System Failure Criteria Research Articles

Railways Health Monitoring Employing KSK Approach: A Novel AIIoT based Decision-Making Approach for Railways

Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.

Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach

The critical infrastructure system exhibits a high degree of instability because of the frequent occurrence of extreme natural disasters. This study proposes a method for exploring the cascading failures of critical infrastructure systems under such impacts, based on dependency relationships for importance analysis and probability assessment. With this method, a cascading failure scenario graph of critical infrastructure is established and empirical research is conducted using typhoon rainfall events as a case study. The main contributions of this study are summarized as follows: it proposes a method for the causal identification of the interrelationships among critical infrastructures; it sorts and evaluates the importance and cascading failure probability of critical infrastructure by using the Interpretative Structural Modeling (ISM) and Rank-order Centroid method (ROC); and it generates scenario graphs of critical infrastructure cascading failures to visualize event states, environmental characteristics, and evolutionary paths. This study provides a new method and tool for disaster risk management of complex systems involved in critical infrastructure.

ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network.

Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input-output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input-output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.

Risk of Stochastic Systems for Temporal Logic Specifications

The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous driving. While there has been a variety of successful demonstrations of these technologies, critical system failures have repeatedly been reported. Even if rare, such system failures pose a serious barrier to adoption without a rigorous risk assessment. This article presents a framework for the systematic and rigorous risk verification of systems. We consider a wide range of system specifications formulated in signal temporal logic (STL) and model the system as a stochastic process, permitting discrete-time and continuous-time stochastic processes. We then define the STL robustness risk as the risk of lacking robustness against failure . This definition is motivated as system failures are often caused by missing robustness to modeling errors, system disturbances, and distribution shifts in the underlying data generating process. Within the definition, we permit general classes of risk measures and focus on tail risk measures such as the value-at-risk and the conditional value-at-risk. While the STL robustness risk is in general hard to compute, we propose the approximate STL robustness risk as a more tractable notion that upper bounds the STL robustness risk. We show how the approximate STL robustness risk can accurately be estimated from system trajectory data. For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly. We illustrate our verification algorithm in the autonomous driving simulator CARLA and show how a least risky controller can be selected among four neural network lane-keeping controllers for five meaningful system specifications.

Condition-based maintenance strategy for redundant systems with arbitrary structures using improved reinforcement learning

The condition-based maintenance (CBM) decision-making for redundant systems has attracted increasing attention. Most existing studies are dedicated to k-out-of-n redundant systems and the search of the optimal maintenance policy is efficient for low-dimensional CBM. In practical applications, complex system structures and failure criteria are commonly observed, posing challenges for searching the optimal CBM policy. This paper studies the optimal CBM strategy for redundant systems with arbitrary system structures using improved reinforcement learning, considering failure and economic dependences. The decisions of imperfect repair and replacement of failed components are considered dynamically, and an efficient solution method of dynamic maintenance strategy is investigated via improved reinforcement learning incorporating re-learning and pre-learning processes. Numerical studies are conducted and the results indicate that the proposed method is effective in reducing the maintenance cost and efficient in searching the optimal CBM strategy for redundant systems.

A review of decentralized and distributed control approaches for islanded microgrids: Novel designs, current trends, and emerging challenges

Distributed energy resources (DER) on the demand side have been fast growing, which could boost energy resilience by uninterruptedly supplying the commercial and residential sectors in the form of islanded microgrids when the utility electricity grid is out of service. Nevertheless, simply applying the centralized hierarchical control strategies, traditionally used for utility electricity grids, onto the islanded microgrids would encounter several critical issues. For instance, the control goals in secondary and tertiary control could be activated tardily, the single-point fault could cause critical system failure, and the properties of dynamic plug and play would be hard to achieve. To this end, decentralized and distributed control approaches have been explored to cope with the issues. Specifically, compared to the centralized hierarchical control, decentralized and distributed control strategies can (i) respond to disturbances more promptly, enhancing the performance of islanded microgrids with limited resources; (ii) guarantee system stability especially when a fault occurs and certain DERs are disconnected from the network; and (iii) facilitate deeper penetration of DERs in the microgrid, owning to the low computational complexity and sparse communication network. In this article, the common approaches for decentralized and distributed control are reviewed, and the current design trends and critical technical challenges are discussed to offer a comprehensive understanding of decentralized and distributed controlled microgrids.

THE RESEARCH OF LEVELS OF DESCRIPTION OF MODERN CRITICAL APPLICATION SYSTEMS

At the current stage of economic and technical development, one of the most important problems is maintaining the reliability and fault tolerance of critical application systems used in the management of nuclear power plants, air, rail and road transport, technological units in metallurgy, the oil and gas industry, etc. The general trend of the development of critical application systems is characterized by the following factors: an increase in the degree of complexity of control objects, an expansion of the composition and level of complexity of solved functional tasks, a reduction in decision-making time, an increase in the cost of errors and the probability of large-scale emergency situations. The main reasons for the escalation of dangerous situations are failures in critical application systems. The publication examines the concept of a critical application system, examines the question of levels of description of critical application systems, examines the issue of interdependent subsystems based on the concept of potential and information technologies supporting the reliability and fault tolerance of critical application systems. For such systems (power supply and energy supply systems, water supply and drainage, etc.), an urgent problem is to ensure the objectivity, reliability and adequacy of forecasting and prevention of dangerous and possible cascading effects that can lead to man-made accidents and catastrophes. This can significantly affect the functioning, stability and survivability of systems of critical application, the ecological and technogenic safety of the surrounding territories, and the safety of life of the population of Ukraine. In the event of damage to the system of critical application, which can be sudden and intense, it will lead to an emergency situation of a man-made nature.

Modelling and analysis of cascading failures in interoperable critical infrastructure systems

Studying the behaviour and structure of the critical infrastructure systems (CISs) and the interdependency among them is of special significance, and if it is not determined properly, in addition to failure and cascading failure, there will be a national catastrophe. One of the reasons for not using the proper technique for critical infrastructure modelling is not to specify the entire sample space of the system and express all its limitations so that in addition to displaying accurate infrastructure behaviour, cascading failures can be prevented to an acceptable extent. Therefore, there is a need for a CIS formal specification modelling to develop different instances of a CIS, and selecting the best and the failure-free instance of it. In this paper, we provide a formal declarative specification of CIS that supports detection and prevention of failure and cascading failure automatically in using Alloy. Since failures and cascading failures occur in dynamic and time-dependent environments, the proposed model considered dynamic aspects of CISs. The results show that using Alloy Analyser, all the models made and the current failures in infrastructure are determined using counter-examples and the best instance in terms of minimum and even without failure can be selected.

A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis

Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.

Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Cascading failure modeling in interdependent critical infrastructure systems (CISs) is an important but challenging work that lays the foundation for disaster impact assessment and recovery planning. However, despite the increasing volume of studies that examine cascading failure across interdependent CISs, the majority of modeling approaches proposed in prior studies do not capture the complex heterogeneous nature and behavior of the CISs under disaster, limiting the ability of these approaches to detailly model the cascading failure process. To address the above limitation, this study proposes a high level architecture (HLA)-based co-simulation approach to integrate heterogeneous domain-specific CIS models and model the cross-domain interdependencies and failure propagation processes. Moreover, the approach provides a novel solution for modeling the dynamic impact of disaster on CISs and analyze its impact on the cascading failure of interdependent CISs. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed approach. The results showed that the model developed using the proposed approach could provide granular data of the state and heterogeneous behaviors of the systems, capture the dynamic evolution of disaster impact on system components, and reveal the paths and mechanisms of failure propagation within and across the systems. The proposed modeling approach can overcome the drawbacks in current interdependent CISs cascading failure models, and provide a foundation for resilience assessment of CISs.

Estimation of the failure flow of a set of passenger car doors

An estimation of the failure flows is a prerequisite for the operation of industrial products. It is based on statistical data about failures that occur within technical items in the process of their operation. In the technical product documentation, this indicator shall be featured in the “Dependability parameter estimation” section. The dependability analysis of rolling stock is still affected by the difficulty of defining the methodology for evaluating this parameter at various system levels. For the purpose of analysing a multicomponent system, a reliability block diagram should be developed, and the possible replacement (redundant) elements should be taken into consideration. Multicomponent systems are often represented through various block diagrams, where, among others, the “m-out-of-n” structure may be used referring to a system with a parallel arrangement of elements that is operable when at least m elements operate. An example of such system is a set of passenger car doors. The manufacturers and customers may have different approaches to calculating technical system dependability. First, the required dependability indicator for the entire train is defined that, in turn, defines the dependability requirements for a car. At the same time, the dependability indicator for a car is determined by the respective values of its components (subsystems, units and parts). However, the nature of the relationship between a car and its components is not always taken into account. At the same time, car manufacturers can and should define in the regulatory documentation (and later supervise in operation) the dependability indicators for a set of doors (components of a car in our case) as a single system. However, the failure criteria of a set of doors are not always defined. This paper examines the method of calculating the failure flow for a set of passenger car doors based on operational data and the failure flow of a single door. Aim. To propose a method for calculating the failure flow of a set of 6 car doors by analysing the possible reliability block diagrams with subsequent transition to transition and state graphs.Conclusions. A number of block diagrams were developed for the purpose of dependability calculation of sets of passenger car doors based on the system failure criterion. The failure flow of a set of car doors was calculated according to the developed block diagrams. It is concluded that the Markovian method of calculating the failure flow is of higher priority than the logic-and-probability approach, since it takes into account the recovery factor. A Markovian method was proposed for calculating the failure flow and recovery time of a set of car doors for the “3-out-of-4” reliability block diagram.

System reliability modeling and life cycle research considering component characteristics and correlation

The accuracy and rationality of the system life depends not only on the detailed grasp of the system operation information, but also on the structural relationship between the internal components of the system and the correlation between the characteristics of each component and the degradation of the components. This article explicitly considers the different characteristics of component failure, and adopts different random process personalized modeling for the failure modes of different components; on this basis, the failure correlation between the components that work together to realize the system function is considered, and the synergistic effect of component failure is integrated into the reliability degradation model of components. In terms of system failure criteria, starting from an economic point of view, the overall working cycle of the system is obtained by comparing the relationship between the replacement cycle of lossy parts and the remaining life of the stable parts. Finally, an example is given to show the effectiveness and practicability of the proposed method.

Reliability assessment for balanced systems with restricted rebalanced mechanisms

Research on reliability modeling and assessment for balanced systems has been getting increased attention in the field of reliability and engineering due to its developments both in theory and applications. In existing research on balanced systems, forcing down and resuming units are two main ways to rebalance the system after unit failures and they are assumed to be perfectly accomplished. In this paper, two restricted rebalanced mechanisms are proposed by considering random failures of unit forced-down function and resumed function respectively with the intent of modelling actual engineering situations. Based on the two restricted rebalanced mechanisms, corresponding new balanced systems are constructed respectively. The operation mechanisms and system failure criteria of the new models are different from those of existing models. A Markov process imbedding approach is employed to analyze the system reliability and derive related probabilistic indices of the proposed models. Case studies have served the purpose to present the applicability of the new balanced systems with restricted rebalanced mechanisms and the effectiveness of the proposed approach.

Evaluation of New Risk-based Regulatory Approach to Classification of GMP Deficiencies (Review)

Introduction . This paper is devoted to analysis of a new risk-based regulatory approach to the classification of deficiencies found by inspections of medicines manufacturers, which is undoubtedly relevant. Text . The article describes the new definitions for the deficiencies found in the medicinal products manufacturing, the proposed gradation of non-conformances distinguishes from the one that is currently used. The proposed innovation in regulatory activity is that a critical deficiency can consist of several related nonconformities, none of which can be critical on their own, but which together can constitute a critical nonconformity or system failure. The proposed approach to the classification of deficiencies, which is based on a risk assessment for the patient, is described. The main stages of the risk-based algorithm are characterized: a detailed assessment of the deficiency for its preliminary classification; assessment of risk increase or decrease factors, regardless of the initial classification; making a decision on the final classification, taking into account the factors of increasing, reducing risk or their absence. Block diagrams and illustrative examples are described, on the basis of which preliminary assignment of a revealed deficiency of one or another level of criticality is carried out. The factors of increasing or decreasing risk are considered. These factors are extrapolated to the category of deficiency and they can both increase the preliminary assigned category or decrease it, and can leave the preliminary classification unchanged. Conclusion . A new risk-oriented regulatory approach to the classification of non-conformances with the requirements of Good Manufacturing Practice, proposed by the Pharmaceutical Inspection Co-operation Scheme PIC / S, is described. An analysis of this approach and its characterization are made. The new approach is based on the systematic and methodological application of risk management tools, aimed at justifiably assigning a certain criticality category to a revealed deficiency. The implementation of the new approach allows to increase the objectivity and transparency of inspection procedures. Further detailed study and possible step-by-step implementation of this new approach is very important for regulators so for medicines manufacturers.

Reliability analysis of k-out-of-n: F balanced systems with multiple functional sectors

In this paper, new concepts of balanced systems are proposed based on real engineering problems. The system under study consists of l groups and each group has n functional sectors. The conception of balance difference is proposed for the first time. It is assumed that unbalanced systems are rebalanced by either forcing down some working units into standby or resuming some standby units into operation. In addition, a case that the forced-down units are subject to failure during standby is studied in this paper. Based on different balanced cases and system failure criteria, two reliability models for balanced systems are developed. The proposed systems have widespread applications in aerospace and military industries, such as wing systems of airplane and unmanned aerial vehicles with balanced engine systems. Markov process imbedding method is used to analyze the number of working units in each sector for each model. Finite Markov chain imbedding approach and universal generating function technique are used to obtain the system reliability for different models. Several case studies are finally presented to demonstrate the new models.

Assessing and strengthening organisational resilience in a critical infrastructure system: Case study of the Slovak Republic

Critical infrastructure is a system that consists of civil infrastructures in which disruption or failure would have a serious impact on the lives and health of the population. It includes, for example, electricity, oil and gas, water supplies, communications and emergency or healthcare services. It is therefore important that technical resilience and organisational resilience is provided continuously and at a high level by the owners and operators of these civil infrastructures. Organisational resilience management mainly consists of continuously assessing determinants in order to identify weak points early so that adequate security measures can be taken to strengthen them. In the context of the above, the article presents a method for Assessing and Strengthening Organisational Resilience (ASOR Method) in a critical infrastructure system. The essence of this method lies in defining the factors that determine organisational resilience and the process of assessing and strengthening organisational resilience. The method thus allows weaknesses to be identified and the subsequent quantification of positive impacts that strengthen individual factors in organisational resilience. A benefit from applying this method is minimizing the risk and subsequent adverse impact on society of critical infrastructure system disruption or failure. The article also contributes to achieving the UN Sustainable Development Goal 9, namely Building Resilient Infrastructure. The ASOR method namely contributes to the development of quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure. Finally, the article presents the results of this method’s practical application on a selected electricity critical infrastructure entity in the Slovak Republic.

Reliable Method to Detect Alloy Soldering Fractures under Accelerated Life Test

In this research, we investigated the development and design of the Accelerated Life Test (ALT) and its approach to the waste of material. The development of a reliability model is based on the moment at which failure appears. The faults detected in welding joints during this research prevented proper current flow within electronic components and this interruption of current is considered a critical system failure. Minitab v18 was used to process data. Through statistical analysis, it was determined that the sample size was adequate with a 95% level of significance. A Shapiro Wilk analysis was carried out to determine the normality of the data, where a p-value of 0.1349 was obtained, which indicates that the data are normal. A Weibull analysis was applied, and it was observed that the data adjusted to the regression analysis and Weibull’s reliability distribution. The results showed that failure phenomena can occur during electronic assembly due to the values of R being too high and too close to each other. Significant issues included the welding alloy, temperature, and the interaction between the welding alloy and vibration. It is observed that with high temperature, the number of faults in the solder alloy used for tin and lead and for tin, silver, and copper were lower. 17 electronic assemblies with measures of 2 cm × 2 cm were fabricated, where components such as leads and electric resistance were used. The objective of analyzing this is to obtain the characteristics of the soldering alloy. Electronic components of this type are used worldwide in all types of electronic components, including: TVs, cell phones, tablet, computers, resistors, diodes, LEDs, and capacitors. For this work, the components were built based on an LED and a diode.

Effect of instructing system limitations on the intervening behavior of drivers in partial driving automation

Proper understanding of automation limitations is vital in order for drivers to deal with unexpected critical situations. The present study focuses on explanation-based knowledge of the limitations given to novice drivers who have no knowledge and experience of using driving automation systems. The knowledge is discussed considering (1) the possibility of the automation failing to issue an alert when the driving automation cannot handle the situation and (2) the manner of describing the limitations from either a functional or scenic point of view. An experiment conducted under 2 × 2 conditions of explanation-based knowledge (n = 24 participants per condition, average age of the participant = 55.9 ± 16.2 years) is implemented in a driving simulator. Data on transition time from automated control to manual control are collected. The results reveal that drivers could intervene more safely if the knowledge is described from a scenic point of view (average ratio of safe intervention = 95%, average reaction time = 2.0 s), as compared to a functional description (88%, 2.3 s). Explicit/scenic knowledge was found to be more beneficial in responding to alerts in such situations involving system limitations as well as in dealing with critical system failures. Further investigation of glance behavior and interviews revealed that novice drivers with explicit/functional knowledge are prone to be over-reliant on the automation’s capability. Therefore, the present study clarified that providing a driver with knowledge about system limitations/failures explicitly while giving is instructive for perceiving and responding to system limitations as well as unexpected hazards due to system failures.

Developing a strategic forensic science risk management system as a component of the forensic science system of systems

ABSTRACTOver the last decade, reviews of the forensic science industry have addressed critical system failures and made recommendations for improvements to the industry. These reviews identify systemic risks and issues but they do not explicitly address the need for strategic risk and issue management systems to deal with the identified issues and to mitigate risks. Risk and issue management, along with quality management systems, are critical components of the forensic science ‘system of systems’, which describes forensic science as a supporting system in the larger criminal justice, law enforcement, intelligence, and military systems. The update of ISO 17,025:2017 General requirements for the competence of testing and calibration laboratories now includes a chapter on risk-based thinking. Furthermore, ISO 31,000 principle and guidelines for risk management was updated in 2018 and it is important for the forensic science industry to consider the updated ISO 17,025:2017 and ISO 31,000. In this paper, we discuss risk and issues management in relation to the forensic science system of systems. The purpose is to review the strategic risks identified in reviews of the forensic science industry to identify risk treatments to help to mitigate against critical system failure, such as miscarriages of justice.

Extended Phase-type models for multistate competing risk systems

Two extended Phase-type models with competing risks for failures of Markov repairable systems with absorbing states are presented in this article. We divide the states into three subsets: perfect, imperfect and failure (absorption) for a Markov repairable system. Two models are developed in terms of the proposed failure criteria of the repairable system. For model 1, the system failure criteria of the repairable system are, whichever occurs first, (1) when the system goes into the failure state (absorption state), or (2) when the system transfers from imperfect states to other states, and before that time the transitions from perfect to imperfect states reached a specified number. For model 2, there still are two criteria whichever occurs first. The first one is the same as failure criterion (1) in model 1, but failure criterion (2) is replaced by (3) which is when the sojourn time in imperfect states exceeds a given threshold. Under two models, two distributions are proposed, which are extensions of the well-known Phase-type distribution. Some reliability indexes under two models, such as the distributions of lifetimes, the point-wise availabilities, various interval availabilities, are given. Finally, some numerical examples are presented to illustrate the results obtained in this article.