent development of fault diagnosis algorithms for swarm systems is reviewed. The description of swarm systems is given, followed by characteristics of faults in swarm systems. Specially, faults in swarm systems are classified into topology faults and component faults. Then fault diagnosis algorithms for swarm systems are classified according to their architectures, fault types and approaches, respectively. Finally, several unsolved problems of fault diagnosis algorithms for swarm systems are highlighted.

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2013.873745

A survey of fault diagnosis for swarm systems

Review on Fault Diagnosis Techniques for Closed-loop Systems

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks

A novel classiflcation framework is proposed, which divides fault diagnosis approaches into two classes: qual- itative analysis approaches and quantitative analysis approaches The basic idea, main research progresses, and typical applications of each method are discussed in detail, with emphasis on the data-driven approaches The state-of-the-art of fault prediction is also outlined Finally, some problems and development trends of the research on fault diagnosis are pointed out

Fault Diagnosis Techniques for Dynamic Systems

Time-varying bias estimation problem was studied for multi-target tracking systems with asyn- chronous sensors without knowing bias dynamics. We con- sidered general situations, where the number of sensors was arbitrary as well as their sampling rates and initial sam- pling instants. A two-layer fusion structure was adopted. For each target, a pseudo-measurement of sensor bias was generated by fusing sensor measurements of this target. To make the pseudo-measurement decoupled from the tar- get state, the fusion coefficient matrix was determined to be a basis for the left null space of an augmented observa- tion matrix. The bias estimation algorithm was proposed based on the Strong tracking filter (STF) by fusing pseudo- measurements. The proposed algorithm makes use of all available sensor information, has strong tracking ability to abrupt changes, and avoids the matrix inversion for large dimensional matrices. Finally, the performance of the pro- posed algorithm is illustrated by the numerical simulation.

HU Yan-Yan

Papers

Fault Diagnosis Techniques for Dynamic Systems

Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF ∗