Improve System Availability Research Articles

Cloud Migration Framework: Transitioning from On-Premises to Azure Cloud for Improved System Reliability and Scalability

This article examines the transition from on-premises IT infrastructure to the Microsoft Azure cloud platform, aimed at enhancing the reliability, flexibility, and scalability of information systems. The purpose of the study is to analyze the migration process from an on-premises network to the Microsoft Azure cloud platform. The article explores the stages of migration, including the assessment of the current infrastructure, the design of an architecture based on cloud technologies, and the implementation of monitoring tools and process automation. The methodology is based on theoretical principles, practical recommendations, and examples of successful migrations. The findings demonstrate that transitioning to the Azure platform improves system availability and reliability through its distributed infrastructure and automated resource management tools. Cloud technologies enable organizations to scale computing power flexibly, adapting to changing demands. Azure also offers integration with other cloud services, expanding the functional capabilities of the ecosystem. This article will be useful for IT professionals, IT managers, cloud solution architects, and developers interested in efficient migration and optimization of IT structures. The conclusion emphasizes that a well-planned cloud transition can reduce operational costs, improve system resilience, and enhance the competitiveness of IT systems, thereby supporting organizational goals.

An induction machine‐based drive system and supervisory control for application to smart water‐pumped storage and networks

AbstractThe authors present an effective induction machine (IM) drive system and intelligent supervisory control for application to smart water‐pumped storage and networks. The main IM drive unit comprises reduced voltage starter, regenerative clutch/variable speed mode fluid coupling and reversible pump to allow bidirectional power flow. Under the wide umbrella of supervisory control, the following objectives are achieved: (1) Recurrent neural network (RNN) and RNN‐aided Kalman filter are proposed for wired and wireless sensor fault‐tolerant control of the plant based on Wald's likelihood ratio test. (2) Cheap fuzzy logic‐based availability assessment scheme is presented and deployed on cloud to arrange for preventive maintenance of the starter. (3) Robust energy saving mode decision‐making algorithm is developed for best energetic cost reduction. (4) Remote synchronisation method of multi‐motor starting with a mechanically switched capacitor is introduced to minimise voltage dip. Further, an auxiliary inverter fed pump unit is adopted to improve system availability and revenue. The validity and superiority of the proposed drive system and control are confirmed through theoretical analysis, computer simulations, cost study, efficiency evaluation and application example to a single‐pump water network during motor and generator operational modes using preliminary test results.

Mathematical Optimization of Wind Turbine Maintenance Using Repair Rate Thresholds

As reliance on wind energy intensifies globally, optimizing the efficiency and reliability of wind turbines is becoming vital. This paper explores sophisticated maintenance strategies, crucial for enhancing the operational sustainability of wind turbines. It introduces an innovative approach to maintenance scheduling that utilizes a mathematical model incorporating an alternating renewal process for accurately determining repair rate thresholds. These thresholds are important for identifying optimal maintenance timings, thereby averting failures and minimizing downtime. Central to this study are the obtained generalized analytical expressions that can be used to predict the total repair time for an observed entity. Four key lemmas are developed to establish formal proofs for the probability density function (PDF) and cumulative distribution function (CDF) of repair rates, both above and below critical repair rate thresholds. The core innovation of this study lies in the methodological application of PDFs and CDFs to set repair time thresholds that refine maintenance schedules. The model’s effectiveness is illustrated using simulated data based on typical wind turbine components such as gearboxes, generators, and converters, validating its potential for improving system availability and operational readiness. By establishing measurable repair rate thresholds, the model effectively prioritizes maintenance tasks, extending the life of crucial turbine components and ensuring consistent energy output. Beyond enhancing theoretical understanding, this research provides practical insights that could inform broader maintenance strategies across various renewable energy systems, marking a significant advancement in the field of maintenance engineering

Aerial computing: Enhancing mobile cloud computing with unmanned aerial vehicles as data bridges—A Markov chain based dependability quantification

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win–win solution for cost-effective and energy-saving communication and computation services in various environments.

Multi‐state reliability assessment for shipboard hybrid turbine‐diesel generation system with redundancy and aging effects

AbstractComplex system structures, dynamic energy demands, and tough operating environment pose a huge challenge to reliability assessment and risk control. Taking multi‐state, redundant structure and aging impact into account, reliability assessment for shipboard hybrid turbine‐diesel generation system is studied. This work adopts a comprehensive method combining the Markov method and universal generating function (UGF): the Markov method is used for reliability modeling at the unit level, and then synthesized and calculated through UGFs to obtain system‐level reliability. Based on the above methodology, this work presents a multi‐state system (MSS) model with different configuration structures and failure rate types, then compares the availability evaluation results of the single power generation system, the hybrid power system with redundancy and aging effects. The results show that (1) redundant design effectively improves system availability to meet power supply requirements; (2) in the case of system aging and imperfect repair, redundant configuration and power distribution can significantly improve the availability of power supply to be almost unaffected by aging, and effectively reduce the risk of performance failure; and (3) when the redundancy is applied, the system power supply availability is not sensitive in the high‐value range of the redundant installed capacity ratio, but sensitive in the high‐value range of effective coefficient about power compensation control.

Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.

Application of artificial intelligence in early fault detection of transmission line-a case study in India

<span lang="EN-US">Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.</span>

Exploring the Application of CBM+ in an ISR Weapon System, Thermal Observation Device

Purpose: This study suggests a method to apply the condition-based maintenance plus (CBM+) concept, known to improve system availability, by introducing a deep learning model on the thermal observation device (TOD)—representative of an intelligence, surveillance, and reconnaissance (ISR) weapon system.BRMethods: The temperature was chosen as a health index, and the data (normal/abnormal) was acquired by operating the device in a controlled laboratory and from the manufacturer.BRResults: The criteria to classify the abnormal condition were established regarding time and temperature. The following deep learning models were introduced: the convolution neural network (CNN) model for detecting the abnormality and long short-term memory (LSTM) for predicting remaining useful life (RUL). The models proved that the accuracy and loss were fine. Thus, the logic flow of CBM+ for the TOD was suggested by incorporating the models.BRConclusion: The data collected had limitations due to the highly controlled data acquisition. Nevertheless, the possibility of applying the CBM+ logic to similar weapon systems that employ the cooled-type detector could be confirmed.

Condition‐based preventive maintenance with a yield rate threshold for deteriorating repairable systems

AbstractRepairable systems deteriorate with age and usage. In order to maintain acceptable reliability and prevent sudden failures, preventive maintenance (PM) is often applied to such systems. Scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. However, condition‐based PM is considered more relevant to system status than age‐based PM for deteriorating repairable systems. In this study, we consider the production yield rate as the condition variable when determining the optimal PM schedule. A non‐homogeneous Poisson process is used to describe the system deterioration, and the concept of system effective (virtual) age is also considered to make the proposed model more realistic. Three different condition‐based PM strategies are presented to provide more diverse choices for decision makers in terms of solving problems based on the situation at hand. The results show that the optimal yield rate threshold can reduce system failure while maintaining acceptable system availability and product quality with affordable costs due to a proper PM schedule. Furthermore, the results of the numerical application showed that the maintenance and penalty costs are the most sensitive to the total cost, where the adoption of the proposed PM strategies are more profitable when related costs are low.

Towards Integrated Design and Operation of Complex Engineering Systems With Predictive Modeling: State-of-the-Art and Challenges

Abstract Growing trends towards increased complexity and prolonged useful lives of engineering systems present challenges for system designers in accounting for the impacts of post-design activities (e.g., manufacturing, condition monitoring, remaining life prediction, maintenance, service logistics, end-of-life options, etc.) on system performance (e.g., costs, reliability, customer satisfaction, environmental impacts, etc.). It is very difficult to develop accredited lifecycle system performance models because these activities only occur after the system is built and operated. Thus, system design and post-design decision-making have traditionally been addressed separately, leading to suboptimal performance over the systems lifecycle. With significant advances in computational modeling, simulation, sensing & condition monitoring, and machine learning & artificial intelligence, the capability of predictive modeling has grown prominently over the past decade, leading to demonstrated benefits such as improved system availability and reduced operation and maintenance costs. Predictive modeling can bridge system design and post-design stages and provide an optimal pathway for system designers to effectively account for future system operations at the design stage. In order to achieve optimal performance over the system’s lifecycle, post-design decisions and system operating performance can be incorporated into the initial design with the aid of state-of-the-art predictive modeling approaches. Therefore, optimized design and operation decisions can be explored jointly in an enlarged system design space. This article conducted a literature review for the integrated design and operation of engineering systems with predictive modeling, where not only the predictive modeling approaches but also the strategies of integrating predictive models into the system design processes are categorized. Although predictive modeling has been handled from data-driven, statistical, analytical, and empirical aspects, and recent design problems have started to evaluate the lifecycle performance, there are still challenges in the field that require active investigation and exploration. So, in the end, this article provides a summary of the future directions that encourages research collaborations among the various communities interested in the optimal system lifecycle design.

Supporting availability evaluation of a smart building monitoring system aided by fog computing

Internet of Things (IoT) is increasingly present in people's lives, mainly to provide security and well-being. In homes and buildings, IoT sensors can monitor various factors such as temperature and the smoke presence. In case of danger, some action can be activated. However, a sensor network is a complex and fault-prone architecture. Evaluating an IoT architecture in this context is essential. Thus, the objective of this paper is twofold: (i) suggest a continuous time Markov chain (CTMC) model for a smart building monitoring system as a starting point; (ii) based on the results of a sensitivity analysis, an expanded CTMC with improved system availability and shorter outage periods is proposed. The proposed solution reduced the downtime by 98% compared with the baseline architecture. Systems administrators can use our models to evaluate their solutions.

A Decentralized Boltzmann-machine-based fault diagnosis method for sensors of Air Handling Units in HVACs

As a key module in a Heating, Ventilation, and Air Conditioning (HVAC) system, an Air Handling Unit (AHU) is controlled based on information collected by sensors to satisfy human thermal comfort and air quality requirements. Fault diagnosis is critical since it allows maintenance crews to know which faults have occurred to improve system availability. However, fault diagnosis in AHUs is challenging because of the following reasons. First, widely used fault indicators are correlated with changing environments, e.g., weather dynamics or occupants, thus may not be enough to distinguish faults. Second, existing decentralized fault diagnosis methods developed for sensors require solving many optimization problems, leading to high computational requirements. To overcome these challenges, this paper develops a decentralized Boltzmann-machine-based method. To address the first issue, residuals between actual values of several sensor readings and their estimates are considered as fault indicators since they are not related to changing environments. To address the second issue, a novel decentralized voting mechanism is developed based on the convergence characteristic of the Boltzmann machine to locate sensor faults while avoiding solving many optimization problems. However, the established Boltzmann machine usually has an asymmetric weight matrix, and thus it does not converge to the state estimates of sensors. To guarantee convergence, a new symmetrization method is developed to symmetrize the Boltzmann machine by adding an extra unit into the Boltzmann machine to reset the weight matrix while retaining the original voting. Experimental results demonstrate that our method can effectively diagnose sensor faults with high diagnostic accuracy.

FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems.

Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.

Improvements in Medical System Safety Analytics for Authentic Measure of Vital Signs Using Fault-Tolerant Design Approach.

The study presents a novel design method that improves system availability using fault-tolerant features in a non-invasive medical diagnostic system. This approach addresses the effective detection of functional faults, improves the uninterruptible system operating period with reduced false alarms, and provides an authentic measure of vital cardiac signs using diverse multimodal sensing elements like the photoplethysmogram (PPG) and the ECG. Most systems rely on a 1oo1 (one-out-of-one) design method, which inherently limits accuracy in existing practice. In this proposed approach, the quality of segregated authentic vital sign measured values could tremendously benefit the performance of resourceful nursing with negligible alarm fatigue and predict illness more accurately. The system builds upon the selected 2oo2 (two-out-of-two) safety-related design architecture and is evaluated with implemented functions like the fault detection and identification logic, the correlation coefficient-based safety function, and the fault-tolerant safe degradation switching mechanism for accurate measurements. The system was tested on 50 adults of various age groups. The analyzed captured data showed highly accurate vital sign data in this fault-tolerant approach with reduced false alarms. The proposed design method evaluated safety-related mechanisms along with a combination of the same and diverse sensors in a medical monitoring device, showing more reliable functioning of the system and authentic data for better nursing. This design approach showed a 45–55% increased improvement in system availability, thus allowing for accurate and uninterruptable tracking of vital signs for better nursing during critical times in the ICU.

Overview on diagnosability and prognosability for system monitoring

The complexification of systems has brought the emergence of a new field of study: system health monitoring. This field is deemed necessary because it improves system availability and it avoids unnecessary maintenance costs. System health monitoring is performed through diagnosis and prognosis methods. Diagnosis consists in detecting and identifying faults that may lead to system failures. Prognosis is related to the prediction of the system Remaining Useful Life (RUL) that corresponds to the remaining time until the system failure. This paper aims at giving an overview on the properties related to diagnosis and prognosis on different types of systems. We will focus on the diagnosability and prognosability properties. This paper will first briefly present the different types of systems of interest for the system health monitoring community. We will consider Discrete Event Systems (DES), Continuous Systems (CS), Hybrid Systems (HS) or Heterogeneous Systems (HtS). The rest of this paper will present the definitions given in the literature for the concepts of diagnosability and prognosability. The similarities and differences in these definitions for the different types of systems will be highlighted. Some metrics associated with the prognosability property will also be discussed.

Learning-Based Modeling and Optimization for Real-Time System Availability

As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from soft and hard errors rises significantly, resulting in a degraded availability of system. In this article, we investigate the dynamic modeling of cross-layer soft error rate based on the Back Propagation (BP) neural network, and propose optimization strategies for system availability based on Cross Entropy (CE) and Q-learning algorithms. Specifically, the BP neural network is trained using cross-layer simulation data obtained from SPICE simulation while the optimization for system availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. Simulation results show that the CE-based method can improve system availability by up to 32 percent compared to state-of-the-art methods, and the Q-learning-based algorithm can further enhance system availability by up to 20 percent compared to the proposed CE-based method.

CIMA: Compiler-Enforced Resilience Against Memory Safety Attacks in Cyber-Physical Systems

Memory-safety attacks have been one of the most critical threats against computing systems. Although a wide-range of defense techniques have been developed against these attacks, the existing mitigation strategies have several limitations. In particular, most of the existing mitigation approaches are based on aborting or restarting the victim program when a memory-safety attack is detected, thus making the system unavailable. This might not be acceptable in systems with stringent timing constraints, such as cyber-physical systems (CPS), since the system unavailability leaves the control system in an unsafe state. To address this problem, we propose CIMA – a resilient mitigation technique that prevents invalid memory accesses at runtime. CIMA manipulates the compiler-generated control-flow graph to automatically detect and bypass unsafe memory accesses at runtime, thereby mitigating memory-safety attacks along the process. An appealing feature of CIMA is that it significantly improves system availability and resilience of the CPS even under the presence of memory-safety attacks. To this end, we design our experimental setup based on a realistic Secure Water Treatment (SWaT) and Secure Urban Transportation System (SecUTS) testbeds and evaluate the effectiveness and the efficiency of our approach. The experimental results reveal that CIMA handles memory-safety attacks effectively while meeting the real-time constraints and physical-state resiliency of the CPS under test. Using CIMA, we have also discovered a memory-safety vulnerability in the firmware of programmable logic controllers and a CVE ID has already been assigned for it.

Sensing for aerospace combustor health monitoring

Purpose This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing. Design/methodology/approach Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy. Findings Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service. Practical implications The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided. Originality/value The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making.

The internet of things for healthcare: optimising e-health system availability in the fog and cloud

E-health systems can be used to monitor people in real-time, offering a range of multimedia-based health services, at the same time reducing the cost since cheaper devices can be used to compose it. However, any downtime, mainly in the case of critical health services, can result in patient health problems and in the worst case, loss of life. In this paper, we use an interdisciplinary approach combining stochastic models with optimisation algorithms to analyse how failures impact e-health monitoring system availability. We propose surrogate models to estimate the availability of e-health monitoring systems that rely on edge, fog, and cloud infrastructures. Then, we apply a multi-objective optimisation algorithm, NSGA-II, to improve system availability considering component costs as constraint. Results suggest that replacing components with more reliable ones is more effective in improving the availability of an e-health monitoring system than adding more redundant components.

Novelty circular neighboring technique using reactive fault tolerance method

The availability of the data in a distributed system can be increase by implementing fault tolerance mechanism in the system. Reactive method in fault tolerance mechanism deals with restarting the failed services, placing redundant copies of data in multiple nodes across network, in other words data replication and migrating the data for recovery. Even if the idea of data replication is solid, the challenge is to choose the right replication technique that able to provide better data availability as well as consistency that involves read and write operations on the redundant copies. Circular Neighboring Replication (CNR) technique exploits neighboring policy in replicating the data items in the system performs well with regards to lower copies needed to maintain the system availability at the highest. In a performance analysis with existing techniques, results show that CNR improves system availability by average 37% by offering only two replicas needed to maintain data availability and consistency. The study demonstrates the possibility of the proposed technique and the potential of deploying in larger and complex environment.