A Fuzzy Petri Nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system

Railway traffic control by dispatchers in case of abnormality is critical to assure the service quality of a railway system’s operation. However, this unique professional knowledge often lies in the dispatcher’s mind. Therefore, this study aims to transform a train dispatcher’s expertise into a useful knowledge rule. The fuzzy Petri Net approach is adopted to formulate the decision rules of train dispatchers in case of abnormality as the basis for future development of a dispatching decision support system. The dispatching decision rules, factors, and possible options when perturbation happens are collected via expert interviews and literature reviews. This study discusses the abnormal scenarios, including centralized traffic control system failure, automatic train protection failure, and locomotive failure. A case study of a line section of Taiwan’s railway network is implemented and the empirical result could be used as a reference in railway dispatching in case of abnormality.     

An integrated expert system/operations research approach for the optimization of natural gas pipeline operations

This paper reports on a project, conducted jointly between SaskEnergy/Transgas and the University of Regina, which aims at developing an integrated decision support system for the optimization of natural gas pipeline operations. In this integrated approach, both expert systems and operations research techniques are used to model the operations of the gas pipelines. The decision support system can perform the tasks of (1) determining the state of the line pack of the pipelines and recommending the control commands to be issued, (2) determining the associated horsepower requirement, and (3) determining the specific compressor unit to be turned on or off. The first two tasks are performed by an expert system, and the third by a fuzzy programming model. The expert system has been developed on G2 and validated using a simulation program.     

基于长输管道SCADA运行数据的诟控操作分析与研究

长输管道SCADA系统建设日趋成熟,在各级油气管道调控中心也搭建了数据小心,实现了对多条管道SCADA运行数据的汇聚,积累了大量的管道生产运行数据,这些数据详实地记录了管道生产运行和调控操作的全过程。而调控中心面临着如何对管道调控操作规律、篱道复杂度和调度漪操作水平进行科学评估与分析的难题。本文介绍了采用数据挖掘中描述性分析技术,基于海量的长输管道SCADA运行数据,对调控操作基础数据进行工况判断规律的提取、分析和完善,最终识别判断出调度人员在各种工况下操作全貌,井基于工况自动判断结果进行各种纬度的统计分析,为调控运行管理提供科学的决策支持数据和有效分析方法。     

An agent-based support system for railway station dispatching

For those railway stations without being automated, railway traffic dispatching still depends on dispatchers, especially under disturbed circumstances. In this study, an agent-based support system, named D-Agent, is developed to assist human dispatchers to make decisions in station operation. To this end, the common knowledge and possible difficulties concerning a station dispatcher in his/her routine work are firstly studied, and the D-Agent is proposed with the purpose of working out practicable solutions to these challenging tasks as a dispatcher does. Then the general model of the D-Agent is established, containing five basic modules: local database, knowledge base, skill base, reasoning mechanism and communication interfaces. The internal skills of the D-Agent are designed to execute various tasks in different scenarios. Besides, a skill extension of the D-Agent with mathematical formulations is particularly discussed in this paper, to find feasible and optimal traffic control solutions in disturbance situations such as train delays and route conflicts. The D-Agent is designed to learn from its own experimental history in applying different skills, and evaluate the skills by preference weights of alternative solutions in a particular task. This procedure allows the agent to have potential for continuous improvement. To verify the applicability of the proposed support system, a D-Agent for a terminal station of subway is simulated. The numerical example of train delays and route conflicts shows that the D-Agent can generally perform as a station dispatcher in fulfilling the specific tasks, estimate the traffic state in different operation strategies and support the decision-making of favored solutions. Significantly, it indicates that the mathematical methods can also been employed by an intelligent agent.     

Applications of artificial intelligence for optimization of compressor scheduling

This paper presents a feasibility study of evolutionary scheduling for gas pipeline operations. The problem is complex because of several constraints that must be taken into consideration during the optimization process. The objective of gas pipeline operations is to transfer sufficient gas from gas stations to consumers so as to satisfy customer demand with minimum costs. The scheduling involves selection of a set of compressors to operate during a shift. The scheduling decision has to be made so as to satisfy the dual objectives of minimizing the sum of fuel cost, start-up cost, the cost of gas wasted due to oversupply, and satisfying minimal operative and inoperative time of the compressors. The problem was decomposed into the two subproblems of gas load forecast and selection of compressors. Neural networks were used for forecasting the load; and genetic algorithms were used to search for a near optimal combination of compressors. The study was conducted on a subsystem of the pipeline network located in southeastern Saskatchewan, Canada. The results are compared with the solutions generated by an expert system and a fuzzy linear programming model.     

Energy dispatch controllers for a photovoltaic system

In this paper two energy dispatch controllers for use in a grid-independent photovoltaic (PV) system are presented. The first, an optimal energy dispatch controller, is based on a class of Adaptive Critic Designs (ACDs) called Action Dependent Heuristic Dynamic Programming (ADHDP). This class of ACDs uses two neural networks to evolve an optimal control strategy over time. The first neural network or “Action” network dispenses the actual control signals while the second network or “Critic” network uses these control signals along with the system states to provide feedback to the action network, measuring performance using a utility function. This feedback loop allows the action network to improve behavior over time. The optimal energy dispatcher places emphasis on always meeting the critical load, followed by keeping the charge of the battery as high as possible so as to be able to power the critical load in cases of extended low output from the PV array, and lastly to power the non-critical load in so far as to not interfere with the first two objectives. The second energy dispatch controller is a smart energy dispatch controller and is built using knowledge from an expert, codified into a series of static rules. This smart energy dispatch controller is called the “PV-priority 2” controller. These energy dispatchers are compared with a static scheme called the “PV-priority 1”. The PV-priority 1 controller represents the standard control strategy. Results show that the ADHDP-based optimal energy dispatcher (or controller) outperforms the standard PV-priority 1 energy dispatcher in meeting the stated objectives, but trails the PV-priority 2 energy dispatcher. However, the major advantage of the ADHDP controller is that no expert is required for designing the controller, whereas for a rule-based controller such as the PV-priority 2 controller, an expert is always required.     

Eliciting knowledge and transferring it effectively to aknowledge-based system

The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves diverse activities. The complete development of a decision support system using knowledge acquisition tools is illustrated. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering     

A decision support system for real-time monitoring and control of dynamical processes

This article presents the concept and development of a prototype diagnostic decision support system for real-time control and monitoring of dynamical processes. This decision support system, known as Diagnostic Evaluation and Corrective Action (DECA), employs qualitative reasoning, in conjunction with quantitative models, for monitoring and diagnosis of malfunctions in dynamical processes under routine operations and emergency situations. DECA is especially suited for application to time-constrained environments where an immediate action is needed to avoid catastrophic failure(s). DECA is written in common Lisp and has been implemented on a Symbolics 3670 machine; its efficacy has been verified using the data from the Three Mile Island No. 2 Nuclear Reactor Accident.     

Management of imprecision and uncertainty for production activity control

The operational levels of production management, often called production activity control (PAC) or manufacturing process control, require increasing reaction capabilities in order to adapt the workshop management to the changes of its environment. It often implies giving more responsibilities to the low decision levels. However, the management of the corresponding degrees of freedom is generally unusual. In such a situation, decision support systems (DSSs) provide a way to reconcile the satisfaction of mid-level objectives and the reaction requirements. A conceptual model is described that provides a design framework for a PAC DSS. Since the available knowledge lies mainly in expertise, a DSS has been implemented using a knowledge-based system. The uncertainty and imprecision of the managed information led to the use of fuzzy logic as a modeling tool. Moreover, various inference semantics have been implemented in the expert rules because different kinds of reasoning have been identified. Two versions of the DSS are described and several examples of implemented reasoning processes are developed.     

A hybrid diagnostic-advisory system for small and medium-sized enterprises: A successful AI application

We describe a hybrid expert diagnosis-advisory system developed for small and medium enterprises. The Performance, Development, Growth (PDG) system is completely implemented and fully operational, and has been successfully used on real-world data from SMEs for several years. Although our system contains a great deal of the domain knowledge and expertise that is a hallmark of AI systems, it was not designed using the symbolic techniques traditionally used to implement such systems. We explain why this is so and discuss how the PDG system relates to expert systems, decision support systems, and general applications in AI. We also present an experimental evaluation of the system and identify developments currently under way and our plans for the future.     

Value-driven expert systems for decision support

Values are an inherent part of all decision processes. Hence, values are at least implicity included in all expert systems intended for decision support. This paper outlines the concepts and methodology, which are based on the principles and procedures of decision analysis, to address explicity the values in an expert system logically and consistently. Implementation of the concepts and methodology involves the elicitation of values using the same general approach as that used by knowledge engineers to explicate expert knowledge.     

An expert system for real-time fault diagnosis of complex chemical processes

This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. The knowledge base structure, representation of knowledge, and access to expertise are technically considered. Industrial applications to the fluid catalystic cracking process in refinery indicate that the expert system diagnoses abnormal events efficiently and promptly.     

Expert system to control and to design closed loop conveyor systems

A conveyor system is an important part of a manufacturing system. As such, the conveyor system must comply with all the requirements of a modern manufacturing system: high flexibility, high efficiency, and high speed—smart reasoning processes to generate future positions based on a given current status. Because a huge number of figures and numerical manipulatuions are associated with the conveyor systems operations, the traditional numerical control techniques cannot satisfy the requirements to initiate and to control operations. New techniques based on very efficient reasoning processes are required. This article discusses an expert system that consists of a knowledge base and an inference engine that was developed to control a converyor system real-time operation. The computer runs that were performed during this research lead to the conclusion that the developed expert system can be employed to control the conveyor system real time operations very effectively. The developed expert system is considered as a reliable simulator of a conveyor system which can be implemented to explore parameters interrelationships at the phase of system design. Computer runs were performed to analyze the interrelationships between operational parameters which characterize the explored conveyor system. The expert system was programmed in a way that provides a generic simulator, which can be employed in a large variety of conveyor systems.     

VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table

This paper proposes an expert system called VIBEX (VIBration EXpert) to aid plant operators in diagnosing the cause of abnormal vibration for rotating machinery. In order to automatize the diagnosis, a decision table based on the cause-symptom matrix is used as a probabilistic method for diagnosing abnormal vibration. Also a decision tree is used as the acquisition of structured knowledge in the form of concepts is introduced to build a knowledge base which is indispensable for vibration expert systems. The decision tree is a technique used for building knowledge-based systems by the inductive inference from examples and plays a role itself as a vibration diagnostic tool. The proposed system has been successfully implemented on Microsoft Windows environment and is written in Microsoft Visual Basic and Visual C++. To validate the system performance, the diagnostic system was tested with some examples using the two diagnostic methods.     

Inference and learning methodology of belief-rule-based expert system for pipeline leak detection

Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for pipeline leak detection.     

A hierarchical expert system for integrated scheduling of ship berthing, discharging and material transport

Abstract: Berthing ships at a port and unloading the stowed materials require a series of scheduling problems: ship berthing, discharging, and material transport. To deal effectively with the scheduling complexity due to mutual interdependence among those problems, this paper proposes a two-level hierarchical architecture for the integrated scheduling of all the activities arising in port. The hierarchical architecture assigns ships to multiple lower level dispatchers, each of which makes its own discharging and material transport schedule independently while satisfying the requirements imposed by the higher level coordinator. If any problem occurs within a dispatcher, the higher level coordinator attempts to resolve the trouble through the coordination of other dispatchers. Based on the hierarchical architecture, a prototype scheduling expert system is developed using G2 for the port scheduling problem at a steelworks. Through the object- oriented interaction of frames, the system is shown to effectively construct integrated schedules from the berth scheduling to the material transport scheduling.     

An expert system for unit commitment and power demand prediction using fuzzy logic and neural networks

Abstract: The paper discusses the implementation of a fuzzy logic and artificial neural networks approach to providing a structural framework for the representation, manipulation and utilisation of data and information concerning prediction of power demand and generation commitments. An algorithm has been implemented and trained to predict the power demand at each load point on an hourly basis. The neural network is then implemented to supply the brute force necessary to accommodate the large amount of sensory data to provide the initial evaluation of the generation units to be committed. Results of the fuzzy model show a reasonable correspondence with the actual power demand. A standard deviation error for an hourly based prediction is limited to 4.4. Further refinement of the fuzzy model may produce further improvements.
Implementation of artificial neural networks for scheduling an hourly unit commitment based on load demands is also discussed The backpropagation technique based on the I/O mapping method has been chosen for structuring the neural network. Geographically related load points and generating units are clustered into groups. Grouping has significantly reduced the number of inputs and outputs to the neural network and, hence, reduced the system complexity. As a result, both training requirements and running real time interaction are significantly improved. The expert system would replace and utilise the requirement for skilled dispatchers in scheduling the generators. It is anticipated that this facility is more accurate, dynamic, adaptive and more efficient than a skilled dispatcher. The overall cost of power generation is expected to be less if the new facility is used. Initial results have reflected a satisfactory correlation between predicted and actual results, with a standard deviation error of 1.71% and 1.96% in the base load units of HTPS and ATPS respectively.     

On the limits of expert systems and engineering models in process control

The review is based on an analysis of current literature of expert systems and of system engineering models in dynamic process control. It starts with an analysis of the mental operations and cognitive requirements needed for supervisory control. Mental models are discussed as a function of situational requirements as well as of personal strategies. Systems engineering models and expert systems are briefly described and their function as decision support tools evaluated. Criteria are the overall functionality, similarity of knowledge bases and reasoning strategies of the human and the support system, adaptability to the operator's skill level and self-explanation of the support system in the interaction mode. As a result, system engineering models are only of limited value for knowledge-based process control. Expert systems seem to be very valuable tools for augmenting human decision making in process control, if the interaction problem can be solved.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

An integrated expert system/operations research approach for the optimization of waste incinerator siting problems

This research aims at developing an integrated decision support system for the optimization of waste incinerator siting problems. In this integrated approach, both expert system and operations research techniques are used to model the siting problems of waste incinerator. Furthermore, an expert decision support system (EDSS) is implemented for the above problem and thus providing the decision makers a useful tool for decision-making. This EDSS is based on multi-criteria decision analysis in finding the best incinerator site by minimizing costs and environmental impacts. The proposed approach identifies a hierarchy of objectives for the siting problem. First of all, several potential sites need to be screened as a set of feasible alternative sites. Second, those alternative feasible sites will be further evaluated via the multi-criteria decision making methods. For the evaluation process, we solve a 0/1 combinatorial optimization problem at the upper level and proceed the multi-attribute utility function at the lower level to get the optimal solutions. An empirical application of a real world waste incinerator site selection existing in Taichung City, Taiwan is followed in the end. Computational results both of the cost minimization and of the whole systems are also provided.