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The standard IEC 61508 provides a methodology to calculate β, a factor used to estimate the probability of common cause failures (CCF), i.e., failures that result from a single cause. This methodology consists of answering 37 checklist questions, each one providing a scored value that is accumulated in the final β-factor. Those questions cover 8 different defense measures, i.e., practices done to mitigate the CCF against system dependencies. Since the inception of the standard in 2010, there has been evolution regarding both new technologies with an impact on the system dependency factors, as well as new knowledge on how to address them. Hence, it is important to capture these aspects and update the methodology that can be used to reason about CCF’s causes. In this paper, we present an enhanced methodology for estimating the β-factor, which builds upon the core methodology provided by IEC 61508. In particular, we add 33 new questions and provide an estimation method for scoring the β-factor. We also illustrate our methodology by applying it to a realistic system and discuss the findings. Our proposed methodology permits the consideration of aspects not included in the core methodology, such as the level of defense support and safety culture. It also allows practitioners to consider more dependencies, leading to CCF reduction. The rationale is that the more defenses are addressed, the more protection can be achieved against CCF. 

CAMP proposes a hierarchical cache subsystem for multi-core mixed criticality processors, focusing on ensuring worst-case execution time (WCET) predictability in automotive applications. It incorporates criticality-aware locked L1 and L2 caches, reconfigurable at mode change intervals, along with criticality-aware last level cache partitioning. Evaluation using CACOSIM, Moola Multicore simulator, and CACTI simulation tools confirms the suitability of CAMP for keeping high-criticality jobs within timing budgets. A practical case study involving an automotive wake-up controller using the sniper v7.2 architecture simulator further validates its usability in real-world mixed criticality applications. CAMP presents a promising cache architecture for optimized multi-core mixed criticality systems. 

The recent advances in digitalization, improved connectivity and cloud based services are making a huge revolution in manufacturing domain. In spite of the huge potential benefits in productivity, these trends also bring in some concerns related to safety and security to the traditionally closed industrial operation scenarios. This paper presents a high-level view of some of the research results and technological contributions of the InSecTT Project for meeting safety/security goals. These technology contributions are expected to support both the design and operational phases in the production life cycle. Specifically, our contributions spans (a) enforcing stricter but flexible access control, (b) evaluation of machine learning techniques for intrusion detection, (c) generation of realistic process control and network oriented datasets with injected anomalies and (d) performing safety and security analysis on automated guided vehicle platoons.

Context: Collaborative systems enable multiple independent systems to work together towards a common goal. These systems can include both human-system and system-system interactions and can be found in a variety of settings, including smart manufacturing, smart transportation, and healthcare. Safety is an important consideration for collaborative systems because one system's failure can significantly impact the overall system performance and adversely affect other systems, humans or the environment. Goal: Fail-safe mechanisms for safety-critical systems are designed to bring the system to a safe state in case of a failure in the sensors or actuators. However, a collaborative safety-critical system must do better and be safe-operational, for e.g., a failure of one of the members in a platoon of vehicles in the middle of a highway is not acceptable. Thus, failures must be compensated, and compliance with safety constraints must be ensured even under faults or failures of constituent systems. Method: In this paper, we model and analyze safety for collaborative safety-critical systems using hierarchical Coloured Petri nets (CPN). We used an automated Human Rescue Robot System (HRRS) as a case study, modeled it using hierarchical CPN, and injected some specified failures to check and confirm the safe behavior in case of unexpected scenarios. Results: The system behavior was observed after injecting three types of failures in constituent systems, and then safety mechanisms were applied to mitigate the effect of these failures. After applying safety mechanisms, the HRRS system's overall behavior was again observed both in terms of verification and validation, and the simulated results show that all the identified failures were mitigated and HRRS completed its mission. Conclusion: It was found that the approach based on formal methods (CPN modeling) can be used for the safety analysis, modeling, validation, and verification of collaborative safety-critical systems like HRRS. The hierarchical CPN provides a rigorous way of modeling to implement complex collaborative systems. 

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community.

Assemblies of machinery commonly require control systems whose functionality is based on application software. In Europe, such software requires high safety integrity levels in accordance with the Machinery Directive (MD). However, identifying the essential regulatory requirements for the safety approval is not an easy task. To facilitate this job, this paper presents a process for Safety Argumentation for Machinery Assembly Control Software (SAMACS). We are inspired by patterns provided in the Goal Structuring Notation (GSN) and the use of contracts in safety argumentation. SAMACS contribution is aligning those methods with the MD by adopting EN ISO 13849. In particular, we define safety goals based on expected software contribution to control system safety and the standard guidance. Software safety goals are detailed into software safety requirements and expressed further as contracts, which shall be verified with prescribed techniques. We apply SAMACS to a case study from a European mining company and discuss the findings. This work aims at helping practitioners compose the safety case argumentation necessary to support machinery integration approval in Europe. 

A small deviation in manufacturing systems can cause huge economic losses, and all components and sensors in the system must be continuously monitored to provide an immediate response. The usual industrial practice is rather simplistic based on brute force checking of limited set of parameters often with pessimistic pre-defined bounds. The usage of appropriate machine learning techniques can be very valuable in this context to narrow down the set of parameters to monitor, define more refined bounds, and forecast impending issues. One of the factors hampering progress in this field is the lack of datasets that can realistically mimic the behaviours of manufacturing systems. In this paper, we propose a new dataset called MIDAS (Modular Ice cream factory Dataset on Anomalies in Sensors) to support machine learning research in analog sensor data. MIDAS is created using a modular manufacturing simulation environment that simulates the ice cream-making process. Using MIDAS, we evaluated four different supervised machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron) for two different problems: anomaly detection and anomaly classification. The results showed that multilayer perceptron is the most suitable algorithm with respect to model accuracy and execution time. We have made the data set and the code for the experiments publicly available, to enable interested researchers to enhance the state of the art by conducting further studies.

Safety systems, i.e., systems whose malfunction can result in catastrophic consequences, are usually designed with redundancy in mind to reach high levels of reliability. However, Common Cause Failures (CCF), i.e., single failure events affecting multiple components or functions in a system, can threaten the desired reliability. To solve this problem, practitioners must use proven methods, such as those recommended by standards, to support CCF quantification. In particular, the β-factor model has become the de-facto model since the safety standard IEC 61508 considers it. As such standard applies to all industries, practitioners must figure out the industrial-specific implementation procedures. In this paper, we conducted a systematic literature review to understand how the β-factor model has been used in practice. As a result, we found 20 different models, which are industry/project-specific extensions of the first β-factor model proposed for the nuclear sector. We further classified those models by considering how the β-factor is estimated, and the level of redundancy support. Tool support for the models and their industrial use are also outlined. Finally, we present a discussion that covers the implication of our findings. Our study targets practitioners and researchers interested in using current β-factor models or evolving new ones for specific project needs.

Tight end-to-end worst-case delay bounds for periodic traffic streams are essential for time sensitive networks. In this paper, we provide an algorithm to compute a tight (and accurate) end-to-end worst-case bound by considering distinct topological patterns and the manner in which streams enter and leave switches. This refined analysis uses non-preemptive, strict-priority arbitration mechanism commonly deployed in Ethernet switches. Compared to the state-of-the-art that considers all higher and equal priority interference as contributing to the worst-case bound, we present an analytical approach for computing a tighter worst-case delay bound and prove through discrete event simulations that only a certain number of equal-priority interference streams can actually affect the worst-case case. Our results enable efficient resource allocation and have implications for online re-configuration mechanisms for time-sensitive factory communication systems.

Practitioners report improved productivity as one of the main benefits of using autonomous dump trucks in underground mining. However, manned vehicles are still needed to transport materials and personnel in the tunnels, which requires practices that may diminish autonomy benefits. Thus, both fleets shall be efficiently mixed to maximize the autonomy potential. In addition, sufficient safety shall be demonstrated for operations approval. This paper proposes a strategy to populate a GSN (Goal Structuring Notation) structure to argue for the sufficient safety of mixed traffic operations in underground mining. Our strategy considers SoS (System of Systems) concepts to describe the operations baseline and the initial argumentation line, i.e., risk reduction mitigation strategies for existing SoS components. Such a strategy is further detailed with risk reduction mitigation arguments for control systems. Mitigation strategies at both levels are derived from safety analysis supported by STPA (System-Theoretic Process Analysis), a safety analysis technique that aligns well with the SoS perspective. We also incorporate regulatory frameworks addressing machinery to align the arguments with mandatory statements of the machinery directive. Our strategy combines SoS concepts with analysis techniques and regulatory frameworks to facilitate safety case argumentation for operations approval in the European mining context.