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

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

Research motivation and current trends on data-driven fault diagnosis are firstly introduced. Then new classification of existing fault diagnosis approaches, which are data-driven methods, analytical model based methods and qualitative knowledge based methods, is proposed to show the status of data-driven methods in the framework. The basic application background is further discussed in terms of data requirements and comparison with analytical model based methods. The state-of-art methods as well as their connections and differences are analyzed in detail. Finally, several potential research directions are also summarized.

Survey on data driven fault diagnosis methods

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

Induction motor stator fault detection via fuzzy logic

Rolling bearing is one of the most critical components in rotating machinery, so in order to efficiently select features, reduce feature dimensions and improve the correctness of fault diagnosis, a feature selection and fusion method based on weighted multi-dimensional feature fusion is proposed. Firstly, features are extracted from different domains to constitute the original high-dimensional feature set. Considering the large number of invalid and redundant features contained in such original feature set, a feature selection process that combines with support vector machine (SVM) single feature evaluation, correlation analysis and principal component analysis-weighted load evaluation (PCA-WLE) is put forward in this paper for selecting sensitive features. The selected features are weighted and fused according to their sensitivity so as to further weaken the interference of low important features. Finally, this process is applied to the data provided by the Case Western Reserve University Bearing Data Center and Xi'an Jiaotong University School of Mechanical Engineering, respectively, and the fault is diagnosed by using the particle swarm optimization-support vector machine (PSO-SVM). The results show that this method can accurately identify different fault categories and degrees of bearing, which is superior and practical than single-domain fault diagnosis with higher recognition ability.

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Bearing Fault Feature Selection Method Based on Weighted Multidimensional Feature Fusion

In this paper first a parametric low differential order model, suitable for mathematical analysis is introduced for induction machines with faulty stator. Second, an adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous-time model with discrete measurements for fault detection ends. Typical motor faults as inter-turn short circuit and increased winding resistance are taken into account.

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Stator fault detection in induction machines by parameter estimation, using adaptive kalman filter

Recent improvements in technologies highlight the importance of life-cycle management in the supply chain. This is more important when dealing with non-instantaneous deteriorating items due to the fact that these goods begin losing their marginal values once they are stored as inventories in the warehouses. In this regard, an accurate inventory policy should be taken into account for this type of deterioration. Considering different aspects of the supply chain, many assumptions have been made in the literature for inventory policies of non-instantaneous deteriorating items. Many of these simplifying assumptions are used in order to avoid the complexity of formulations and mathematical modelling. However, in this paper, a thorough and comprehensive model based on the Stackelberg game is proposed in order to consider a two-echelon supply chain with uncertain demand that is dependent upon price and time. Furthermore, a price-discount strategy is applied within the deterioration period as a persuasion strategy to increase customer demand and reduce deterioration costs. The different relationships between inventory parameters and variables are investigated by a precise sensitivity analysis, and the robustness of the presented results is confirmed. The results suggest the appropriate time for the beginning of a discount strategy and the amount of discount. 

Pricing and inventory policy for non-instantaneous deteriorating items in vendor-managed inventory systems: a Stackelberg game theory approach

In recent decades, manufacturers’ intense competitiveness to suit consumer expectations has compelled them to abandon the conventional workflow in favour of a more flexible one. This new trend increased the importance of master production schedule and make-to-order (MTO) strategy concepts. The former improves overall planning and controls complexity. The latter enables the production businesses to reinforce their flexibility and produce customized products. In a production setting, fluctuating resource capacity restricts production line performance, and ignoring this fact renders planning inapplicable. The current research work addresses the MPS problem in the context of the MTO production environment. The objective is to resolve Rough-Cut Capacity Planning by considering resource capacity fluctuation to schedule the customer’s order with the minimum cost imposed by the company and customer side. Consequently, this study is an initial attempt to propose a mathematical programming approach, which provides the optimum result for small and medium-size problems. Regarding the combinatorial intrinsic of this kind of problem, the mathematical programming approach can no longer reach the optimum solution for a large-scale problem. To overcome this, an innovative agent-based heuristic has been proposed. Computational experiments on variously sized problems confirm the efficiency of the agent-based approach.

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F. Bagheri

Papers

Stator fault detection in induction machines by parameter estimation, using adaptive kalman filter

Pricing and inventory policy for non-instantaneous deteriorating items in vendor-managed inventory systems: a Stackelberg game theory approach

An Agent-Based Approach for Make-To-Order Master Production Scheduling