Repair Actions Research Articles

Implementing Self-Healing N-Policy Queueing Models and their Impact on IoT Design Applications

We embarked on a comprehensive exploration of a single server queueing design, with a specific focus on handling soft failures. Soft failures refer to instances where customers do not need to be removed but rather need to wait for the server to be reactivated. These occurrences can happen at any time during the server's operation. When a soft failure occurs, the process automatically initiates a repair action, which we will refer to as the self-healing time. This self-healing time is relatively short, as the server possesses a remarkable restoration capability. Once the repair is complete, the server resumes its service provision and resumes normal operations. Moreover, during periods of prolonged idleness, the server can enter a dormant state, akin to a vacation mode. This dormant state is triggered when the server awaits the accumulation of N or more users. Once the threshold is reached, the server transitions into a busy state and resumes its normal operations This study represents the pioneering integration of soft failures with the N policy, marking the first of its kind in this field. Additionally, we provide explicit expressions for the transient probabilities of the model, employing generating function methodology and Laplace transform techniques. Furthermore, we include performance measures and a numerical component to underscore the significance of the model's parameters.

Using Large Language Models to Recommend Repair Actions for Offshore Wind Maintenance

Abstract The Offshore Wind (OSW) industry is experiencing significant expansion, resulting in increased Operations & Maintenance (O&M) costs. Intelligent alarm systems offer the prospect of swift detection of component failures and process anomalies, enabling timely and precise interventions that could yield reductions in resource expenditure, as well as scheduled and unscheduled downtime. This paper introduces an innovative approach to tackle this challenge by capitalising on Large Language Models (LLMs). We present a specialised conversational agent that incorporates statistical techniques to calculate distances between sentences for the detection and filtering of hallucinations and unsafe output. This potentially enables improved interpretation of alarm sequences and the generation of safer repair action recommendations by the agent. Preliminary findings are presented with the approach applied to ChatGPT-4 generated test sentences. The limitation of using ChatGPT-4 and the potential for enhancement of this agent through re-training with specialised OSW datasets are discussed.

GB-SAR geocoding considering the radar attitude inclination angles for building facade deformation extraction

ABSTRACT The process of engineering construction typically impacts the nearby existing buildings, causing either localized or widespread deformation of these structures. The application of deformation monitoring technology enables the timely identification of damage or uneven deformation, thereby facilitating the implementation of appropriate maintenance and repair actions. This paper explores the application of ground-based synthetic aperture radar (GB-SAR) in building facade deformation monitoring. GB-SAR acquires two-dimensional (2D) images and measures deformation along the line of sight (LOS) direction through interference calculation. In order to accurately determine the location of deformation and retrieve the elevation information of image pixels, it is usually required to utilize external three-dimensional (3D) data to reconstruct the 3D coordinate of these pixels, which is known as geocoding, or georeferencing. In the case of the GB-SAR system, it is necessary for the observer to measure the angles between the antenna orientation, the radar movement direction and the reference plane of the local surveying coordinate system. Nevertheless, the measurement of these attitude angles may be overlooked in practice, and the impact of small attitude angles or their measurement errors on GB-SAR geocoding may be disregarded, which directly affects the slant range projection (SRP) calculation of the external 3D data, thereby leading to biased 3D coordinate reconstruction of GB-SAR pixels. This paper proposes a method to calculate the SRP coordinates of point cloud data considering the attitude inclination angles of radar sensor, and perform matching calculations on the high-quality pixels (HQPs) with the point cloud after SRP to extract building facade deformation. The results demonstrate that the method is capable of effectively mitigating the influence of the radar attitude inclination angles, resulting in a reduction of the average deviation of 3D coordinates to approximately one-third of the value without consideration of the sensor’s attitude in a real-world building monitoring experiment.

Reliability-aware techno-economic assessment of floating solar power systems

Floating solar photovoltaic systems (FPV) have emerged as a promising technology to harness solar energy on water surfaces. With its numerous benefits, including increased land availability, reduced water evaporation, and improved system cooling, FPV systems hold great potential for sustainable energy generation. However, due to its unique installation and operation in water bodies, the management of aging becomes a critical factor to ensure long-term success. Consequently, reliability analysis plays a pivotal role in predicting and mitigating operational risks and estimating the economic feasibility of FPV projects. In this context, this paper presents a reliability-aware techno-economic assessment approach of FPV systems. The approach is tested with a case study in India, and the results are compared with ground-based photovoltaic (GPV) systems. Here, different failure and repair strategies are taken into account to determine the lifetime performance. Results showed that even though FPV system has higher failure rate, considering standard maintenance, the energy generated by the FPV system is 5.38% higher than similar GPV system. The cost of electricity by the PV system depends on the repair and maintenance. For normal maintenance the levelized cost of electricity (LCOE) for FPV system is calculated as 0.0551 $/kWh which is comparable to the LCOE by GPV systems, while for reduced repair actions, the LCOE of the FPV is higher than the LCOE of the GPV system.

Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company

Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions for categorising them, extracting specific information about failure, or supporting failure analysis methodologies (such as FMEA). Whereas, the analysis of repair actions and its relationship with failure remains underexplored. Addressing this gap, our study makes three significant contributions. Firstly, we focused on the Italian language, which presents additional challenges due to the dominance of NLP systems that are mainly designed for English. Secondly, it proposes a method for automatically subdividing a repair action into a set of sub-tasks. Lastly, it introduces an approach that employs association rule mining to recommend sub-tasks to maintainers when addressing failures. We tested our approach with a case study from an automotive company in Italy. The case study provides insights into the current barriers faced by NLP applications in maintenance, offering a glimpse into the future opportunities for smart maintenance systems.

Holistic life cycle cost analysis of road bridges with non-metallic reinforcement

Corrosion-related damage due to exposure to environmental conditions is the main cause of costs in the maintenance of transport infrastructure. Because of its high corrosion resistance and the associated higher durability, non-metallic reinforcement offers great potential for preventing such damage, thus reducing maintenance costs. In this article, potential savings for an integral road bridge are studied through a holistic life cycle cost (LCC) analysis considering four different reinforcement materials (steel, glass, basalt, carbon). A fair comparison is enabled by a material-specific design, the calculation of maintenance and user costs and the consideration of the respective disposal scenario. Moreover, environmental costs are recognised by carbon pricing based on life cycle analysis (LCA). The influence of individual parameters is quantified by means of a sensitivity analysis and the probability of savings is studied by Monte Carlo simulation. It is shown that higher investment costs for non-metallic reinforcement can be compensated by lower user costs. This is mainly due to shorter maintenance periods, as less time is required for repair action, whereby potential savings in user costs are particularly evident if the traffic route below the bridge is not disrupted. It is concluded that even from today's perspective, the use of glass and basalt fibre-reinforced polymer (FRP) reinforcement in highway bridges with average traffic volumes very likely offers an economic advantage over corrosion-prone reinforcing steel.

A condition-based maintenance policy for a two-component balanced system with dependent degradation processes

Preventive maintenance of balanced systems has garnered increasing research attention due to its significance in production and servicing processes. This paper investigates the optimization problem of a condition-based maintenance policy for a two-component balanced system with dependent degradation processes. The system’s degradation follows a bivariate gamma process. Once the degradation level of one component exceeds a critical value, the system fails and triggers an immediate corrective replacement. Moreover, if the degradation difference between two components exceeds a threshold, the system is in an unbalanced state, which incurs a penalty cost. Periodic inspections are implemented to reveal the degradation of the system. At each inspection epoch, the decision-maker chooses an action among do-nothing, repair, and preventive replacement. Different from previous research, a repair action does not reduce the degradation levels of either component but increases the degradation level of the lower one to rebalance the system. The objective is to determine the optimal condition-based maintenance policy that minimizes the long-run average cost rate. The optimization problem is analyzed in a semi-Markov decision process framework. A policy-iteration dynamic programming algorithm is developed to derive the optimal policy. A numerical example is presented to illustrate the effectiveness of the proposed approach.

An asset management modelling framework for wind turbine blades considering monitoring system reliability

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

Towards equitable infrastructure asset management: Scour maintenance strategy for aging bridge systems in flood-prone zones using deep reinforcement learning

Bridges play a critical role in transportation networks; however, they are vulnerable to deterioration, aging, and degradation, especially in the face of climate change and extreme weather events such as floodings. Furthermore, bridges can significantly affect social vulnerability; their damage or destruction can isolate communities, inhibit emergency responses, and disrupt essential services. Maintaining critical bridges in a cost-effective and sustainable manner is crucial to ensure their longevity and protect vulnerable communities. To address the maintenance optimization problem of bridge systems considering the effects of time deterioration, flood degradation, and social vulnerability, this study proposes a deep reinforcement learning algorithm to optimally allocate resources to bridges that are at expected cost of failure due to scour. The algorithm considers the effects of flood degradation with different return periods and is trained using a Markov Decision Process as the environment. The study conducts four flood simulation scenarios using Geographic Information System data. The findings suggest that the deep reinforcement learning algorithm proposes a sequence of repair actions that outperforms the status quo, currently employed by bridge managers. The significance of this study lies in its valuable insights for cities worldwide on how to effectively optimize their limited resources for the maintenance and rehabilitation of critical infrastructure systems to decrease portfolio cost and increase social equity.

Corrective maintenance minimizing expected mission losses in shock exposed multistate production-storage systems

Production-storage systems are often subject to random external shocks such as storm weather and electromagnetic interference. However, most of the existing models failed to take the shock effects into consideration. This paper extends the state of the art by modeling random shocks and optimizing their mitigating corrective maintenance policy (CMP) that defines the condition triggering repair actions to restore the production subsystem to a higher performance level. The CMP can greatly impact the amount of unsupplied product as well as the maintenance and operation costs, all contributing to the expected mission losses (EML). This paper contributes by modeling and optimizing the CMP to minimize the EML. The proposed solution encompasses a new numerical algorithm that evaluates the EML of the considered production-storage system under a given CMP and the genetic algorithm realized for solving the CMP optimization problem. The proposed model is demonstrated using a case study of a multistate power generation system subject to voltage surges due to storm weather. Based on the investigation of effects of several model parameters (shock rate, storage capacity, initial amount of product in the storage, cost of unsupplied product) on the EML and optimization solutions, useful and insightful management recommendations are provided.

DRL-based progressive recovery for quantum-key-distribution networks

With progressive network recovery, operators restore network connectivity after massive failures along multiple stages, by identifying the optimal sequence of repair actions to maximize carried live traffic. Motivated by the initial deployments of quantum-key-distribution (QKD) over optical networks appearing in several locations worldwide, in this work we model and solve the progressive QKD network recovery (PQNR) problem in QKD networks to accelerate the recovery after failures. We formulate an integer linear programming (ILP) model to optimize the achievable accumulative key rates during recovery for four different QKD network architectures, considering different capabilities of using trusted relay and optical bypass. Due to the computational limitations of the ILP model, we propose a deep reinforcement learning (DRL) algorithm based on a twin delayed deep deterministic policy gradients (TD3) framework to solve the PQNR problem for large-scale topologies. Simulation results show that our proposed algorithm approaches well compared to the optimal solution and outperforms several baseline algorithms. Moreover, using optical bypass jointly with trusted relay can improve the performance in terms of the key rate by 14% and 18% compared to the cases where only optical bypass and only trusted relay are applied, respectively.

A Comprehensive Review of the Role of Stem Cells in Neuroregeneration: Potential Therapies for Neurological Disorders.

Stem cell research has emerged as a groundbreaking field with significant potential for advancing neuroregeneration and neurological disorder treatment. Neurological conditions such as Alzheimer's disease, Parkinson's disease, stroke, and spinal cord injuries pose severe challenges due to their impact on quality of life and the limited efficacy of current treatments, which primarily focus on symptom management rather than addressing the underlying damage. Neuroregeneration, the process of repairing and restoring damaged neural tissues, is crucial for improving patient outcomes, given the central nervous system's limited intrinsic repair capacity. Stem cells offer a promising solution due to their ability to self-renew and differentiate into various neural cell types, providing opportunities for innovative therapies. This review provides a comprehensive analysis of the role of stem cells in neuroregeneration, exploring different types of stem cells, including embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and adult stem cells, and their mechanisms of action in neural repair. It examines current clinical trials and translational research efforts, highlighting successes and ongoing challenges such as ethical considerations, immunogenicity, and technical limitations. The review also discusses future directions in stem cell research, including advancements in gene editing, tissue engineering, and personalized medicine. By addressing these aspects, the review aims to offer a thorough understanding of the potential and challenges of stem cell-based therapies, contributing to the development of effective treatments for neurological disorders and ultimately enhancing patient quality of life.

Multi-hazard life-cycle consequence analysis of deteriorating engineering systems

Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.

OSTEOINDUCTIVE CAPACITY OF PLATELET-RICH FIBRIN VS BIODENTINE FOR MANDIBLE FRACTURE

Highlights Biodentine has undergone thorough research as a bone grafting substance due to its ability to promote bone regeneration and effectively treat root or tooth fractures. PRF and Biodentine can stimulate osteogenesis, affecting bone repair, particularly in mandibular fractures. Abstract Background: Mandibular fracture is one of the most common fractures. The most common treatment for mandibular fractures is fixation. Therefore, xenogeneic agents such as platelet-rich fibrin (PRF) and Biodentine accelerate the reparative process. Biodentine is an interesting active ingredient that can induce bone regeneration. PRF and Biodentine can promote bone healing, but no literature discusses the differences between PRF and Biodentine osteoinduction mechanisms in treating mandibular fractures. Objective: This article aimed to compare the effect of osteoinductive PRF with Biodentine for mandibular fractures. Material and Method: The research was conducted as a scoping review by performing a thorough search of the PubMed, Scopus, Science Direct, Elsevier, and Google Scholar databases. The study was obtained based on literature studies in the form of journals and textbooks in the last ten years (2013-2023). Result: The osteoinductive effect and mechanism of Biodentine in enhancing bone repair are likely correlated with releasing biologically active ions from calcium silicate cement and stimulating gene expression Runx-2. PRF has an osteoinductive role, causing the mechanism of releasing growth factors such as PDGF, VEGF, TGF-β, and IGF that promote the osteogenic process. Conclusion: There was no significant difference in the osteoinduction effect of PRF and Biodentine because these materials have different mechanisms of action for bone repair.

BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert users (IT staff and data scientists), there is a lack of studies from the perspective of non-expert ones. Given a set of available data quality rules, we exploit machine learning techniques to guide the user to identify the dirty values for each violation and repair them. We show that with a low user effort, it is possible to identify the values in tuples that can be trusted and the ones that are most likely errors. We show experimentally how this machine learning approach leads to a unique clean solution with high quality in scenarios where other approaches fail.

A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete

Self-healing cementitious materials show potential to reduce material consumption, maintenance costs, and environmental impacts within the construction sector. This study explores the feasibility of installing a ductile-porous vascular network in a series of retaining walls under realistic construction conditions, with the objective of both assessing the efficacy of the self-healing system and addressing any constructability issues that may arise. Numerical modelling was performed first to determine a suitable mix design and wall configuration that would promote cracking, so cracks would appear without mechanical intervention. The predicted crack distribution informed the optimal network configuration. Healing and sustainability considerations are discussed, and the benefits of implementing this technology are evaluated. When comparing a single maintenance activity using a vascular network versus manual repair, there is no significant benefit of using a vascular network. However, environmental impacts are substantially reduced when using a vascular network once multiple repair actions are considered.

An analytical approach to evaluate life-cycle cost of deteriorating pipelines

Optimal inspection and maintenance planning for deteriorating pipelines is important for cost-effective risk management decisions. The objective of this study is to develop an analytical approach for determining the expected value and standard deviation of life-cycle cost (LCC) for deteriorating pipelines, which can incorporate imperfect repair and inspection actions without making independent assumptions of failure and repair events. The framework is developed based on a decision tree model by using analytical methods to evaluate events and considers the impact of the inspection schedules and possible repair actions on the probability distributions of failure times. To illustrate the proposed framework, a steel transmission pipeline with a corrosion defect is used, where a stochastic model of corrosion growth is assumed and burst failure is considered as the pipeline failure mode. The case study indicates that the failure cost due to two or more failure occurrences can be ignored; and the inspection interval, repair threshold and type, the length of lifetime considered, and unit failure cost all play important roles in the expected value of the total cost. The variation in the LCC estimation is found to be significant, and it has a great impact on determination of optimal decision variables.

Prime Editing and DNA Repair System: Balancing Efficiency with Safety.

Prime editing (PE), a recent progression in CRISPR-based technologies, holds promise for precise genome editing without the risks associated with double-strand breaks. It can introduce a wide range of changes, including single-nucleotide variants, insertions, and small deletions. Despite these advancements, there is a need for further optimization to overcome certain limitations to increase efficiency. One such approach to enhance PE efficiency involves the inhibition of the DNA mismatch repair (MMR) system, specifically MLH1. The rationale behind this approach lies in the MMR system's role in correcting mismatched nucleotides during DNA replication. Inhibiting this repair pathway creates a window of opportunity for the PE machinery to incorporate the desired edits before permanent DNA repair actions. However, as the MMR system plays a crucial role in various cellular processes, it is important to consider the potential risks associated with manipulating this system. The new versions of PE with enhanced efficiency while blocking MLH1 are called PE4 and PE5. Here, we explore the potential risks associated with manipulating the MMR system. We pay special attention to the possible implications for human health, particularly the development of cancer.

A logical retrofit strategy optimization framework for resiliency bridge infrastructure management considering life-cycle cost

Bridges play an important role in providing essential services to communities as one of the most critical components of transportation infrastructure. In this regard, selecting reliable, robust, and efficient indicators is necessary to prepare a disaster management strategy. This study presents a multi-objective optimization framework for decision-makers to find the most optimal retrofit strategies that satisfy a given threshold of functionality/Resilience (R) while minimizing a structure's Life-Cycle Cost (LCC). Accordingly, various retrofit strategies include different materials (steel, Carbon Fiber Reinforced Polymer (CFRP), and Glass Fiber Reinforced Polymer (GFRP)), thicknesses, arrangements, and timing of retrofitting actions. In each scenario, the fragility curves are derived through nonlinear time-history Incremental Dynamic Analysis (IDA) to evaluate the LCC and resilience. In the subsequent step, the LCC analysis is conducted, considering the proposed formulation of multiple occurrences of seismic events, which incorporates the effects of complete/incomplete repair actions of damage conditions induced by previous seismic events. This study employs an elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify the optimal set of solutions. The various aspects of the optimal retrofit strategies are thoroughly investigated and discussed for a bridge as a case study infrastructure. Results show that the considered objectives lead to reasonable and sense-making retrofit strategies.

DNA Damage Responses in Tumors Are Not Proliferative Stimuli, but Rather They Are DNA Repair Actions Requiring Supportive Medical Care.

In tumors, somatic mutagenesis presumably drives the DNA damage response (DDR) via altered regulatory pathways, increasing genomic instability and proliferative activity. These considerations led to the standard therapeutic strategy against cancer: the disruption of mutation-activated DNA repair pathways of tumors. Justifying that cancer cells are not enemies to be killed, but rather that they are ill human cells which have the remnants of physiologic regulatory pathways. 1. Genomic instability and cancer development may be originated from a flaw in estrogen signaling rather than excessive estrogen signaling; 2. Healthy cells with genomic instability exhibit somatic mutations, helping DNA restitution; 3. Somatic mutations in tumor cells aim for the restoration of DNA damage, rather than further genomic derangement; 4. In tumors, estrogen signaling drives the pathways of DNA stabilization, leading to apoptotic death; 5. In peritumoral cellular infiltration, the genomic damage of the tumor induces inflammatory cytokine secretion and increased estrogen synthesis. In the inflammatory cells, an increased growth factor receptor (GFR) signaling confers the unliganded activation of estrogen receptors (ERs); 6. In breast cancer cells responsive to genotoxic therapy, constitutive mutations help the upregulation of estrogen signaling and consequential apoptosis. In breast tumors non-responsive to genotoxic therapy, the possibilities for ER activation via either liganded or unliganded pathways are exhausted, leading to farther genomic instability and unrestrained proliferation. Understanding the real character and behavior of human tumors at the molecular level suggests that we should learn the genome repairing methods of tumors and follow them by supportive therapy, rather than provoking additional genomic damages.