Self-healing System Research Articles

Optimal expansion planning of a self-healing distribution system considering resiliency investment alternatives

This paper proposes a three-stage optimization framework for the expansion planning of a self-healing distribution system that determines the optimal characteristics of distributed generation, energy storage systems, electric vehicle charging stations, and sectionalizing switches for the planning horizon. The main contribution of this model is that the proposed model considers the resilient investment alternatives in the expansion planning exercise to reduce the system's vulnerability against external shocks. The mobile energy storage system commitment in contingent conditions is another contribution of this paper. In the first stage, the optimal location, capacity, and time of installation of the electricity facilities are calculated. Then, the optimal allocation of sectionalizing switches is performed in the second stage. The third stage consists of three levels. In the first and second levels, the optimal normal and contingent operational scheduling are determined, respectively. The system is sectionalized into multi-microgrid systems in contingent conditions. Finally, the resilient investment alternatives for the designed system are evaluated. The proposed model utilizes a self-healing index and resilient expansion planning index to assess the impacts of resilient investment alternatives on the operational scheduling conditions. The proposed model was evaluated using the IEEE 123-bus system. The proposed method reduced the estimated average value of the worst-case energy not supplied by 50.52 % for the 5th year of the planning horizon concerning the no-resiliency investment case. Further, the proposed resilience investment method increased the self-healing index by about 9.32 % concerning the no-resiliency investment case.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

An Intelligent Approach to Automated Operating Systems Log Analysis for Enhanced Security

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model’s accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

Cellular automaton model of self-healing

We propose a simple cellular automaton model of a self-healing system and investigate its properties. In the model, the substrate is a two-dimensional checkerboard configuration which can be damaged by changing values of a finite number of sites. The cellular automaton we consider is a checkerboard voting rule, a binary rule with Moore neighborhood which is topologically conjugate to the majority voting rule. For a single-color damage (when only cells in the same state are modified), the rule always fixes the damage. For a general damage, when it is localized inside a 3 × 3 square, the rule also fixes it always. When the damage is inside of a larger n × n square, the efficiency of the rule in fixing the damage becomes smaller than 100 % , but it remains better than 98 % for n ≤ 5 and better than 75 % for n ≤ 7 . We show that in the limit of infinite n the efficiency tends to zero.

Bio-inspired self-healing polyurethane system: Mimicking connective tissue with hydrogen-bonding mechanism

Achieving a balance between mechanical strength and self-healing efficiency in self-healing materials is challenging yet crucial. This work introduces PCL-HBU, a polyurethane material designed for the first time to mimic collagen fibers, elastic fibers, and vascular networks of connective tissue, with excellent mechanical properties and self-healing efficiency. Meanwhile, by adjusting the 2-ureido-4-[1H]-pyrimidinone (UPy) and N,N-bis(2-hydroxyethyl)oxamide (BHO) ratio, this work creatively developed PCL-HBU3 with three distinct phase structures: amorphous region, soft segment crystallisation, and hard segment crystallisation, which approach confers excellent mechanical properties to the material. PCL-HBU3 exhibits a tensile strength of 70.0 MPa and elongation at break of 1500 %. It can recover from 600 % elongation within 20 s, achieving 100 % elongation at break self-healing efficiency. Furthermore, the composite conductive material fabricated using PCL-HBU3 as the matrix showcases self-healing abilities and holds promise in motion detection and sound recognition applications.

Unlocking the efficacy of cement-shell encapsulation for microbial self-healing process of concrete cracks

Oxygen supply and bacterial viability are crucial for efficient calcium carbonate precipitation in concrete crack healing processes. However, the open deliverance of oxygen-releasing compounds (ORCs) and bacterial spores during concrete mixing and casting poses a threat to spore viability and limits oxygen availability. To address these challenges, we developed an innovative approach integrating an oxygen self-supply strategy with bacterial strain B6. As such, B6 spores, along with Ca(OH)2 and CaO2 as oxygen sources and essential nutrients, were embedded within cement-shell microcapsules to build a microbial self-healing system. The result shows that in the presence of oxygen source and nutrients, B6 group (BPN) exhibits successful crack repair, achieving a repair ratio of approximately 100% after 30 days, while other control groups show less effective repair rates (20–60%). Moreover, water permeability significantly improves in the BPN specimens after crack repair, reaching 0 ml/cm−2·min, highlighting the efficacy of the developed self-healing system. Encapsulating the healing agent within cement shell effectively mitigates the reactivity between ORCs (CaO2) and water, thereby facilitating stable oxygen supply and safe delivery of microbial agents during crack healing. Furthermore, in-situ micromorphology and composition identification of crack repair materials affirm the presence of calcium carbonate precipitation to seal the concrete cracks. The encapsulation of healing material within cement shell stands out as an innovative approach, demonstrating an efficient self-healing process of concrete cracks. These findings might unveil new possibilities for bio-concrete to be used in sustainable and resilient construction practices, particularly in environments prone to cracking and water ingress.

Design and performance of cost-effective green engineered cementitious composites with low drying shrinkage and self-healing

Despite its excellent mechanical properties and crack control ability, engineered cementitious composite (ECC) was still facing challenges such as high cost, high carbon footprint, high shrinkage, and insufficient self-healing ability in natural environments for engineering applications. This study designed and developed a new ECC with characteristics of low-cost, green, ultra-high ductility, low drying shrinkage and self-healing. The effects of superabsorbent polymer (SAP) and light-burned magnesia oxide (LB-MgO) on the micro and macro mechanical properties of ECC were studied; the self-healing performance evaluation system of ECC in natural environment was established; the shrinkage regulation mechanism of SAP and LB-MgO on ECC was revealed; the microstructure, pore structure and hydration mechanism were analyzed. The study showed that the addition of SAP and LB-MgO can effectively reduce the fracture toughness and shrinkage of the matrix, improve the strain hardening strength index and energy index of ECC, and obtain the self-healing ability in the natural environment, which can continue to maintain its durability. This kind of ECC helps improve the long-term durability of the structure and meets high performance requirements while reducing material costs and environmental impact effectively.

Tough Self-Healing Materials via In Situ Growth of Zeolitic Imidazolate Framework-8 Nanocrystals in Polymers.

Construction of self-healing materials with improved mechanical performanceis a great challenge. A strong and tough self-healing composite is fabricated via in situ growth of zeolitic imidazole framework-8 (ZIF-8) nanocrystals in imidazole-containing polymer networks. By adjusting the stoichiometric ratio of the zinc salt to 2-methylimidazole, composites with various mechanical performances are obtained. The existence of ZIF-8 nanocrystals via in situ growth in the polymer networks is confirmed by X-ray diffraction (XRD), transmission electron microscopy (TEM), and atomic force microscopy (AFM). The zinc-imidazole interactions between the ZIF-8 nanocrystals and the polymer are confirmed by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The composites can repair themselves under mild conditions owing to dynamic zinc-imidazole interactions. The self-healing efficiency of composites can reach up to 91% under the condition of 60°C for 48h. In contrast to the pure zinc cation crosslinking system, the composite containing ZIF-8 nanocrystals prepared via in situ growth exhibited enhanced tensile strength and toughness by 43% and 100%, respectively. This study proves that incorporating the metal-organic frameworks(MOFs) materials into a self-healing system via an in situ growth strategy is highly promising for designing self-healing materials with improved mechanical performance.

Enzymatic Activation and Continuous Electrochemical Production of Methane from Dilute CO2 Sources with a Self-Healing Capsule.

Converting dilute CO2 source into value-added chemicals and fuels is a promising route to reduce fossil fuel consumption and greenhouse gas emission, but integrating electrocatalysis with CO2 capture still faced marked challenges. Herein, we show that a self-healing metal-organic macrocycle functionalized as an electrochemical catalyst to selectively produce methane from flue gas and air with the lowest applied potential so far (0.06 V vs reversible hydrogen electrode, RHE) through an enzymatic activation fashion. The capsule emulates the enzyme' pocket to abstract one in situ-formed CO2-adduct molecule with the commercial amino alcohols, forming an easy-to-reduce substrate-involving clathrate to combine the CO2 capture with electroreduction for a thorough CO2 reduction. We find that the self-healing system exhibited enzymatic kinetics for the first time with the Michaelis-Menten mechanism in the electrochemical reduction of CO2 and maintained a methane Faraday efficiency (FE) of 74.24% with a selectivity of over 99% for continuous operation over 200 h. A consecutive working lab at 50 mA·cm-2, in an eleven-for-one (10 h working and 1 h healing) electrolysis manner, gives a methane turnover number (TON) of more than 10,000 within 100 h. The integrated electrolysis with CO2 capture facilitates the thorough reduction of flue gas (ca. 13.0% of CO2) and first time of air (ca. 400 ppm of CO2 to 42.7 mL CH4 from 1.0 m3 air). The new self-healing strategy of molecular electrocatalyst with an enzymatic activation manner and anodic shifting of the applied potentials provided a departure from the existing electrochemical catalytic techniques.

Isocyanate microcapsules recovering automatically impermeability of cement paste through self-hydrophobic broken panels

Cracks are inevitable in the use of cement-based materials. It takes a lot of resources to repair these cracks and prolong the service life of cement-based materials. Self-healing cement-based materials that can automatically repair cracks and improve the self-healing ability of cement-based materials have become the focus of current research. The current self-healing systems achieve impermeability recovery of matrix by the filling cracks. Few scholars have paid attention to the impermeability recovery through self-hydrophobic broken panels. In this paper, SEM, FTIR, core content titration, water flux test and contact angle test were carried out. The morphology, composition, core content and impermeability recovery effect of self-healing cement paste were prepared and characterized. Carbon nanotubes are used to improve the water resistance of isocyanate microcapsules, which possess a diameter of 237.6 ± 59.1 μm, distinct core-shell structure, and good dispersibility. The residual core fraction is 58.2 % after 21 days in Ca(OH)2 aqueous solutions. The microcapsules maintain integrity without breakage when mixed with fresh cement paste and have good bonding compatibility with cement matrix. When microcapsules are embedded into cement paste, the cracks could break the microcapsules releasing isocyanate to wet the broken panels. The isocyanates react with the moisture with the formation of polyurea, changing the contact angle of broken panels from near 0° to 118.4 ± 0.1°. The hydrophobic panels could retard the water penetration and recover the impermeability of cement paste. Based on this, a new self-healing mechanism of self-hydrophobic fracture surface to realize self-healing of impermeability of cement matrix is proposed.

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.

Modeling coupling impacts of self-healing mechanisms and dynamic environments on systems subject to dependent failure processes

Modern smart products are endowed with the capability to autonomously repair damage from random shocks, thereby highlighting the critical necessity to incorporate this self-healing attribute in reliability assessments. This paper investigates periodically inspected self-healing systems that are exposed to dependent competing failure processes within dynamically evolving environments. The impact of environmental changes is manifested in that both the natural degradation process and the self-healing behavior are governed by distinct laws in varying environments. Explicit formulas for the system reliability and availability are analytically derived using the stochastic process theory, and their correctness is verified by proposed simulation algorithms respectively. Meanwhile, the inspection period is optimized by minimizing the long-run average cost rate. Lastly, a practical example of semiconductor lasers is applied to showcase the application of the presented methods. The sensitivity analysis reveals that increasing the self-healing time threshold or the shock arrival rate can improve the system performance while lowering the long-run average cost rate.

Epoxy/polymercaptan microcapsules composite: Synthesis, mechanics, and self-healing behavior

To enhance the longevity of materials, minimize maintenance expenses, and optimize material performance. The wall material employed in this study was urea-melamine-formaldehyde (UMF), and a dual microcapsule self-healing system was established by synthesizing two distinct microcapsules of epoxy resin and polymercaptan via an in situ polymerization technique. Moreover, the reaction conditions were optimized. The reaction parameters were as follows: core-shell ratio of 2:1, surfactant concentration of 0.6 g/L (sodium dodecyl benzene sulfonate (SDBS): Arabic gelatin (GA) = 2:1), polymerization pH = 3.5, stirring rate of 700r/min. Then, the two microcapsules (The mass ratio of the two microcapsules is 1:1) were added to the epoxy resin matrix at 0, 2.5, 5, 7.5, 10, and 12.5 wt% to prepare the resin matrix with self-healing properties. The conical double cantilever beam (TDCB) was used to test the mechanical properties of the matrix. The results showed that the matrix with microcapsule content of 10 wt% had relatively better comprehensive properties, and the self-healing rate was 49.87% at room temperature. These findings provide valuable insights and potential for advancing the development of self-healing materials.

A Review Exploring the Wound-Healing Activity of Self-Healing Hydrogels: Fabrication, Characterization, Mechanism, and Biomedical Applications

The natural physiological response to skin injury is wound healing. However, to restore skin continuity, wound healing is a complicated process that involves the collaboration of a variety of cell types and other mediators. This process ultimately results in tissue regeneration and the restoration of skin barrier function. Hydrogels are appealing dosage forms for biomedical regenerative medicine since they are composed of 3D networks with high water content and flexible rheological features. Hydrogels that can self-heal are particularly interesting for wound treatment because they can autonomously restore their original functionalities and repair structural damage. Recently, the use of self-healing hydrogels as biomedical materials has attracted increased interest. In this review, the self-healing systems used in tissue regeneration, especially wound healing, will be explored. A focus on the fabrication methods, characterization tests, and mechanism of self-healing will be introduced, along with the biomedical applications of self-healing hydrogels loaded with conventional and therapeutic biomaterials. In addition, the differences between hydrogels and self-healing hydrogels will be discussed.Graphical

Enthalpy-Driven Self-Healing in Thin Metallic Films on Flexible Substrates.

Self-healing microelectronics are needed for costly applications with limited or without access. They are needed in fields such as space exploration to increase lifetime and decrease both costs and the environmental impact. While advanced self-healing mechanisms for polymers are numerous, practical ways for self-healing in metal films have yet to be found. A concept for an autonomous intrinsic self-healing metallic film system is developed, allowing the healing of cracks in metallic films on flexible substrates. The concept relies on stabilizing metastable thin films with high mixing enthalpy via segregation barriers. This allows the films to possess autonomous intrinsic self-healing capabilities triggered by cracking at temperatures not detrimental to flexible microelectronics. The effect will be shown on metastable Mo1-xAgx thin films, stabilized via a Mo segregation barrier. Without a segregation barrier, the system is known to exhibit spontaneous Ag particle formation on the surface. This property is controlled and directed to heal cracks and partially restore the electro-mechanical properties of the multilayer system. This mechanism opens up the field of self-healing thin metallic films that could profoundly impact the design of future microelectronics.

Self-healing hybrid intrusion detection system: an ensemble machine learning approach

The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data.

A quadratic formulation for the optimal allocation of fault indicators

Faults are an inherent part of power distribution systems, and fault indicators play a crucial role in self-healing smart distribution systems by assisting in fault detection and location. However, the effectiveness of fault indicators depends on their number and location in the distribution system. This paper addresses the optimal decisions on the allocation of fault indicators in distribution networks to minimize the expected fault location time.To build upon the existing knowledge, a comprehensive review of the literature on the allocation of fault indicators is provided. Additionally, a novel approach is given by formulating the fault location time as a quadratic function of the network’s section sizes. From a relationship established between the fault indicator allocation problem and connected edge-partitions, a mathematical model and a metaheuristic are developed.To evaluate the proposed methodologies, a benchmark comprising 19 networks is employed, including the largest network solved for this problem to date. The results demonstrate the superiority of the new methodologies over previous approaches, as evidenced by the quality of the solutions and their optimality gaps.

Self-Healing Fiber Bragg Grating Sensor System Using Free-Space Optics Link and Machine Learning for Enhancing Temperature Measurement

This research investigates the integration of free-space optics (FSO) with fiber Bragg grating (FBG) sensors in self-healing ring architectures, aiming to improve reliability and signal-to-noise ratio in temperature sensing within sensor systems. The combination of FSO’s wireless connectivity and FBG sensors’ precision, known for their sensitivity and immunity to electromagnetic interference, is particularly advantageous in demanding environments such as aerospace and structural health monitoring. The self-healing architecture enhances system resilience, automatically compensating for failures to maintain consistent monitoring capabilities. This study emphasizes the use of intensity wavelength division multiplexing (IWDM) to manage the complexities of increasing the multiplexing number of FBG sensors. Challenges arise with the overlapping spectra of FBGs when multiplexing several sensors. To address this, a hybrid approach combining an unsupervised autoencoder (AE) with a convolutional neural network (CNN) is proposed, significantly enhancing the accuracy and efficiency of sensor signal detection. These advancements signify substantial progress in sensor technology, validating the effectiveness of the AE-CNN hybrid model in refining FBG sensor systems and underscoring its potential for robust and reliable applications in critical sectors.

Shear strength recovery of sand with self-healing polymeric capsules

Self-healing approaches are increasingly being explored in various fields as a potential method to recover damaged material properties. By self-recovering without external intervention, self-healing techniques emerge as a potential solution to arrest or prevent the development of large strains problems in soils (e.g., landslides) and other ground effects that influence the serviceability of structures (e.g., differential settlement). In this study, a microcapsule-based self-healing sand was developed, and its performance during mixing and compaction, shearing, and recovery of shear strength was demonstrated. The cargo used for sand improvement, a hardening oil, tung oil, was encapsulated in calcium alginate capsules by the ionic gelation method. The surface properties, internal structure, thermal stability and molecular structure of the capsules were evaluated by advanced material characterization techniques. The survivability of capsules during mixing and compaction was assessed by measuring the content of tung oil released into the sand, while their influence on sand shear strength and its recovery was assessed with shear box tests. The results showed that the capsules could rupture due to movement of the sand particles, releasing the tung oil cargo, leading to its hardening and minimizing its strain-softening response and enhancing up to 76% of the sand shear strength (at a normal stress of 10 kPa and capsules content of 4%). This study demonstrates the potential of a capsules-based self-healing system to provide ‘smart’ autonomous soil strength recovery and thus with potential to actively control the large strain behavior of soils.