Service Disruption Research Articles

Navigating the spectrum of human-robot collaboration: Addressing robophobia-robophilia in the hospitality industry

Analyzing human-robot collaboration, this study draws on insights from cognitive science and psychology, with a particular focus on developing strategies for instructing robots and addressing the dynamics of robophobia and robophilia. Robophilia signifies the evolving, positive human-robot relationship, emphasizing the attribution of personhood to robots, while robophobia represents fear of robots. Although hospitality studies have explored anthropomorphism in robots, there is a gap in understanding robophobia impact in this context. This research investigates the transition from robophilia to robophobia and its implications for human-robot interactions in hospitality. Past studies primarily emphasized positive aspects, like anthropomorphism and robot personhood neglecting potential negatives such as robophobia. Guided by analogical transfer theory, fear acquisition theory, and uncanny valley theory using three experimental studies, this research explore factors driving this transition, its impact on embarrassment, customer outrage, and employee sabotage intentions during joint robot interactions. This research enriches our understanding of complex human-robot dynamics, shedding light on robophobia's mediating role within the context of service disruptions.

Vulnerabilities to disasters in healthcare facilities in the face of the COVID-19 pandemic: a scoping review.

This article maps the structural, nonstructural and functional vulnerabilities of healthcare facilities to the COVID-19 pandemic. It reports on a scoping review guided by JBI recommendations and structured by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The PubMed, CINAHL, LILACS, EMBASE, SciELO, Scopus and Web of Science Repositories and databases were consulted, as was the grey literature. The protocol was registered in the Open Science Framework. The 54 studies included summarised 36 vulnerabilities in three categories in 29 countries. Functional and non-structural vulnerabilities were the most recurrent. Limited material and human resources, service disruption, non-COVID procedures and inadequate training were the items with most impact. COVID-19 exposed nations to the need to strengthen health systems to ensure their resilience in future health crises. Prospective risk management and systematic analysis of health facility vulnerabilities are necessary to ensure greater safety, sustainability and improved standards of preparedness and response to events of this nature.

An empirical study of reflection attacks using NetFlow data

Reflection attacks are one of the most intimidating threats organizations face. A reflection attack is a special type of distributed denial-of-service attack that amplifies the amount of malicious traffic by using reflectors and hides the identity of the attacker. Reflection attacks are known to be one of the most common causes of service disruption in large networks. Large networks perform extensive logging of NetFlow data, and parsing this data is an advocated basis for identifying network attacks. We conduct a comprehensive analysis of NetFlow data containing 1.7 billion NetFlow records and identified reflection attacks on the network time protocol (NTP) and NetBIOS servers. We set up three regression models including the Ridge, Elastic Net and LASSO. To the best of our knowledge, there is no work that studied different regression models to understand patterns of reflection attacks in a large network. In this paper, we (a) propose an approach for identifying correlations of reflection attacks, and (b) evaluate the three regression models on real NetFlow data. Our results show that (a) reflection attacks on the NTP servers are not correlated, (b) reflection attacks on the NetBIOS servers are not correlated, (c) the traffic generated by those reflection attacks did not overwhelm the NTP and NetBIOS servers, and (d) the dwell times of reflection attacks on the NTP and NetBIOS servers are too small for predicting reflection attacks on these servers. Our work on reflection attacks identification highlights recommendations that could facilitate better handling of reflection attacks in large networks.

Transmission Outage Cost Analysis Using Value of Loss Load Approach Based on Macro Economic Data

The West Nusa Tenggara (NTB) electricity system, which consists of the Lombok System and the Sumbawa-Bima System, has an important role in supporting the country's economy, especially in the tourism segment. Quantitatively, there has been no measurement of the impact of electricity disruption on the macro economy. Value of Loss Load (VoLL) is a useful quantity parameter in the economic evaluation of electric power systems. It can be represented as the value of losses borne by customers in case of electricity service interruptions. For policy makers and electricity management, the size of the VoLL would affect decisions regarding investment. A low VoLL requires for a low reliability level and a high VoLL for a high reliability level. This research will calculate Transmission outage costs using the Value of Loss Load approach based on macro economic data and predicting VoLL 2024 - 2030. The outcome of the research shows that The Lombok System VoLL is lower than Bima – Sumbawa System. Outage costs due to disruptions on the Transmission System side affect GDP by 0.001% / year. The trend of VoLL 2024 – 2030 is estimated to decrease by an average of 2.29% / year which is indicate it is is inline with Rencana Usaha Penyediaan Tenaga Listrik 2021 - 2030.

Analyzing Power Systems and Power Electronics Interdependencies: A DEMATEL Method Approach

Energy research encompasses all aspects of electrical energy, focusing on innovation in energy production and delivery, alternative resources, and efficient devices. It involves studying systems and equipment for converting, providing, and utilizing energy as electricity. Power electronics have become essential to power systems, enhancing quality and efficiency and promoting the development of intelligent, efficient energy solutions. There are various types of power electronics within power systems. The architectural study of converting electrical energy from one form to another falls under power electronics. Globally, electronics recycle or recover more than 80% of the total electricity produced, averaging 3.4 billion kilowatt-hours annually. Power electronics converters, also known as power converters or switching converters, are used to process or convert electrical energy. Electricity exists as AC power and DC power, leading to the classification of distribution systems into AC and DC based on the type of power used. Power system analysis is crucial for designing electrical power systems. It involves calculations and simulations to ensure that the electrical system and its components are appropriately specified to function as intended, endure expected stress, and be protected from failures. Advantages of power electronics include high power density electricity and improved efficiency, reaching up to 99% in energy conversion. Their efficiency and reliability make switching power supplies suitable for medical devices with acoustically sensitive applications. Power system reliability addresses issues like service interruptions and power outages, often guided by specific codes relevant to consumers. Common reliability indices in the US include SAIFI, SAIDI, and CAIDI. The DEMATEL (Decision Making Trial and Evaluation Laboratory) method is applied across various industries such as non-metal mineral products, general equipment manufacturing, coal mining and washing, textiles, and food manufacturing. DEMATEL visualizes and assesses the interactions and dependent relationships between factors through a structural model, identifying critical elements. Evaluation parameters for power electronics include their application in power systems, transportation systems, energy conservation, heating and lighting control, and renewable energy integration. Power electronics in power systems are ranked highest, while energy conservation is ranked lowest.

Quick service during DDoS attacks in the container-based cloud environment

Distributed Denial of Service (DDoS) attacks are one of the biggest internet security risks. As DDoS attacks directly target the availability of the victim’s services, defending against them is one of the most challenging tasks for the victim organization’s cyber security personnel. Service disruptions, resource conflicts, reputation harm, and financial losses are just a few of the many direct and indirect effects of DDoS attacks. There are several DDoS attack solutions, however, victim organization(s) cyber security personnel still struggle to mitigate it. Among the several ways of mitigating DDoS attacks, it was observed that the most effective approaches include bandwidth limiting and resource reservations. Individually, these solutions have a drawback of increasing the average response time during a DDoS attack for both the malicious and the benign users. We propose a two-line defense system that combines both of these strategies in addition to containerization to minimize DDoS attack effects. First line defense system separates incoming requests based upon the number of connections made for target web-service at any particular instant and sends them to second line defense system. At second line defense system, we limit the bandwidth of intermediate machines that transmit requests to the destination web-service(s). Additionally, the load balancer at the second line defense system routes incoming requests to different containers on the target computers based on line defense system decisions. By segregating incoming requests, we are able to serve benign users requests which send lesser number of requests than threshold value even in the presence of DDoS attacks. The proposed technique shows that benign users’ average response time under DDoS attack is nearly equivalent to that obtained under normal network conditions. The proposed technique is able to preserve the service availability to ∼98% of benign requests even in the presence of massive DDoS attack.

Impact of COVID-19 pandemic on routine childhood vaccinations

Routine pediatric vaccination is one of the most effective public health inter-ventions for the control of a number of fatal diseases. However, during the coronavirus disease 2019 pandemic, routine pediatric vaccination rates were severely affected by disruptions of health services and vaccine confidence issues. Governments and the United Nations have taken measures to re-establish routine pediatric vaccination, while additional efforts are needed to catch up and develop plans to ensure routine vaccination services for the future pandemics.

Railway Infrastructure Cybersecurity: An Overview

The railway infrastructure constitutes a type of operational technology (OT)-based critical infrastructure, which is expected to work 24x7, 365 days a year, and where the life expectancy of operational equipment often exceeds 30 years. In this domain, an operational anomaly compromising the OT system can cause a train accident or interrupt traffic, with potentially significant impact in terms of business as well as for passenger safety. Due to their relevance, railways are strategic assets of national interest and, consequently, targets of interest for cybercriminals and cyberwarfare activities. For instance, service interruptions may trigger ripple effects resulting in product shortages and widespread supply chain disruptions, with severe impacts for both the economy and national security. In a bid to optimise and streamline operations. the railway industry has recently started taking a series of significant steps towards digitization, with infrastructures experiencing a significant paradigm shift which, for instance, makes it possible to have centralised interlockings and Radio Block Centre (RBC) for an entire country, with geographical redundancy, ensuring the utmost availability and punctuality by moving the control logic to the cloud. Nevertheless, these developments must always be carried on within the scope of established cybersecurity standards and frameworks. This paper presents an analysis of the state of the art on railway cybersecurity, focused on the existing solutions based on the application of the CENELEC “Technical specification 50701 - Railway Application – Cybersecurity”, which is currently the latest European specification addressing railways, being designed to help suppliers, integrators, and operators to implement a cybersecurity risk assessment plan, the necessary controls, and the management of the complete system life cycle. Special attention will be paid to the conduit between the rail signal interlocking system, that controls the line signalling, and the Automatic Train Supervision (ATS) that runs in the Operational Control Centre (OCC), as this has been identified by the European Union Agency for Cybersecurity (ENISA) as one of the most critical systems identified by the operators of essential services.

Botnets in Healthcare: Threats, Vulnerabilities, and Mitigation Strategies

The increasing digitization of healthcare systems has introduced new opportunities to improve efficiency and accessibility for medical professionals and patients. Examples include the simplified collection, storage, and organization of patient data using electronic health records (EHRs), the use of teleconferencing software like Zoom to allow patients to meet with their care providers remotely, and medical IoT devices like glucose monitors, pacemakers, and other remote patient monitoring devices that leverage software and the internet to provide patients and their healthcare providers with critical information. All of these use cases are examples of how technology can increase the quality of patient care. While the healthcare industry has realized many benefits from its increased investment in new technology, trends have shown that this increased utilization has also opened avenues for malicious cyber actors. One of these threats is botnets. These malicious networks of compromised computers, controlled by cybercriminals, can wreak havoc on all sectors of society, with the healthcare industry proving to be a desirable target. This research is a high-level analysis that investigates the threat botnets pose by employing an exploratory review. We identify the multifaceted nature of botnet threats in healthcare, analyzing their standard forms and the vulnerabilities inherent in healthcare infrastructures, ranging from outdated software to inadequate cybersecurity protocols to poor or total lack of security awareness training for staff. Moreover, the various techniques botnets use to propagate are explored to elucidate the potential points of exploitation and the damage they can cause organizations when proper controls are not implemented. These negative consequences include data breaches, service disruptions, and compromised patient confidentiality, which can endanger medical staff and patients if not addressed. This paper then discusses proven mitigation strategies such as end-user awareness, traffic monitoring, and detection response tools that organizations can employ to reduce the potential and efficacy of such threats. The threat landscape will continue to evolve; however, by staying on top of the latest trends, we can ensure the security of such critical infrastructure and save lives.

Social Determinants in COVID-19 Experiences of Children With Disabilities Receiving School-Based Services in Chicago: Mixed-Methods Study of Parent/Guardian Perspectives.

To identify the impact of social determinants on the experiences of children with disabilities and their families during the COVID-19 pandemic from the perspective of parents/guardians. A mixed-methods study engaged parents/guardians of children with Individualized Education Programs (IEPs) in July to August 2021 at a developmental/behavioral pediatrics clinic in 1 urban academic medical center. All parents/guardians completed study-specific surveys on experiences and impact of COVID-19. A subset completed semi-structured interviews. Analysis included descriptive statistics and Fisher exact tests for survey questions and thematic analysis to code interviews and identify themes. Results were corroborated by experts in developmental/behavioral pediatrics and special education. Participants included 24 parents/guardians representing 27 children (mean = 7.37 years). A majority attended public school (78%) and identified as non-White (78%). Most commonly, the children's disabilities were autism (52%), attention-deficit hyperactivity disorder (37%), and speech/language impairment (33%). The services received by children most commonly were speech/language (89%) and physical/occupational (70%) therapies. Five themes emerged about the impact of social determinants on experiences during COVID-19 related to: adapting to disruption of routines, attendance/engagement in learning, interruption of IEP services, support for children and families, and challenges with technology. Social determinants, such as housing, income, insurance, and quality of education, affected the experiences of families and their ability to adapt to the needs of children with disabilities in the setting of COVID-19 pandemic-related changes.

Bayesian estimation of the mean time between failures of subsystems with different causes using interval‐censored system maintenance data

AbstractEnsuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level.

Structural violence and necropolitics among Indigenous Peoples living with HIV who use substances in the Prairie provinces during COVID-19

BackgroundWithin Manitoba and Saskatchewan, pre-existing health inequities amongst Indigenous groups were intensified during the COVID-19 pandemic. Service disruptions in the health and social service sector—combined with the effects of intersectional stigma—disproportionately impacted Indigenous peoples living with HIV (IPLH). IPLH experience structural violence and necropolitical exclusion through systemic forms of stigma situated within Canada's expansive colonial history. Utilizing the theoretical foundations of structural violence and necropolitics, this qualitative study examines how the COVID-19 pandemic amplified preceding states of inequity for IPLH. MethodsSemi-structured interviews were conducted with 60 participants. The sample comprised of those with lived experience (n = 45) as well as those who provided services for IPLH (n = 15). Indigenous Storywork guided the data collection and analysis process. Topics explored within each interview included access to health and social services, harm reduction, substance use, and experiences in providing services during COVID-19 pandemic. Thematic analysis was used to identify common themes throughout each story. ResultsOur results indicate that the COVID-19 pandemic exposed and amplified pre-existing forms of structural violence and necropolitical logics for IPLH within Manitoba and Saskatchewan. Specifically, we describe how structural violence and necropolitics are manifested via three main avenues— (i) restrictions and removal of care, (ii) bureaucracy and institutional care politics, and (iii) discrimination and systemic racism within the Canadian healthcare system. ConclusionThe COVID-19 pandemic within Manitoba and Saskatchewan sparked massive changes in service provision within settler-colonial and neoliberal institutions of care. For those services that remained open to IPLH, masking requirements, questionnaire requirements, scheduling requirements, and a lack of in-person services acted as only some of the barriers described by community members as detrimental to care access. Increased experiences of discrimination in health care on the basis of substance use or HIV status further limited access to needed services.

Assessing the Vulnerabilities: Cybersecurity Challenges in Power System Infrastructure in Nigeria

This study examined the Cyber security Challenges in Power System Infrastructure in Nigeria. Four research questions guided the study which employed a survey research design. The population of the study was 46 respondents comprising 23 Technical Units Heads and 23 Heads of Cyber security Units from the (23) power-generating plants in Nigeria. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Cyber security Challenges in Power System Infrastructure Questionnaire (CCPSIQ) developed by the researchers. A reliability index of 0.89 was obtained using the Cronbach Alpha reliability method. The mean statistic was used to answer the research questions. The findings of the study revealed that the primary cyber threats targeting power system infrastructure in Nigeria including network-based attacks, insider threats, supply chain vulnerabilities, advanced persistent threats (APTs), physical attacks, zero-day exploits, data breaches, and ransom ware/extortion; it further reveal that cyber-attacks on power grid infrastructure in Nigeria lead to significant service disruptions, financial losses, equipment damage, safety compromises, erosion of customer confidence, regulatory non-compliance, increased vulnerability to future incidents, reputation damage, and prolonged recovery periods, underscoring the multifaceted impact of cyber threats on operational efficiency. The study recommended that: Collaboration among stakeholders, including government agencies, industry partners, and cyber security experts, is paramount to effectively address emerging threats and enhance cyber security resilience. By implementing proactive measures and fostering a culture of cyber security awareness, Nigeria can strengthen its power system infrastructure against cyber threats, safeguarding the reliability and security of critical services for its citizens.

A scalable optimization approach to the intervention planning of complex interconnected infrastructures

The functioning of infrastructure networks is vital for modern communities. Maintenance should be planned to ensure infrastructure's functionality and safety at the lowest cost. Interconnected infrastructure networks can affect each other's functionality, and maintenance on one network can impact the serviceability of others. Planned intervention grouping across infrastructures reduces set-up costs and service interruption, improving infrastructure availability and serviceability at lower costs.Finding the best grouping strategy is a known NP-hard problem, with several optimization strategies have been proposed, mainly based on nonlinear models which are computationally expensive and do not guarantee scalability. Furthermore, infrastructure intervention planning models mostly focus on grouping of interventions which are considered as given. In this paper, we propose a new efficient optimization model to optimize intervention grouping for interconnected infrastructure networks. We develop a scalable two-step optimization model where we first plan each individual intervention type based on a preventive maintenance policy accounting for the degradation behavior of objects, then group interventions to minimize the net costs, considering dependencies within and accross infrastructure networks.We formulate the grouping problem as an Integer Linear Program, which can be solved exactly with standard solvers. The model accounts for interactions between infrastructure networks and considers the impact on all stakeholders. It also accommodates various intervention types like maintenance, removal, and upgrading.Using a demonstrative application, we show that our model significantly reduces net costs and outperforms alternative nonlinear formulations and related heuristics in terms of both solution quality and computation performance. Additionally, the optimal intervention plan shows repetitive patterns, which suggests that a rolling horizon strategy could be used where the optimization problem is solved for shorter time horizons, leading to significant computational benefits.

Securing the green grid: A data anomaly detection method for mitigating cyberattacks on smart meter measurements

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid.

Improving Incident Management Processes with Feature Models

A cybersecurity incident is any event that directly or indirectly affects the confidentiality, availability, or integrity of a system or a service (or its data). The aim of a cyber-incident management process is to restore normal service levels as quickly as possible, by mitigating or eliminating the effects of system service disruptions. During the different phases of a cyber-incident management process, the documentation can be confusing and difficult to comprehend, making it ineffective. This paper aims to improve cyber-incident management processes that already exist by introducing feature models in order to handle incident documentation, classification, prioritisation, and mitigation. An example of an improved cyber-incident process is evaluated with respect to its efficiency and effectiveness, by conducting two case studies. The results of this work reveal that the improved process increases efficiency in addressing and repairing cyber-incidents by reducing the incident response time.

A time series analysis of disruptions to maternal and child health care in northern Togo during the COVID-19 pandemic in the context of an integrated primary care program

There are significant concerns that COVID-19-related disruptions in routine health care will reverse the gains made over the past 2 decades in reducing maternal and under-5 mortality in low- and middle-income countries. The objective of this study was to examine the extent of disruptions in community-based maternal and child health services and explore community perceptions and experiences with health care use in the year following the start of the COVID-19 pandemic in one district in northern Togo, West Africa. This study included analysis of multiple types of data including: (1) routine health management information system data from 18 public sector health centers, (2) programmatic data collected through an integrated health center- and community-based primary care program at 5 health centers, and (3) semi-structured interviews with health center managers, community health workers (CHWs), and community members at those 5 health centers. We found only short-term declines in service utilization coinciding with the COVID-19 pandemic and no decline in the delivery of community-based care by CHWs. Qualitative data were consistent with the quantitative results, reporting sustained use of health-care services. Multilevel factors related to the continued provision of care during the COVID-19 pandemic included regular and clear communication from CHWs as trusted community sources, risk minimization at the health-center level, continued provision of community-based care by CHWs, and collaboration between community leaders and health-care workers to limit COVID-19 transmission. Findings demonstrate resiliency of an integrated primary care system when equipped with an adequately trained, supervised, and supplied health workforce, implementation of infection prevention and control measures, communication by trusted community sources, and adaptations to health-care delivery that enable the continued provision of care.

Food and water insecurity in households of children and adolescents living with HIV and receiving care in a rural Zambian hospital: A mixed-methods study.

Approximately 62,000 Zambian children are living with HIV. HIV care and treatment is generally more limited in rural areas, where a heavy reliance on rain-fed subsistence agriculture also places households at risk of food and water insecurity. We nested a mixed methods study with an explanatory sequential design in a clinical cohort of children and adolescents living with HIV (CHIV) in rural Zambia. We used validated questionnaires to assess household food and water insecurity and examined associations between indicators derived from those scales, household characteristics, and HIV treatment adherence and outcomes using log-binomial regression. We identified caregivers and older CHIV from food insecure households for in-depth interviews. Of 186 participants completing assessments, 72% lived in moderately or severely food insecure households and 2% in water insecure households. Food insecurity was more prevalent in households of lower socioeconomic status (80% vs. 59% for higher scores; p = 0.02) and where caregivers had completed primary (79%) vs. secondary school or higher (62%; p = 0.01). No other characteristics or outcomes were associated with food insecurity. Parents limited both the quality and quantity of foods they consumed to ensure food availability for their CHIV. Coping strategies included taking on piecework or gathering wild foods; livestock ownership was a potential buffer. Accessing sufficient clean water was less of a concern. During periods of drought or service interruption, participants travelled further for drinking water and accessed water for other purposes from alternative sources or reduced water use. Community contributions afforded some protection against service interruptions. Overall, while food insecurity was prevalent, strategies used by parents may have protected children from a measurable impact on HIV care or treatment outcomes. Reinforcing social protection programs by integrating livestock ownership and strengthening water infrastructure may further protect CHIV in the case of more extreme food or water system shocks.

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

In recent years, the distribution network in Chile has undergone various modifications to meet new demands and integrate new technologies. However, these improvements often do not last as long as expected due to inaccurate forecasting, resulting in frequent equipment changes and service interruptions. These issues affect project investment, unsold energy, and penalties for poor quality of supply. Understanding the electricity market, especially in distribution, is crucial and requires linking technical quality standards with service quality factors, such as the frequency and duration of interruptions, to understand their impact on regulated distribution to customers. In this context, a comparative study will be carried out between Long Short-Term Memory (LSTM) and transformer architectures, with the aim of improving the sizing of distribution transformers and preventing failures when determining the nominal power of the transformer to be installed. Variables such as voltages and operating currents of transformers installed between 2020 and 2021 in the Valparaíso region, Chile, along with the type and number of connected customers, maximum and minimum temperatures of the sectors of interest, and seasonality considerations will be used. The compilation of previous studies and the identification of key variables will help to propose solutions based on error percentages to optimise the accuracy of transformer sizing.