A knowledge-based approach for multi-factory production systems

This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among factories and can be delivered to the customer in batches which have limited capacity. The aim of this optimization problem is threefold: (1) determining the number of batches, (2) assigning jobs to batches, and (3) scheduling the batches production and delivery in each factory. The proposed problem formulated as a mixed-integer linear program. Then the model's performance is analyzed and evaluated through two examples. Moreover, a knowledge-based imperialistic competitive algorithm (KBICA) is also presented to find an approximate optimum solution for the problem. Computational experiments of the proposed problem investigate the efficiency of the method through different sizes of the test problems.     

Uncertain variables and learning algorithms in knowledge-based control systems

This paper concerns a class of knowledge-based control systems with unknown parameters in the knowledge representation describing a plant. For the deterministic case, the logic-algebraic method of determination of the control decisions has been developed. The purpose of this paper is to present an extension of this method for the case with unknown parameters. In the first part, an approach based on uncertain variables is presented. In the second part, the method and algorithms of current knowledge updating in the learning system are proposed. The main idea of this approach consists of a step-by-step estimation of the unknown parameters in the knowledge representation using the successive values of the control decisions and their results. This concept may be considered as an extension of the known ideas of identification and adaptation for the traditional case. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Learning algorithms in a class of knowledge-based systems

A control system with a static plant described by knowledge representation in the forms of relations and of logical formulas is considered. The learning process consists in using successive knowledge validation and updating it to make current control decisions. Two approaches and algorithms are described: one for the validation and updating of knowledge about the plant, and one on the form of control. In both cases two versions are presented: the learning process in either an open-loop or a closed-loop control system. For a plant with logical knowledge representation, the logic-algebraic method is applied. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Quality control expert systems: a review of pertinent literature

Statistical quality control (SQC) is an effective tool that ensures quality products and services by means of control charts, the essence of SQC, and sampling plans. While the computation of sample statistics and the development of control charts are routine exercises, the interpretation of chart patterns, trends and the associated diagnosis of assignable causes requires expert knowledge. The present trend is to develop a quality control system and apply it throughout the company (company-wide quality control CWQC or total quality control - TQC). This frequently means involvement of non-quality personnel in QC teams. Additionally, many companies are faced with a shortage of experienced quality controllers and individuals who can train and educate others on statistical quality control techniques. Quality control expert systems (QCESs) are considered as one way to alleviate these difficulties. In recent years, quality control expert systems have attracted the attention of both quality researchers and practitioners. This paper reviews existing quality control expert systems and recommends a set of quality engineering techniques that should be used to form a knowledge base, the heart of an expert system.     

切换系统的同时故障估计与容错控制

当切换系统发生故障时,通常需要依次进行故障诊断和容错控制.为了提高切换系统故障诊断的可靠性和容错控制的及时性,本文提出一种同时故障估计与容错控制方法.针对满足平均驻留时间约束的线性切换系统,首先建立了基于状态观测器的同时故障估计与容错控制器,并将其设计问题转化为了加权H∞性能指标下增广误差系统的多目标求解问题.然后使用平均驻留时间技术和多Lyapunov函数方法设计了故障估计与容错控制器的参数,又通过松弛矩阵方法进一步得到了保守性较低的结果.最后,利用一个例子对本文所提方法进行了仿真,证实了该方法的有效性.     

Modular supervisory control of discrete-event systems

A modular approach to the supervisory control of a class of discrete-event systems is formulated, and illustrated with an example. Discrete-event systems are modeled by automata together with a mechanism for enabling and disabling a subset of state transitions. The basic problem of interest is to ensure by appropriate supervision that the closed loop behavior of the system lies within a given legal behavior. Assuming this behavior can be decomposed into an intersection of component restrictions, we determine conditions under which it is possible to synthesize the appropriate control in a modular fashion. The work of this author was supported by NSERC (Canada) under Grant No. A-7399. The work of this author was supported by the National Science Foundation through Grant No. ECS-8504584.     

Roles for knowledge-based computer systems: Case studies in maternity care

The design of medical knowledge-based computer systems requires effective interdisciplinary communication for the development of a community sharing common goals and a common language for design. Over the past 9 years the Perinatal Research Group, an interdisciplinary team of computer scientists, engineers and clinicians, have developed a prototype knowledge-based computer system to aid clinicians in the care of women in labour. The group were uncertain which approach to adopt to progress this system from a prototype to a useful clinical tool to support decision making. A case study and activity theory analysis, of an existing clinical knowledge-based computer system in routine use, helped to resolve a number of communication and methodological issues that the design team encountered. Sharing of backgrounds and perspectives caused the design team to question previous assumptions and to explore alternative functions and roles for knowledge-based computer systems in maternity care. We are now undertaking a longitudinal case study and activity theory analysis of obstetric teams and women in labour to analyse the relationships between clinicians, patients and technology. This work will inform the development of our knowledge-based computer system to place the patient at the centre of the decision-making process.     

Fault tolerant approach for biosignal-based robot control

This paper proposes a fault tolerant framework for biosignal-based robot control with multiple sensor electrodes. In this approach, to cope with sensor faults, a reliable joint torque estimation model is selected from a group of estimation models based on sensor failure classifiers. The correlation among the electromyography (EMG) signal streams is used as input feature vectors for fault detection. To validate our proposed method, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow-joint torque estimation experiments with five participants. When one EMG sensor electrode experiences one of the problems, the experimental results show that the joint torque can be estimated with significantly fewer errors using our proposed approach than a joint torque estimation method without sensor fault detection or than a method with a conventional sensor fault detection algorithm. Furthermore, we controlled a mannequin-arm-attached one-DOF exoskeleton based on the estimated torque profiles by generating movements with the estimated torque derived from the selected model.     

An organizational approach to designing an intelligent knowledge-based system: Application to the decision-making process in design projects

Knowledge-based engineering (KBE) approaches are designed to reduce the time and cost of product development by capturing, retaining and re-using design knowledge. They currently focus on repetitive design tasks where knowledge is considered as a static resource. However, knowledge is intrinsically linked to the organizations and people who use it. Thus, to be efficient, these knowledge-based systems (KBS) have to be able to take into account all the mechanisms of knowledge creation, sharing and evaluation made by the users. Using the agent paradigm, new knowledge-based systems can be designed in order to address this research issue. Indeed, the agents have social abilities and are able to achieve very complex tasks. These two features are necessary for making a knowledge-based system efficient. However, there still exists today a lack of approaches and methodologies to help design such applications. This paper presents DOCK, a methodology to design an intelligent knowledge-based system that aims to support the knowledge management process. In order to take into account all the mechanisms of knowledge generation, sharing and re-use, DOCK is based on the hypothesis that efficient modelling of human organizations, by highlighting their roles, collaborations, skills, goals and knowledge, will help the KBS designer to specify an adapted knowledge-based system. Finally, DOCK is implemented to design the SMA SNOTRA that is dedicated to supporting a decision-making process for design projects.     

Fault diagnosis of networked control systems

Networked control systems (NCS) are feedback systems closed through data networks. NCS have many advantages compared with traditional systems; however, the network-induced delay and other characteristics of data networks may degrade the performance of feedback systems designed without taking the network into account. Supported by the National Nature Science Foundation of China, we studied the fault diagnosis and fault-tolerant control theory for NCS in recent years. This paper summarizes our main ideas and results on fault diagnosis of NCS, including the fundamentals of fault diagnosis for NCS with information-scheduling, fault diagnosis approaches based on the simplified time-delay system models, and the quasi T-S fuzzy model and fault diagnosis for linear and nonlinear NCS with long delay.     

Fault tolerant control for switching discrete-time systems with delays: an improved cone complementarity approach

In this paper, fault tolerant control problem for discrete-time switching systems with delay is studied. Sufficient conditions of building an observer are obtained by using multiple Lyapunov function. These conditions are worked out in a new way, using cone complementarity technique, to obtain new LMIs with slack variables and multiple weighted residual matrices. The obtained results are applied on a numerical example showing fault detection, localisation of fault and reconfiguration of the control to maintain asymptotic stability even in the presence of a permanent sensor fault.     

Decision-making on pipe stress analysis enabled by knowledge-based systems

This paper presents engineering decision-making on pipe stress analysis through the application of knowledge-based systems (KBS). Stress analysis, as part of the design and analysis of process pipe networks, serves to identify whether a given pipe arrangement can cope with weight, thermal, and pressure stress at safe operation levels. An iterative process of design and analysis cycle is done routinely by engineers while analyzing the existing networks or while designing the process pipe networks. In our proposal, the KBS establishes a bidirectional communication with the current engineering software for pipe stress analysis, so that the user benefits from this integration. The stress analysis knowledge base is constructed by registering the senior engineers’ know-how. The engineers’ overall strategy to follow up during the pipe stress analysis, to some extent contained by the KBS, is presented. Advantages in saving engineering man-hours and usefulness in guiding experts in pipe stress analysis are the major services for the process industry. Matías Alvarado is a Research Scientist at the Centre of Research and Advanced Studies (CINVESTAV-IPN, México). He got a Ph.D. degree in computer science at the Technical University of Catalonia with a major in artificial intelligence. He has a B.Sc. degree in mathematics from the National Autonomous University of Mexico. His interests in research and technological applications include knowledge management and decision-making, autonomous agents and multiagent systems for supply chain disruption management, concurrency control, pattern recognition, and computational logic. He is the author of about 50 scientific papers, the Guest Editor of journal Special Issues on topics of artificial intelligence and knowledge management for the oil industry, and an Academic, invited to the National University of Singapore, Technical University of Catalonia, University of Oxford, University of Utrecht, and Benemérita Universidad Autónoma de Puebla. Miguel A. Rodríguez-Toral is a Chemical Engineer educated at the University of Edinburgh, U.K. (Ph.D.), UMIST, U.K. (M.Sc.), and UNAM, México (B.Sc.). He has 13 years of work experience at the Mexican Petroleum Institute (IMP) in the areas of engineering design of heat transfer equipment, cogeneration, and process engineering for the oil, gas, and petroleum refining industry. He is currently the topside leader of the Deepwater program at the IMP. He has interest in the applications of mathematical optimization and knowledge-based systems for the solution of process engineering and energy efficiency design problems. Armando Rosas Elguera is a Civil Engineer working at the IMP. He has 27 years of experience as a Specialist in flexibility and support of critical piping systems for the process industry. In 1979, he was a piping stress and flexibility Specialist, then an Office Head of piping flexibility, Coordinator and Representative of the IMP in the Laguna Verde project (a nuclear power plant in Mexico). He was also the Head of the pipe stress analysis department from 1994 to 1998. Currently, he is a Researcher in the applications of pipe stress analysis. He has deep practical experience in pipe stress analysis for nuclear power projects, for process and power plants involving all the different phases of engineering projects, from engineering design to plants start-up and operation. Sergio Ayala got a B.Sc. degree in civil engineering from the Mexican National Polytechnic Institute (IPN). He is now retired from the IMP. He has more than 30 years of industrial experience gained at the IMP in the area of pipe stress analysis of process plants. He has extensive practical experience in the engineering design and technical advice during start-up and operations of piping systems for the upstream and downstream sectors of the Mexican petroleum industry. He is a Senior Specialist in pipe stress analysis. He has interest in the applications of computer science for the implementation of a corporate memory in his area of speciality.     

Fault tolerance in partitioned manufacturing networks

Fault tolerance is especially important for computer systems that require a high degree of confidence. Computer Integrated Manufacturing (CIM) is an area where computer systems must not be disturbed by uncontrolled failures. This article deals with two problems that are related to fault tolerance and network partitions in automated manufacturing systems.The first problem relates to the distribution of information in partitioned data networks in CIM systems. We indicate how to overcome this problem by using the material network as a redundant data network:The second problem relates to fault detection and diagnosis in manufacturing systems. The problem is whether the indication of a fault means that a production unit itself has actually broken down, or that the indication is instead due to disturbances in the transmission of material. That is, the production unit continues to operate propcrly despite indications to the contrary. We describe how the material network can be used for detection and diagnosis.     

Fault estimation and fault tolerant control for linear stochastic uncertain systems

This article investigates the fault estimation and fault tolerant control (FTC) problems for linear stochastic uncertain systems. By introducing the fictitious noise, the fault is augmented as part of the systems state, and then a robust estimator is proposed to simultaneously obtain the state and fault estimation. Based on the estimated information, the active FTC is presented to eliminate the impact of the fault. Finally, a simulation example is conducted to demonstrate the effectiveness of our main method.     

Fault recoverability analysis of switched systems

This article provides an approach to evaluate the fault recoverability of switched systems (SS) under given energetic constraints. Such evaluation reveals the capability of the SS to tolerate the worst faults over a prescribed set under possible energy constraints. The evaluation is achieved by using the smallest eigenvalue of a proposed ‘switched-system controllability Gramian’. Simulation results about a longitudinal flight control process example are presented to show the effectiveness of the proposed method.     

Special cause management: A knowledge-based approach to statistical process control

In this paper we discuss a set of software tools developed to support the tasks associated with managing special causes of variation in a manufacturing process. These tasks include the detection of significant changes in process variables, a diagnosis of the causes of those changes, the discovery of new causes, the management of performance data, and the reporting of results. The software tools include automatic recognition of out-of-control features in critical process variables, rule-based diagnosis of special causes, a model-based search for symptoms where a diagnosis is not possible, and automated reporting aids. It is hoped that these tools will enhance the efficiency of special cause management.     

Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems.     

Optimal control of nonlinear systems: a predictive control approach

A new nonlinear predictive control law for a class of multivariable nonlinear systems is presented in this paper. It is shown that the closed-loop dynamics under this nonlinear predictive controller explicitly depend on design parameters (prediction time and control order). The main features of this result are that an explicitly analytical form of the optimal predictive controller is given, on-line optimisation is not required, stability of the closed-loop system is guaranteed, the whole design procedure is transparent to designers and the resultant controller is easy to implement. By establishing the relationship between the design parameters and time-domain transient, it is shown that the design of an optimal generalised predictive controller to achieve desired time-domain specifications for nonlinear systems can be performed by looking up tables. The design procedure is illustrated by designing an autopilot for a missile.