Desgin of multi-agent system for quality control and machine maintenance

As the complexity and scale of modern buildings increase, there is a growing need for efficient, scalable, and flexible control systems to manage the myriad of sensors, actuators, and meters involved in building automation. Traditional building management systems (BMS) struggle with scalability, especially when dealing with multi-agent control setups that require extensive manual configuration. This limitation poses significant challenges in deploying advanced control strategies across different building environments, leading to inefficiencies and underutilization of building automation technologies.Previous research has explored various aspects of multi-agent systems (MAS) in building automation, including agent-based modeling, decentralized control strategies, and the application of machine learning to optimize building operations. However, much of the existing work has focused on small-scale implementations or specific applications, such as energy management or HVAC control. The problem of automating the setup of multi-agent control systems to adapt to different building environments with minimal manual intervention remains largely unaddressed.The aim of this work is to develop a scalable framework for the automated setup of multi-agent control systems in buildings. This framework will enable the seamless integration of sensors, actuators, and meters into a unified control system, significantly reducing the need for manual configuration. The framework will leverage large language models (LLMs) for semantic tagging and context understanding, thereby facilitating the automated configuration of action and input spaces for individual agents within the control system.The proposed framework will consist of three key components: (1) an LLM-based system for semantic tagging and context interpretation, (2) an automated setup process for defining the action and input spaces of the agents, (3) and an initial offline training of the agents. The LLM will be trained on a diverse dataset of building management setups to ensure it can accurately tag and interpret various building elements. The system will be tested in real-world office buildings to evaluate its scalability, accuracy, and effectiveness.Preliminary simulations indicate that the automated setup framework can significantly reduce the time and effort required to deploy multi-agent control systems in buildings. We expect that this framework will enable more scalable and flexible building management systems, ultimately leading to improved energy efficiency, occupant comfort, and overall operational performance. The significance of this work lies in its potential to transform the deployment of building automation systems, making advanced control strategies more accessible and cost-effective across various building types and scales.

It is imperative for manufacturing systems to improve production quality not only to maintain profitability, market share and competitiveness, but also to reduce energy waste resulted from defective items. Although quality and energy saving have attracted extensive attention in the past few decades, there is little research effort devoted to a systematic understanding of their intersection. Therefore, this paper analyzes the energy usage of a multistage production system with quality control. The Geometric reliability and Bernoulli quality models are assumed. A Markov process model is established to predict the dynamics of the production system. The energy economics of the production system are analyzed to include both production and energy cost. The optimal PWQ (Production with Quality Inspection) machine allocation method and the cost-effectiveness analysis method are formulated to increase the profit. According to the computational experiments, the proposed optimal PWQ machine allocation method can effectively reduce energy consumption. In addition, pay-back period (PBP) is an effective indicator which helps production managers make cost effective decisions for machine replacement. The research results in an in-depth understanding of the energy economics of systems with quality control, which is necessary for manufacturers to gain competitiveness with better product quality and higher energy efficiency.

Damage products that are not in accordance with standards is a major problem for each company, because these problems will cause losses for the company as a result of increasing production costs and lower selling value of products. The quality control system carried out at PT. Sinar Jaya Inti Mulya is still lacking due to a lack of quality control personnel as well as machine maintenance and labor constraints. The purpose of this study was to determine the type and amount of damage that occurred and what factors caused the damage or deterioration of product quality. The method used in this research is explanatory survey method. This type of research is quantitative research with the object of research is product quality control and product damage. Data collection using interviews and documentation. The analytical tool used is to use C Chart analysis, Pareto Chart and Fishbone Analysis. The results of the C Chart analysis research showed that the amount of damage that occurred was still within tolerance limits. The number of product defects based on the order of the number is the highest due to the presence of foreign matter impurities, moisture content and free fatty acid content. The factors that influence product damage are mostly related to workers who do not supervise the production process and lack of quality control personnel as well as other causes such as raw materials and the environment.
 Keywords: Control of product quality, product damage

In this dissertation, new algorithms for formation control of multi agent systems (MAS) based on continuum mechanics principles will be suggested. For this purpose, agents of the MAS are considered as particles in a continuum, evolving in R^n, whose desired configuration is required to satisfy an admissible deformation function. Considered is a specific class of mappings that are called homogenous where the Jacobian of the mapping is only a function of time and is not spatially varying. The primary objectives of this dissertation are to develop the necessary theory and its validation on a mobile-agent based swarm test bed that includes two primary tasks: 1) homogenous transformation of MAS and 2) deployment of a random distribution of agents on a desired configuration. Developed will be a framework based on homogenous transformations for the evolution of an MAS in an n-dimensional space (n=1,2, and 3), under1) no inter-agent communication (predefined motion plan), 2) local inter-agent communication, and 3) intelligent perception by agents. In this dissertation, different communication protocols for MAS evolution that are based on certain special features of a homogenous transformation will be developed. It is also aimed to deal with the robustness of tracking of a desired motion by an MAS evolving in R^n. Furthermore, the effect of communication delays in an MAS evolving under consensus algorithms or homogenous maps is investigated. In this regard, the maximum allowable communication delay for MAS evolution is formulated on the basis of eigen-analysis.

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

Use of multi-agent system for industrial production control

Maintenance, production and quality are strongly linked to each other. Predictive maintenance is an effective way to eliminate the potential failures and ensure the stable operation of the manufacturing system, and further improve the reliability of the manufacturing system and the quality of manufactured products. Regarding the intelligent manufacturing philosophy “prediction and manufacturing#x201D;, a novel predictive maintenance strategy is proposed, in which the product quality control is integrated into predictive maintenance decision-making. First, the key process variables that characterize equipment wear are identified and integrated into the modeling of the equipment failure rate. Second, quality deviation that characterizes product quality level is defined based on co-effect between manufacturing system component reliability and product quality (i.e., Q-R chain). Third, the optimal maintenance strategy is obtained by optimizing the quality cost, maintenance cost, and interruption cost simultaneously. Finally, a case study on the predictive maintenance strategy decision-making for a cylinder head manufacturing system is presented to illustrate the effectiveness of the proposed method. The final result shows that the proposed method can achieve a superior economic performance relative to the conventional preventive maintenance mode.

This paper proposes a new multiagent control system (MACS) for service restoration in distribution systems with integrated distributed generation (DG) units. First, the MACS detects and locates faults, then decides the optimal reconfiguration of the network for restoring de-energized loads, and finally regulates the nodes voltages. Unintentional islanding operation of DG units is avoided and different postfault response modes of DG unit are addressed in the MACS design. The MACS has a hybrid centralizeddecentralized structure where agents are arranged in two layers with different responsibilities and communication capabilities. Agents at load buses in the first layer can only communicate directly with its next neighbor load agents on the same feeder and to its feeder agent, whereas the higher level agents in the second layer can communicate directly with each other. This MACS structure reduces the possibilities of control system failures for a moderate communication network infrastructure. Full dynamic simulation model for evaluating the MACS is implemented.

The article considers innovative approaches to ensuring quality and safety of labor, aimed at increasing productivity, initiative and involvement of personnel in achieving key performance indicators of the organization. The purpose of the study is to substantiate the methodological tools for internal control of labor quality and safety, based on innovative methods, technologies and tools that encourage the existing organized system to self-assessment and self-development, as well as ensuring the continuous transition of this system to a new quality with a higher level of synergy. The authors substantiate the need to apply a systematic innovative approach to internal control of labor quality and labor safety as a comprehensive, dynamic and proactive system (integrated innovative internal control system) focused on continuous improvement of product (work, service) quality management processes and ensuring worker safety. The systematic innovative approach considers control of labor quality and safety not as separate events, but as interconnected elements of a single system that includes goals, objectives, structure, processes, resources and performance indicators. The integrated innovative system of internal control of labor quality and safety is based on the digitalization and automation of internal control procedures, the use of artificial intelligence and machine learning, strategic management tools and safety culture, modern training methods and advanced training, and also assumes the focus of management on the human factor. In addition, the advanced innovative control tools and practices that make up the system ensure the implementation of a customer-oriented approach to management, as they are aimed at ensuring that products (works, services) comply with the established requirements, standards and expectations of consumers, continuously improving the quality and safety management systems of the organization, as well as increasing the consumer value of manufactured goods. This system is considered as a self-developing innovative management subsystem focused on the constant search, development and implementation of new, more effective methods, technologies and internal control tools that take into account the best practices and latest achievements of science and technology. The work emphasizes the importance of involving employees in the management and self-control of labor quality and safety, developing a safety culture, creating comfortable and safe working conditions. It is concluded that the systemic innovative approach to internal control of labor quality and safety is a modern, effective and promising management strategy that allows organizations to achieve high results in ensuring the quality of products (works, services), as well as a high level of health protection and maximizing satisfaction with the results of employee work. Keywords: innovation, internal control, labor, quality, safety, system.

In this paper, MASCONTROL, a multiagent system (MAS) for system identification and process control, is presented. This MAS implements a self-tuning regulator (STR) scheme. In this work, an ontology agent (OA) is included, using DAML+OIL as ontology language. From their experience, the authors consider this architecture highly useful for identification and control processes

This paper will review the process a vertically integrated subsea equipment OEM took to implement Advanced Product Quality Planning (APQP) techniques in effort to satisfy API Specification Q1, 9th Edition risk assessment requirements. Through this paper, we will highlight the organization's implementation approach, challenges faced, and benefits expected including improved product quality, increased customer service level, and lower costs. Driven by recent changes in API Specification Q1, 9th Edition along with customer specific quality requirements, Dril-Quip desired to institute Advanced Product Quality Planning (APQP) processes across its diverse product line and global manufacturing footprint. APQP has been successfully deployed and adopted in many manufacturing industries including automotive, aerospace, and defense. In this paper, Dril-Quip will discuss how it defined APQP standards to fit the specific needs of the oil and gas industry and its approach to apply them to the full range of Dril-Quip products. We will discuss the cross-functional approach including development of Design Failure Modes, Effects and Criticality Analysis (DFMECA) and Process Failure Modes, Effects and Criticality Analysis (PFMECA) for subsea wellhead systems. Critical to that discussion will be a conversation about how Dril-Quip identified the Critical to Quality characteristics (CTQs) and the procedure by which these critical features were assimilated into Dril-Quip's Quality Management System. Additionally, this paper will highlight how APQP will help the organization improve product quality and thus unlock constrained manufacturing capacity. Traditional quality management processes used in the oil and gas industry rely heavily on quality control and inspection. As a result, manufacturing operations often generate a significant number of non-conformances and customers require witness and hold points to ensure product quality. APQP brings a more proactive approach to quality management starting with product design and carrying through product verification analysis and validation testing, manufacturing and product delivery; thus, reducing non-conformances. By reducing non-conformances and designing quality into the product and its production processes, overall product quality and customer confidence will improve eliminating the need for third party witness and unlocking trapped capacity. Equipment manufacturers who are able to unlock this capacity will in turn be able to increase production volumes, shorten lead times and reduce costs. While APQP is not a novel or new concept and has been successfully deployed in the automotive, aerospace, and defense industries, it brings a new approach to quality management in the oil and gas industry. The ultimate conclusion will focus on the reasons why APQP implementation at a vertically integrated OEM reduces costs, improves process efficiency, and achieves a vast quality improvement over previous models including highlighting benchmarks from improvements realized from implementing APQP in other industries.

This paper proposes a multiagent control system (MACS) for a fossil-fuel power unit (FFPU). The MACS consists of fossil-fuel power unit agent (FFPUA), reference agent (RA), feedforward control agents (FFCAs), feedback control agents (FBCAs) and coordinator agent (COA). The reference agent decomposes the task of the MACS by multiobjective optimization. The FFCAs make decision using the neuro-fuzzy systems and the FBCAs make decision using the genetic algorithm-based fuzzy systems. The coordinator agent coordinates the agents in the MACS according to different operating conditions.

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

The current trends in industry include an integration of information and knowledge base networks with a manufacturing system, which coined a new term, e-manufacturing. From the perspective of e-manufacturing, any production equipment and its control functions do not exist alone, instead becoming a part of the holistic operation system with distant monitoring, remote quality control, and fault diagnostic capabilities. The key to this new paradigm is the accessibility to a remotely located system and having the means of responding to a rapidly changing environment. In this context, this correspondence presents innovative methods in remote part tracking and quality control using the Ethernet SmartImage Sensor and the Internet controllable Yamaha SCARA robot. Remote control of an automation process using the Internet can suffer from a time lag, if the network is congested with heavy data traffic, which may be the greatest hurdle for using the Internet for real-time control. The approach discussed in this correspondence overcomes the time lag and mathematically calculates the part locations on the conveyor at various instances and efficiently guides the robot to the product. The accuracy of the proposed scheme has been verified, and subsequent quality control functions have been integrated, which vindicate the industrial applicability of the setup. The Internet-based manufacturing operations present many benefits, such as ubiquitous access, remote control/programming/monitoring capabilities, and integration of production equipment into information networks for improved efficiency and enhanced product quality.

The electric power industry is currently in transition to a new technological platform, which is based on the smart grid concept. Important components of this grid include distributed generation plants as well as those based on renewable energy sources. The centralized system of control of numerous distributed generation plants becomes inefficient due to the need for transferring large amounts of data to the control center. The control efficiency can be enhanced by use of peripheral systems based on the agent approach. The paper suggests the design, description and Joiner-network of an event model of the multi-agent control system of distributed generation plants based on turbine-generators. The main functions of the agents of the interactive network are presented. A system of differential equations defining processes within the power supply system including distributed generation plants is considered. The model of the proposed multi-agent control system is developed in the AnyLogic simulation environment. Modeling results showed positive effects gained with the multi-agent control system. The adaptability of the system to changing conditions and applicability of the multi-agent technologies when establishing active-adaptive networks of intelligent power supply systems are demonstrated.