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This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability, and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 are utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently. [ABSTRACT FROM AUTHOR]

The decision-making process in Industrial Wireless Sensor Networks heavily relies on the information provided by smart sensors. Ensuring the trustworthiness of these sensors is essential to prolong the lifetime of the network. Additionally, dependable data transmission by sensor nodes is crucial for effective decision-making. Trust management approaches play a vital role in safeguarding industrial sensor networks from internal threats, enhancing security, dependability, and network resilience. However, existing trust management schemes often focus solely on communication behaviour to calculate trust values, potentially leading to incorrect decisions amidst prevalent malicious attacks. Moreover, these schemes often fail to meet the resource and dependability requirements of IWSNs. To address these limitations, this paper proposes a novel hybrid Trust Management Scheme called the Multi-layered Assessment System for Trustworthiness Enhancement and Reliability (MASTER). The MASTER scheme employs a clustering approach within a hybrid architecture to reduce communication overhead, effectively detecting and mitigating various adversarial attacks such as Sybil, Blackhole, Ballot stuffing, and On–off attacks with minimal overheads. This multifactor trust scheme integrates both communication-based trust and data-based trust during trust estimation, aiming to improve the lifetime of industrial sensor networks. Furthermore, the proposed MASTER scheme utilizes a flexible weighting scheme that assigns more weight to recent interactions during both direct and recommendation (indirect) trust evaluation. This approach ensures robust and precise trust values tailored to the specific network scenario. To efficiently process and glean insights from dispersed data, machine learning algorithms are employed, offering a suitable solution. Experimental results demonstrate the superior performance of the MASTER scheme in several key metrics compared to recent trust models. For instance, when 30% of malicious Sensor Nodes (SNs) exist in a network comprising 500 sensor nodes, the MASTER scheme achieves a malicious behaviour detection rate of 97%, surpassing the rates of other models. Even after the occurrence of malicious SNs exceeding 30%, the False Negative Rate (FNR) in the MASTER scheme remains lower than other models due to adaptive trust functions employed at each level. With 50% malicious SNs in the network, the MASTER scheme achieves a malicious behaviour detection accuracy of 91%, outperforming alternative models. Moreover, the average energy consumption of SNs in the MASTER scheme is significantly lower compared to other schemes, owing to its elimination of unnecessary transactions through clustered topology utilization. Specifically, with 30% and 50% malicious SNs in the network, the MASTER scheme achieves throughput rates of 150 kbps and 108 kbps, respectively, demonstrating its efficiency in challenging network scenarios. Overall, the proposed MASTER scheme offers a comprehensive solution for enhancing security, trustworthiness, and collaboration among sensor nodes in IWSNs, while achieving superior performance in various metrics compared to existing trust models. [ABSTRACT FROM AUTHOR]

Effective quality management plays a pivotal role in ensuring the smooth operation of smart city systems, which have significant implications for safety, accessibility, affordability, and maintainability. Dependability of autonomous systems is of utmost importance, as achieving satisfactory levels of availability and reliability poses considerable challenges. Smart cities are characterized by interconnected sub-architectures, encompassing vehicle monitoring, sidewalk monitoring, and building monitoring, all of which need to function efficiently. Analytical models such as Petri nets, Markov chains, and fault trees are well-suited for evaluating complex scenarios in the context of smart cities. This paper presents analytical models that utilize fault tree and Markov chain techniques to assess the availability and reliability of smart city monitoring systems. The model is divided into shared and non-shared components, with non-shared components being specific to certain contextual applications, while shared components, such as data processing and electrical power, are essential for all smart city monitoring and management systems. The study underscores the ease with which the fault tree model can enhance availability by modifying failure requirements and resources. Case studies provide concrete examples of how availability improved from 95.3 to 99.8% by varying a configuration known as "KooN" in multiple components. This paper takes a comprehensive approach to evaluating the dependability of smart city architectures and contributes advancements, such as hierarchical modeling, sequential sensitivity analysis, and the "KooN" analytic method. These contributions expand the existing knowledge and methodologies in smart city dependability analysis. Moreover, this work aims to serve as a practical tool to assist smart city managers in optimizing their proposals. All modeling aspects and parameters are detailed thoroughly to enable effective implementation of the proposed approach by anyone using it. [ABSTRACT FROM AUTHOR]

One of the most popular models that provide computer resources today is cloud computing. Today's dynamic and successful platforms are created to take advantage of various resources available from service providers. Ensuring the performance and availability of such resources and services is a crucial problem. Any software system may be subject to faults that might propagate to cause failures. Such faults with the potential of contributing to failures are critical because they impair performance and result in a delayed reaction, which is regarded as a dependability problem. To ensure that critical faults can be discovered as soon as possible, the impact of such faults on the system must be tested. The performance and dependability of cloud-native systems are examined in this empirical study using fault injection, one of the chaos engineering techniques. The study explores the impacts and results of injecting various delay times into two cloud-native applications with diverse user numbers. The performance of the applications with various numbers of users is measured in relation to these delays, which accordingly reflects measuring the dependability of those systems. Firstly, the systems' architecture were identified, and serverless with two Lambda functions and containerised microservices applications were chosen, which depend on utilising and incorporating cloud-native services. Secondly, faults are injected in order to quantify performance attributes such as throughput and latency. The results of several controlled experiments carried out in real-world cloud environments provide exploratory empirical data, which promoted comparisons and statistical analysis that we utilised to identify the behaviour of the application while experiencing stress. Typical results from this investigation include an overall reduction in performance that is embodied in an increase in latency with injecting delays. However, a remarkable result is noticed at a particular delay in which defects and availability problems appear out of nowhere. These findings assist in highlighting the value of using chaos engineering in general and fault injection in particular to assess the dependability of cloud-native applications and to find unpredicted failures that could arise quickly from defects that aren't supposed to spread and result in dependability issues. [ABSTRACT FROM AUTHOR]

Despite widespread evidence-based treatments for addressing internalizing concerns, many youth do not demonstrate reliable or clinically meaningful improvement. Regular progress monitoring, consisting of measurement and feedback, offers the opportunity to improve outcomes in real time. The 21-item Depression Anxiety and Stress Scales (DASS-21; Lovibond & Lovibond, 1995) has potential as a progress-monitoring tool for internalizing concerns in youth; however, limited psychometric data are available to support this use. The purpose of this study was to investigate the dependability of data obtained from the DASS-21 when completed by adolescents in a clinical setting. This study also aimed to understand the feasibility and utility of using the DASS-21 as a progress-monitoring tool from youth and clinicians' perspectives. Generalizability and dependability analyses were conducted to determine the number of ratings needed to obtain a dependable estimate of youth functioning within 1 week. Whereas two daily ratings were needed to dependably estimate total distress, results for the depression, anxiety, and stress subscales indicated that two to five data points would be needed over the course of the week. Finally, results demonstrate the usability of the DASS-21 from both youth and clinician perspectives when used in a progress-monitoring context. [ABSTRACT FROM AUTHOR]

The mechatronic industry is currently subject to huge changes challenging it to offer products matching individual customer requirements at competitive prices. The design of such products calls for sophisticated and complex components integration following different technologies. Since we are on the cusp of the Fourth Industrial Revolution, in which the world of mechatronic production, network connectivity, the Internet of Things, and cyber-physical systems are correlated, the complexity of these systems increases exponentially, and we are talking about advanced mechatronic systems. To assist these changes, various methods, sweeping all project phases, are used by business houses. Predictive dependability assessment in the earlier design stage is considered a powerful metric used to evaluate the performances of different kinds of mechatronic products before the production phase. Altogether, dependability analysis ties the design directly to the desired functionality, operability, and integrity of the system. This paper explores an approach to assessing the dependability attributes, reliability, availability, and maintainability (RAM), of repairable mechatronic systems based on timed colored Petri nets and a Monte Carlo simulation, integrating simultaneously diverse components technologies: mechanical, electronic, and software. The proposed approach is tested taking the case of a regenerative braking system. The methodology appears to be efficient for evaluating predictive RAM indicators (MTTFF, MTTR, MTBF...) for the whole system and for each individual component separately. [ABSTRACT FROM AUTHOR]

This paper addresses two complex areas of research: the detection of turn-to-turn faults (TTF) in power transformers and the impact of inverter-based resources on the TTF protection scheme operation. Detecting turn-to-turn faults in power transformers by protection algorithms poses a challenge due to the minimal fault currents observed at transformer terminals. Yet, the demand for dependable TTF protection is very high because of the high fault currents inside the shorted turns and the resulting damage consequences. On the other hand, for such sensitive protection, adverse conditions such as transformer inrush currents or CT errors may lead to protection maloperation. Moreover, the fault current characteristic of the inverter-based source infeed is very different compared to the synchronous machine infeed, particularly concerning the negative-sequence current used in the TTF protection schemes, which calls for thorough research analysis. A simulation model of the converter transformer capable of simulating TTFs, and the Modular Multilevel Converter (MMC) for a High Voltage Direct Current (HVDC) transmission link, has been developed. The test results of turn-to-turn fault protection schemes in inverter-based generation-dominated power systems compared to the synchronous generator infeed are presented. The negative-sequence current protection quantities are analysed in more detail for TTFs with small and large number of shorted turns, i.e. without and with reactive negative-sequence current injection by the MMC control. Finally, the paper assesses the dependability of the transformer differential protection and sensitive TTF protection schemes in detecting faults with different numbers of shorted turns and fault resistance for TTFs occurring in the star and delta winding of the converter transformer.

Long-duration human space missions require intelligent regenerative life support systems that can recycle resources and automatically manage failures. This paper explores using Petri nets to model the reliability and complex interactions of such closed-loop systems. An architecture consisting of primary systems, backups, and consumable reserves is outlined. The automation system that controls everything is described. Petri nets can capture concurrency, failure modes, redundancy, and dynamic behavior. A modular modeling methodology is presented to develop hierarchical Petri net models that scale in fidelity. Elementary fragments represent failures and redundancy. Subsystem modules can be substituted for more detailed models. Analysis and simulation assess system reliability and failure response. This supports designing ultra-reliable systems to safely sustain human life in space.

In the industrial systems there is a requirement that systems should work efficiently for long time. System performance is an important aspect for failure free operation but in real practice complete failure free operation of any production system is seldom possible. Detailed critical literature review for the past thirty three years of Reliability, Maintainability and Availability (RAM) approaches has been carried out which can help to improve performance of Complex systems. Review of some papers provided the detailed information about past and current scenario of RAM practices in research field and industries. Different RAM tools and techniques extracted from the review may be helpful in qualitative and quantitative analysis of the complex systems. In this paper, author tried to focuss on some major aspects of RAM approaches. [ABSTRACT FROM AUTHOR]

The paper examines the specificity of artificial intelligence-based automatic train operation systems. Justifying the functional safety (FS) of such systems is quite difficult. The paper proposes a process for proving the functional safety of intelligent systems. A hybrid control system for a shunting locomotive was developed and analysed. It combines machine vision (MV), train protection devices and manual control by a driver. A model is presented that allows examining the functional safety of a locomotive control system layer by layer, i.e., evaluating the time to safety degradation depending on the component failure and subsequent requirement of bringing the locomotive to a complete stop. This allows to improve the FS of the shunting locomotive control system with machine vision from SIL 2 to SIL 3 and maintaining it during sufficiently long periods of time (over a quarter of the mean time to system failure). The mean time of faultless operation of a locomotive control system until it has to be brought to a complete stop for safety reasons can be increased three times. A general approach is proposed to design the functional safety of automatic train operation systems. It is based on the division of the information processing process into two subprocesses, i.e., internal intelligent information processing onboard the locomotive for the purpose of decision-making regarding track vacancy and communication of initial visual information to the operating driver for decision-making. The division of this process must be combined with redundant machine vision facilities, regular comparison of the outputs of the onboard and fixed machine vision facilities, redundant comparison outputs, smoothing of the outputs in the process of locomotive movement. [ABSTRACT FROM AUTHOR]

When designing new systems and components, it is very important to correctly determine the degree and ability of the joint to withstand stress and load. Every new product that is intended for the market must meet the requirements for high safety and reliability during the entire life cycle. The presented article deals with the possibility of modelling the ability to withstand such a load, the principle of the interference method was used in the experimental modelling. The interference theory of reliability is based on the analysis of regularities and properties of two random variables that characterize reliability. Among these elementary properties from the point of view of reliability assessment, we can successfully use dependability and lifetime analysis. It originates from the concept of "safe life", which is determin-istic, based on determining and respecting the values of reliability factors. The described approach as-sumes that a malfunction or a faulty function occurs when the strength limit of the object is exceeded, i.e., ability to withstand stress. [ABSTRACT FROM AUTHOR]

Space vehicles, from spacecraft to launchers and landers or probes, have as purpose a large range of scopes and aim at fulfilling a broad number of critical functions in many fields of work such as science, Earth observation, navigation, telecommunication. The cost of any of these missions can range from several million to billions, and hence most of these vehicles do not allow access for on-board maintenance, planning ahead and targeting high reliability is imperative. Reliability availability maintainability and safety (RAMS) analyses represent tools used during design and development to ensure that the right choices are made from all points of view such as: engineering budgets, cost, safety, survivability, and planetary protection. RAMS or dependability and safety as commonly referred in various standards, represents, for a space mission, an iterative process that starts at the beginning of the project and ends with decommissioning. Similarly to system engineering and fault detection isolation and recovery (FDIR), RAMS is a system discipline that tackles and supports all fields of engineering that could be encountered within a space mission. Throughout the project life cycle, the RAMS assurance program is implemented, and it comprises a number of technical activities that consist of an extensive number of analyses to be performed at various stages. Sometimes, in projects, it is noticed that the purpose of these analyses is slightly neglected or misunderstood and certain outputs from the RAMS analyses are not properly used. This paper aims at clarifying this aspect focusing first on a general introduction with regard to RAMS and overall use of RAMS tools in engineering. A brief review of existing literature will be made with regard to typical RAMS mathematical models based on the existing and currently used body of knowledge followed by the use of RAMS tools and analyses in space engineering and space missions description. Further on, a clear view on the RAMS analyses from system to equipment level through the "RAMS map" will be provided. The "RAMS map" also aims to show the recommended use of RAMS analyses within space missions and to clarify their interdependencies with other fields of expertise by pointing out what outputs/inputs are required to have balanced and optimized results during design and development. Such detailed diagram that shows the dynamics on various levels of implementation for the overall RAMS process within a spacecraft has not been found in literature yet. The "RAMS map" presented within this paper can, therefore, represent an extremely useful tool for space system engineers and mission analysts. It provides a rather generic view based on standards, expert judgment, and overall experience acquired from a variety of past and on-going space missions with main focus on spacecraft. This "RAMS map" can be tailored per each mission and several examples will be provided as part of the paper to better show the adaptability and versatility of the "RAMS map". The developed and presented work represents an effective way to show, explain, and understand the way the RAMS methods should be used within a space mission to be able to provide valuable input for design and development to system engineering and FDIR engineering. The paper also aims at highlighting the importance of RAMS for optimization and reduction of cost, schedule, risk, and design budgets, while maximizing overall mission success. [ABSTRACT FROM AUTHOR]

Downhole safety valves are essential equipment for oil and gas extraction, and it is crucial to carry out a downhole safety valve failure risk evaluation and reliability analysis to ensure the safety of oil and gas production. In order to improve the operation and maintenance management level of downhole safety valves and explore the key failure risk factors of downhole safety valves, this study firstly carries out a Failure Mode and Criticality Analysis of downhole safety valves; identifies the causes of failure of downhole safety valves and the consequences of accidents through the Bow-tie method; and quantitatively evaluates the failure risk factors based on the improved Decision-making Trial and Evaluation Laboratory method and obtains the influence and importance ranking of 14 types of failure risk factors. Specific preventive measures for key failure risk factors are proposed in several aspects: optimising the structural design of downhole safety valves, improving the processing and manufacturing process, setting up an efficient field management team, carrying out equipment operation and maintenance management training, establishing a field failure response mechanism, and setting up an intelligent O&M management platform for downhole safety valves. The research results of this study are conducive to improving the reliability of downhole safety valves, ensuring the safety and integrity of on-site operation and maintenance management, and providing theoretical guidance for the analysis of the risk of failure and operation and maintenance management of downhole safety valves. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, health-care, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these digital services, whether due to human or technical failures, natural disasters, or terrorism, would significantly impact citizens’ daily lives. Hence, 6G networks need not only to provide high-performance services but also to be resilient in maintaining essential services in the face of potentially unknown challenges. This paper provides a general review of the state of the art on resilient systems, definitions, concepts, and approaches. Moreover, it introduces a comprehensive concept, i.e., resilience-by-design (RBD), in three different levels for designing resilient 6G communication networks, summarizing our initial studies within the German Open6GHub project. First, we outline the general RBD enabling principles and discuss their related sub-categories. Next, adopting an interdisciplinary approach, we propose to embed these principles across all 6G layers/perspectives including electronics, physical channel, network components and functions, networks, services, and cross-layer and cross-infrastructure considerations and discuss their challenges. We further elaborate the RBD principles and their realizations along with several 6G use-cases. The paper is concluded by presenting a comprehensive list of open problems for future research on 6G resilience.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability, and low costs. These subsystems are usually part of the thermal power plant’s coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 are utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model’s effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system’s status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

Safety-critical systems used in applications that demand high levels of dependability, efficiency, and fault-tolerance often use sequential logic circuits in its design and implementation. The safety-critical digital system typically uses latches, flip-flops, and other memory elements, which are prone to the effects of natural faults and single event upsets (SEUs) caused by radiation-induced effects. The faults can lead to subsystem failures due to the continuous advancement in the realization of the small size transistor. To design a reliable digital-based system, it is essential to develop new faulttolerance approaches that are integrated into the design of sequential logic circuits. This work proposes a novel fault-tolerant approach based on the redundancy of sequential logic circuit, which consists of a variety of design components, D flip-flop storage elements linked to a fault injection unit, a duplicate modular redundancy, and data monitoring units with a switching circuit. The experimental simulation results using a five-state Markov chain analysis model prove that the proposed fault-tolerant system can achieve 0.99999998 for reliability of the fault detection coverage (C) which equal to 0.99999. Finally, we believe that using this new approach of fault-tolerance and redundancy would improve the dependability and reliability of next generation safety-critical applications. [ABSTRACT FROM AUTHOR]

This study focused on identifying potential locations for the implementation of Small Hydropower Systems to enhance the existing energy infrastructure in the Pumpum River Basin (PRB). Utilizing remote sensing and the Soil and Water Assessment Tool, basin parameters were generated and simulated for hydropower analysis. The energy potential of suitable sites was assessed through flow duration curves and power duration curves. The findings reveal that the PRB exhibits potential for small hydropower development, identifying nine sites along the river. The results of the model indicate power capacities of 1,521 kW for the Run of River and 2,290 kW for the dam sites respectively. The most promising hydropower sites were identified as dam site 1, river of run site 1, and dam site 6 with corresponding firm power of 2356.2 kW, 1567 kW and 264 kW, resulting in estimated firm energy production of 12.07 GWh, 8.03 GWh, and 1.35 GWh, respectively. Considering the suitable spacing of more than between 12 km among dam site 1, river of run site 1, and dam site 6 and a combined annual energy production of 21.65 GWh, a recommended approach involves establishing a cascade hydropower arrangement at sites dam site 1, river of run site 1, and dam site 6 along the Pumpum River. With a plant capacity of 65% to 90% dependability range, incorporating an installed Francis turbine, the total annual firm energy that can be produced from the nine (9) (Run of river and dam) projected sites ranges between 0.69 and 12.07 GWh. [ABSTRACT FROM AUTHOR]

Long-duration human space missions require intelligent regenerative life support systems that can recycle resources and automatically manage failures. This paper explores using Petri nets to model the reliability and complex interactions of such closed-loop systems. An architecture consisting of primary systems, backups, and consumable reserves is outlined. The automation system that controls everything is described. Petri nets can capture concurrency, failure modes, redundancy, and dynamic behavior. A modular modeling methodology is presented to develop hierarchical Petri net models that scale in fidelity. Elementary fragments represent failures and redundancy. Subsystem modules can be substituted for more detailed models. Analysis and simulation assess system reliability and failure response. This supports designing ultra-reliable systems to safely sustain human life in space. [ABSTRACT FROM AUTHOR]

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability. [ABSTRACT FROM AUTHOR]

This paper presents methods of dependability assessment and safety of aeronautical engineering selected from scientific achievements of the Air Force Institute of Technology. Studying dependability and safety was preceded by the implementation of the computer system used for the support of the operational process and consisting of the following modules: recording, database and module for processing of operational information. The processing system was provided with the developed models and assessment algorithms. The dependability and safety of military aircraft are assessed by registering defects appropriately classified into causes and effects that determine certain factors and their correlation with aircraft incidents and accidents. A significant assessment factor is a failure flux parameter, damageability, and probability of performing an aircraft operation with or without failure to the aircraft. Favourable flight conditions and the occurring disruptions are taken into consideration. Correlation assessment between failure frequency and the probability of favourable operations enables us to evaluate the relationship between dependability and flight safety. [ABSTRACT FROM AUTHOR]

Abstract High‐dependability Internet of Vehicle (IoV) is essential for urban transit, since it contributes to the safe operation of vehicles, and even to the safety of the entire city. This paper introduces the IoV cluster model with network slicing. Based on this model, an integrated vehicles clustering and network slicing scheme for IoV networks is introduced to guarantee the dependability of different traffics in IoV. The network slicing scheme is proposed to divide the wireless frequency resource of the IoV network into separate logical networks. A ChristmasTree analysis model, based on age of information, is utilized as an integrated indicator for evaluating the dependability of the IoV system in urban transit. Asynchronous reinforcement learning with advantage actor‐critic is employed to optimize the integrated dependability of IoV. It is demonstrated in simulations that the proposed IoV cluster incorporating the network slicing scheme can guarantee the integrated dependability of the IoV system. Moreover, by the application of reinforcement learning, the system dependability can be further improved.

The mechatronic industry is currently subject to huge changes challenging it to offer products matching individual customer requirements at competitive prices. The design of such products calls for sophisticated and complex components integration following different technologies. Since we are on the cusp of the Fourth Industrial Revolution, in which the world of mechatronic production, network connectivity, the Internet of Things, and cyber-physical systems are correlated, the complexity of these systems increases exponentially, and we are talking about advanced mechatronic systems. To assist these changes, various methods, sweeping all project phases, are used by business houses. Predictive dependability assessment in the earlier design stage is considered a powerful metric used to evaluate the performances of different kinds of mechatronic products before the production phase. Altogether, dependability analysis ties the design directly to the desired functionality, operability, and integrity of the system. This paper explores an approach to assessing the dependability attributes, reliability, availability, and maintainability (RAM), of repairable mechatronic systems based on timed colored Petri nets and a Monte Carlo simulation, integrating simultaneously diverse components technologies: mechanical, electronic, and software. The proposed approach is tested taking the case of a regenerative braking system. The methodology appears to be efficient for evaluating predictive RAM indicators (MTTFF, MTTR, MTBF…) for the whole system and for each individual component separately.