Self-healing Control Research Articles

Residual integral inverse reinforcement learning for intelligent self-healing control of unknown systems with actuator faults

Residual integral inverse reinforcement learning for intelligent self-healing control of unknown systems with actuator faults

Self-Healing Control of Nuclear Power Plants Under False Data Injection Attacks

The transition from analog to digital instrumentation and control (I&C) systems introduces new threats caused by cyberattacks in the nuclear industry. This paper proposes a self-healing strategy to respond to a false data injection attack that targets digital I&C systems, which is a type of cyberattack commonly targeting nuclear power plants with the potential to cause serious physical impacts. This resilience strategy for self-healing control contains three components: (1) an anomaly detection model that can detect false data injection attacks, (2) a device-level control that utilizes inferred values to perform control under a detected false data injection, and (3) a system-level control that leverages another controller that is not under attack to lead the system back to a safe operation state when the device-level control is unavailable. Anomaly detection and device-level control use an autoencoder while system-level control utilizes reinforcement learning. The proposed self-healing resilience strategy is demonstrated with a generic pressurized water reactor (GPWR) simulator under false data injections, targeting the steam generator water level. The results show that the proposed strategy effectively leads the system back to a normal operation state under various false data injection cases.

SPARC overexpression in allogeneic adipose-derived mesenchymal stem cells in dog dry eye model induced by benzalkonium chloride

BackgroundNowadays, companion and working dogs hold significant social and economic importance. Dry eye, also known as dry keratoconjunctivitis (KCS), a common disease in ophthalmology, can readily impact a dog’s working capacity and lead to economic losses. Although there are several medications available for this disease, all of them only improve the symptoms on the surface of the eye, and they are irritating and not easy to use for long periods of time. Adipose-derived mesenchymal stem cells (ADMSC) are promising candidates for tissue regeneration and disease treatment. However, long-term in vitro passaging leads to stemness loss of ADMSC. Here, we aimed to use ADMSC overexpressing Secreted Protein Acidic and Rich in Cysteine (SPARC) to treat 0.25% benzalkonium chloride-treated dogs with dry eye to verify its efficacy. For in vitro validation, we induced corneal epithelial cell (HCECs) damage using 1 µg/mL benzalkonium chloride.MethodsFifteen male crossbred dogs were randomly divided into five groups: normal, dry eye self-healing control, cyclosporine-treated, ADMSC-CMV-treated and ADMSC-OESPARC-treated. HCECs were divided into four groups: normal control group, untreated model group, ADMSC-CMV supernatant culture group and ADMSC-OESRARC supernatant culture group.ResultsSPARC-modified ADMSC had the most significant effect on canine ocular surface inflammation, corneal injury, and tear recovery, and the addition of ADMSC-OESPARC cell supernatant also had a salvage effect on HCECs cellular damage, such as cell viability and cell proliferation ability. Moreover, analysis of the co-transcriptome sequencing data showed that SPARC could promote corneal epithelial cell repair by enhancing the in vitro viability, migration and proliferation and immunosuppression of ADMSC.ConclusionThe in vitro cell test and in vivo model totally suggest that the combination of SPARC and ADMSC has a promising future in novel dry eye therapy.Graphical abstract

Knowledge-Guided Adaptive Neuro-Fuzzy Self-Healing Control for Sludge Bulking in Wastewater Treatment Process

Knowledge-Guided Adaptive Neuro-Fuzzy Self-Healing Control for Sludge Bulking in Wastewater Treatment Process

Sensor and Actuator Fault Estimations and Self-Healing Control of Discrete-Time T-S Fuzzy Model With Double Observers and Its Application to Wastewater Treatment Process

Sensor and Actuator Fault Estimations and Self-Healing Control of Discrete-Time T-S Fuzzy Model With Double Observers and Its Application to Wastewater Treatment Process

Distributed self-healing control of single-phase grounding fault in neutral point non-effective grounding system

Abstract In distribution networks, single-phase grounding occurrences in non-effectively grounded systems do not result in short-circuits, thus leading to low fault currents. Particularly in high-resistance grounding scenarios, fault currents become extremely low, increasing the risk of protection misjudgments. To enhance the speed and accuracy of self-healing during such faults, a distributed self-healing control method based on flexible grounding and zero-sequence current analysis for non-effectively grounded systems is proposed. This method employs peer-to-peer distributed self-healing and flexible grounding techniques to convert isolated or arc-suppressed neutral systems to low-resistance grounded systems. Additionally, a localization criterion unaffected by neutral grounding modes is introduced, utilizing deviations in zero-sequence current upstream and downstream of the fault as distinguishing characteristics. The proposed method is straightforward in principle and leverages existing terminal equipment for accurate and swift fault processing. Simulation results validate the method’s resilience to transition resistance and neutral grounding conditions, demonstrating its suitability for single-phase grounding fault localization across all system types. The research findings effectively ensure the accuracy and swiftness of self-healing during single-phase grounding faults in non-effectively grounded systems.

Self-healing control of abnormal conditions for fused magnesium furnace based on data augmentation and improved JITL

Abnormal conditions that arise during the fused magnesia smelting process (FMSP) can negatively impact product quality and increase energy consumption. However, due to the process's nonlinear and time-varying nature and the limited availability of abnormal condition data, researching effective self-healing control measures has been severely constrained. Therefore, this paper proposes a fused magnesium furnace (FMF) abnormal condition self-healing control method based on data augmentation and improved just-in-time learning (JITL). Firstly, data augmentation technology is used to generate virtual abnormal condition samples to expand the historical dataset. Then, an improved JITL model is constructed to solve the self-healing measures corresponding to abnormal conditions. Experimental results demonstrate that the proposed method can effectively restore the FMF to normal conditions and outperforms existing methods.

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

Industrial Metaverse-Powered Interactive and Self-Healing Control Method for Combustion System

Industrial Metaverse-Powered Interactive and Self-Healing Control Method for Combustion System

Data and knowledge collaborative-driven fault identification and self-healing control action inference framework for blast furnace

The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity.

Self-Healing Control for Wastewater Treatment Process Based on Variable-Gain State Observer

This paper proposes a variable-gain state observer-based sliding mode self-healing control (VSO-based SMSHC) method for a wastewater treatment process (WWTP) with changeable external disturbances and sensor failures. To reconstruct the disturbances and unmeasured system states, a novel VSO is developed, in which the VSO gains are adjusted by following an automatic variable-gain mechanism for adapting to the variation of external disturbances. An adaptive compensation coefficient of failure factor is designed to counteract the failure effects on observation and tracking performance. By utilizing the cubic absolute-value Lyapunov stability criterion, it is shown that system stability and tracking performance of WWTP are guaranteed. Experimental studies are carried out on a standardized platform of WWTP, and the results on dissolved oxygen and nitrate nitrogen regulation indicate that the proposed control strategy can ensure excellent control performance and reduce both failure and disturbance impacts.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Abstract In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.

Fault estimation and self-healing control of discrete-time T-S fuzzy model with sensor and actuator faults based on dual observers

This paper proposes a sensor and actuator fault estimation and self-healing control scheme to address issues related to deteriorating effluent quality, process failures, and safety hazards. Firstly, a discrete T-S fuzzy model of the wastewater treatment system is developed by employing the weighted recursive least squares method, which involves identifying the system based on the Benchmark Simulation Model No. 1. Secondly, in order to overcome the conservativeness problem when estimating sensor and actuator faults with a single observer, a fault estimation method employing dual observers is proposed utilizing the discrete-time T-S fuzzy model established. Thirdly, a fuzzy weighted self-healing controller is designed based on integral sliding mode and state feedback. This controller aims to overcome the aforementioned issues. Finally, Some simulations are provided to ensure the effectiveness of the obtained results. The simulation results show that compared with other methods, the fault estimation method using dual observers achieves shorter error estimation correction time and faster response to sudden fault estimation. Compared with output feedback controller, dynamic output feedback based self-healing controller exhibits superior control performance and better dynamic tracking performance.

A self-healing controller based on sliding-mode control for sensor fault in wastewater treatment processes

Wastewater treatment processes (WWTPs) include numerous sensor devices, which are prone to failure in the difficult work environment after long-term treatment, resulting in the effluent quality not meeting the requirements. The existing control methods do not consider this issue, to address this issue, we introduce self-healing control into WWTPs. Firstly, due to the difficult of analyzing the mechanistic model of WWTPs, this paper establishes a low-order model by using the Takagi–Sugeno fuzzy method. Secondly, with respect to the sensor additive fault, a generalized system is built to turn the problem of estimating the sensor fault into the problem of estimating the actuator fault, and a generalized sliding-mode observer is designed. Then, the system output is corrected by fault estimation, and a self-healing controller based on sliding-mode control is developed. Finally, the simulation experiments of a dissolved oxygen instrument in case of sudden and slow change faults are conducted. The simulation results demonstrate that the generalized sliding-mode observer can estimate the fault precisely and quickly, and the self-healing controller can make the system follow the setpoint quickly and accurately under fault conditions, which verifies the effectiveness of the proposed methods.

Fault Seamless Self-Healing Method of Regional Distributed Network Based on the Cooperation of Source-Load-Storage

The large-scale development of distributed generators poses challenges to the operation of distribution networks in remote areas. The power grid structure of the regional distributed networks in remote areas is relatively weak. When a fault occurs in the connecting channel, the regional distributed network may be separated from the main grid and become an island. Those distributed generators not only cannot maintain stable operation of the island but may lead to reclosing failure. This may lead to power interruption of important loads. Aiming at the demand for continuous power supply of important loads in regional distributed networks, a self-healing control method based on the cooperation of source-load-storage is proposed in this paper. The characteristics of the regional distributed network are analyzed first. A real-time island stability control method based on the operating conditions combined with regulation with tripping is proposed. This method comprehensively evaluates the self-healing ability of the regional distributed network after the island. The regulation strategy is adopted preferentially to realize the stable operation of the island without losing any load. When the regulation ability is insufficient, the strategy combines regulation and tripping. Considering the importance of load and source-load ratio, and according to the real-time power of each feeder, the optimal feeders are cut off. The purpose of island stability control of the regional distributed network was achieved. In this way, the load loss is minimized, and the speed of island re-connection to the grid is also accelerated. The seamless power supply of important loads in a regional distributed network is guaranteed. A complex Hardware-in-Loop simulation test platform of RTDS is built to verify the correctness of the proposed method.

Residual-triggered threshold decision and performance self-healing control for wastewater treatment process

The delicate control of effluent quality in wastewater engineering becomes imperative for fulfilling its role in protecting the ecological environment and promoting the water recycling. In this paper, an adaptive multivariable self-healing control strategy for the wastewater treatment process (WWTP) operating under complicated environments and facing abnormal conditions. The control goal is to meet the strict effluent standards of the WWTP and to optimize the abnormal conditions by analyzing the process operating status in the event of sudden sensor failures. Therein, a new residual-triggered threshold decision method is designed and integrated into a dynamical detection mechanism to identify such sensor failures. After locating the failures, a multivariable self-healing control scheme incorporating failure compensation information is activated to guarantee the process output tracking and improve the self-recovery capability of faulty performance in the WWTP. Extensive experiments are carried out on a standardized wastewater treatment platform, and the regulation results on the key component concentrations indicate that the proposed control scheme can ensure excellent control performance and reduce the impact of sensor failures.

Design of Broad Learning-Based Self-Healing Predictive Control for Sludge Bulking in Wastewater Treatment Process

Self-healing control plays a crucial role in taking remedial action to minimize the adverse impacts of sludge bulking in wastewater treatment process (WWTP). However, since sludge bulking is a strong nonlinear and complex process with multiple fault conditions, the conventional self-healing control is difficult to obtain reliable performance. Thus, the purpose of this paper is to design a broad learning-based self-healing predictive controller (BL-SHPC) for sludge bulking in WWTP. The main innovations of the proposed controller are threefold. First, a dynamic fuzzy broad learning system (DFBLS) with an adaptive expansion strategy (AES) is used to identify the fault conditions of sludge bulking. Then, the fault features of sludge bulking can be comprehensively extracted with desirable performance. Second, a prioritized multi-objective optimization algorithm (PMOOA)-based predictive control, which considers the objective correlation and preference of fault conditions, is presented to obtain the optimal solutions to achieve self-healing. Then, the proposed controller can feasibly and precisely readjust manipulated variables to eliminate the sludge bulking. Third, the stability of the developed controller is proved by the Lyapunov stability theorem. Then, the stability analysis can ensure the successful application of BL-SHPC. Finally, the proposed BL-SHPC is tested on the Benchmark Simulation Model No.2 (BSM2) to validate its merits. The simulation results indicate that the proposed controller can obtain superior self-healing ability for sludge bulking in WWTP.

Optimal Service Restoration and Adaptive Switching of Tie Switches Method of Distributed Self-healing Control in Distribution Systems

Optimal Service Restoration and Adaptive Switching of Tie Switches Method of Distributed Self-healing Control in Distribution Systems

Self-Healing Control: Review, Framework, and Prospect

Self-Healing Control: Review, Framework, and Prospect

Self-healing Control Based on State Observer for Networked Control Systems

Self-healing Control Based on State Observer for Networked Control Systems