Abstract The increasing prominence of electric transportation underscores the critical importance of battery system reliability and longevity. This paper presents an interpretable online degradation monitoring method for heavy-duty electric vehicle (EV) batteries to identify the pre-failure inspection period. The proposed approach uses online battery sensor data to continuously analyze battery performance to predict the pre-failure interval without relying on traditional statistical assumptions regarding battery degradation. By considering the stochastic nature of EV battery degradation, the methodology seeks to identify subtle patterns indicative of performance deterioration through logical reasoning and pattern recognition. This proactive strategy facilitates timely interventions and preventive maintenance, thereby enhancing overall system reliability and safety. The effectiveness of the approach is validated using a 40-foot Electric Bus, demonstrating its capability in predicting pre-failure conditions. The proposed approach achieves an average accuracy of 98.8% with unseen samples across various testing and training ratios, illustrating the robustness of online monitoring.

Reinforcement learning (RL) usage in the industrial domain is on the rise since the shift towards Industry4.0 systems. The need for faster adaptive systems is encouraging manufacturers to invest more in artificial intelligence (AI) technologies. Our aim is to add better intelligence into simulation tools by embedding RL capabilities. Discrete event simulations have been used for decision support in manufacturing systems for decades. New simulation tools such as AnyLogic have improved significantly in the past few years. In this paper, we built a RL library for AnyLogic simulation models. The RL library is developed for model designers, who may not be experts in the field of RL. We applied our RL library on a real-world use case model for truck dispatching problem. The results show the benefits of using RL in real-world problems to find better dispatching policies. Additionally, the visualization capabilities of AnyLogic enabled us to explain the RL agent's reaction to changes in the system.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

Wheel Motor Electric Bus (W.M.E-Bus) is a recent e-mobility technology, which has a complex system integration. Since the operational reliability and life cycle data of such systems is scarce, it becomes impractical to plan for maintenance and determine system-critical components. Moreover, E-Bus system dismantling and assembling is a long time process especially for components near to the its Power-system. In this paper, we propose a Fuzzy-logic fault-tree evaluation for the W.M.E-Bus system under uncertain failure data. The proposed method indicates the critical components that significantly influence the system's failure uncertainty. At 10% failure rate uncertainty, control unit failure, including the embedded software, is ranked the top critical failure mode with 1.8 Fuzzy Importance Measure (FIM).

Internet of Things (IoT) aims to bring connectivity to almost every objects, i.e., things, found in the physical space. It extends connectivity to everyday things, however, such increase in the connectivity creates many prominent challenges. Context: Generally, IoT opens the door for new applications for machine-to-machine and human-to-human communications. The current trend of collaborating, distributed teams through the Internet, mobile communications, and autonomous entities, e.g., robots, is the first phase of the IoT to develop and deliver diverse services and applications. However, such collaborations is threatened by the fragmentation that we witness in the industry nowadays as it brings difficulty to integrate the diverse technologies of the various objects found in IoT systems. Diverse technologies induce interoperability issues while designing and developing various services and applications, hence, limiting the possibility of reusing the data, more specifically, the software (including frameworks, firmware, applications programming interfaces, and user interfaces) as well as of facing issues, like security threats and bugs, when developing new services or applications. Different aspects of handling data collection ranging from discovering smart sensors for data collection, integrating and applying reasoning on them must be available to provide interoperability and flexibility to the diverse objects interacting in the system. However, such approaches are bound to be challenged in future IoT scenarios as they bring substantial performance impairments in settings with the very large number of collaborating devices and technologies. Objective: We raise the awareness of the community about the lack of interoperability among technologies developed for IoT and challenges that their integration poses. We also provide guidelines for researchers and practitioners interested in connecting IoT networks and devices to develop services and applications. Method: We apply the methods advocated by the evidence-based software engineering paradigm. This paradigm and its core tool, the systematic literature review (SLR), were introduced to the software-engineering research community early 2004 to help researchers and industry systematically and objectively gather and aggregate evidences about different topics. In this paper, we conduct an SLR of both IoT interoperability issues and the state-of-practice of IoT technologies in the industry, highlighting the integration challenges related to the IoT that have significantly shifted the landscape of Internet-based collaborative services and applications nowadays. Results: Our SLR identifies a number of studies from journals, conferences, and workshops with the highest quality in the field. This SLR reports different trends, including frameworks and technologies, for the IoT for better comprehension of the paradigm and discusses the integration and interoperability challenges across the different layers of this technology while shedding light on the current IoT state-of-practice. It also discusses some future research directions for the community.

We propose in this paper a high-temperature non-linear modeling for the I-V characteristics of GaN150 HEMT. Three different data-driven models were developed for a temperature range varying from 25°C to 250°C, by using three machine learning regression techniques namely: The Artificial Neural Network (ANN), the Support Vector Machine (SVM) and the Decision Tree (DT). Experiments were conducted on a GaN150 device with a width of 40 μm and accordingly, a set of measurements were obtained and exploited to build the device model. The three models were evaluated based on their ability to predict the I-V characteristics outside the temperature range (greater than 250°C) and their mean square error. The obtained results show that the models predict the device characteristics correctly based on the calculated mean squared error between the actual and predicted characteristics.

We describe in this paper a model for the I-V characteristics of AlGaN/GaN high electron mobility transistors (HEMTs) working in high-temperature environments up to 250°C. Modeling of this emerging technology is a very significant step toward incorporating the technology in harsh environment applications. An extended version of the Angelov model is modified in this paper to consider the temperature as a variable. The developed model is fitted to the experimental I-V data using MATLAB. The reported experimental data are in good agreement with the model outputs over the specified temperature range. Moreover, the model was validated using the Spectre circuit simulator.

Equipment usually consists of many components arranged in hierarchical structure. In order to achieve efficient maintenance strategy, the system hierarchy should be taken into account. In this paper, we first give a nomenclature to describe a system composed of multiple non-identical components in a hierarchical structure, the system for an age-based and an opportunistic preventive maintenance strategies is modeled by using a Markov Decision Process (MDP). Then, near-optimal policies are found through the SARSA(λ) algorithm from Reinforcement Learning (RL), where the expected discounted cost is minimized. Simulation experiments to compare near-optimal policies obtained by SARSA(λ) are performed for both strategies with corrective maintenance and with age-based preventive maintenance policy obtained from renewal reward theory. We show that the proposed opportunistic preventive maintenance outperforms other strategies.

This paper proposes a methodology for knowledge representation using ontology concepts. We employ an ontology-based schema to overcome the problems of heterogeneity and inconsistency in maintenance records, which are attributable to abbreviations, noisy data, nongeneric data structures, and ambiguous technical words in textual maintenance records. Our methodology employs a bond graph model (BGM) to produce a function structure of equipment related to fault propagation in part-component levels. Our method combines OWL-Lite/RDF and the ISO 14224 and ISO 15926 international standards in order to obtain a generic system-level representation model. Our approach also constructs transparent cause-effect knowledge, which facilitates interpretation and computer conversion using ISO 14224 and ISO 15926. The web ontology language (OWL) and resource description framework (RDF) are used to convert the generic human-readable interpretation into a standard computer-readable representation, thereby generating a knowledge base with maximum shareability and accessibility. We applied the methodology to a typical pneumatic valve. The results show that BGM can cross-link the identified words and the domain-specific logic to obtain the function structure of an object with causality inference, as well as enriching semantic extraction based on the context of a maintenance report, which improves the interpretation and computer conversion. Using OWL/RDF, actions such as interexchange, retrieval, and storage are possible for fault diagnosis applications in a multidisciplinary environment. Our method provides a generic technical understanding, which enriches semantic extraction and knowledge discovery in a typical maintenance report.

In this paper we present a novel approach to the problem of understanding, monitoring, and controlling the machining process of composites materials. The approach is called Logical Analysis of Data (LAD). It is based on data mining and pattern recognition, and uses a machine learning artificial intelligence technique. This novel approach is used for the first time in order to define machining conditions that lead to conforming products, and also conditions which will lead to nonconforming products. In this paper, we introduce the LAD technique, we apply it to the machining of composites, and we report on the results based on data obtained experimentally. We conclude with a discussion of the potential use of LAD in manufacturing.

This article proposes a model to calculate the reliability function, and the mean residual (remaining) life of a piece of equipment, when its degradation state is not directly observable. At each observation moment, an indicator of the underlying unobservable degradation state is observed, and the monitoring information is collected. The observation process is due to a condition monitoring system where the obtained information is not perfect. For that reason, the observation process doesn't directly reveal the exact degradation state. To match an indicator's value to the unobservable degradation state, a stochastic relation between them is given by an observation probability matrix. It is assumed that the equipment's unobservable degradation state transition follows a Markov chain, and we model it using a hidden Markov model. The Bayes' rule is used to determine the probability of being in a certain degradation state at each observation moment. Cox's time-dependent proportional hazards model is considered to model the equipment's failure rate. This paper addresses two main problems: the problem of imperfect observations, and the problem of taking into account the whole history of observations. Two numerical examples are presented.

This article proposes methods to estimate the parameters of condition monitored equipment whose failure rate follows the Cox's time-dependent Proportional Hazards Model. Due to errors of measurement, of interpretation, or due to limited accuracy of measurement instruments, the observation process is not perfect, and does not directly reveal the exact degradation state. At each observation moment, we observe and collect information about an indicator of the underlying unobservable degradation state. To match the indicator's value to the unobservable degradation state, the stochastic relation between them is given by an observation probability matrix. In this study, we consider the case of imperfect observations, and we assume that the equipment's unobservable degradation state transition follows a Hidden Markov Model. We determine the Probability Density Function of the time to failure, and use the Maximum Likelihood Estimation to estimate the model's parameters. These are the Proportional Hazards', and the Hidden Markov Models' parameters. We study the cases of censored, and uncensored data; and carry out simulation studies to test the accuracy, and the convergence of the estimation methods.