Artificial intelligence techniques applied to vehicle management system diagnostics

The User's Manual provides the user with enough information to allow him to use the program which resides on National CSS Timeshare. Detailed information is conveyed by the use of several simulation examples and an explanation of the results of these examples (on a line-by-line basis) as required. Except for a brief description of the road load equations, this User's Manual does not include descriptions of the math models. A complete report of the ELVEC simulator is being prepared by the Vehicle Systems Modeling Task Area Team of the Electric nd Hybrid Vehicle System Research and Development Project at the Jet Propulsion Laboratory. This report should be available to those wishing a complete program description by October 1978. However, it is not necessary to possess this information in order to conduct electric and hybrid vehicle performance simulations using ELVEC. Flow charts and source listings are available for those wishing to review the coding. This information should be requested through T.A. Barber, Project Manager, Electric and Hybrid Vehicle System Research and Development Project, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91103.

Scenarios are described which have been generated in support of the Hybrid Vehicle Potential Assessment Task under the JPL Electric and Hybrid Vehicle Systems Research and Development Project. The primary function of the scenario generation is to develop a set of consistent and credible forecasts required to estimate the potential impact of hybrid vehicles on future petroleum consumption in the USA, given a set of specific electric, hybrid and conventional vehicle designs. The forecasts are limited to the next 32 years (1978 to 2010. The four major areas of concern are: population and vehicle fleet size; travel patterns and vehicle fleet mix; conventional vehicle technology (Otto baseline); battery technology; and prices. The forecasts have been generated to reflect two baseline scenarios, a Petroleum Conservation Scenario (Scenario A) and an Energy Conservation Scenario (Scenario B). The primary assumption in Scenario A is higher gasoline prices than in Scenario B. This should result in less travel per car and an increased demand for smaller and more fuel efficient cars (compared to Scenario B). In Scenario B the primary assumption is higher prices on cars (new as well as used) than in Scenario A. This should lead to less cars (compared to Scenario A) and a shift to other modes of transportation.

A real-time expert system is being developed using 0PS83 rule based programming system for monitoring and analysis, diagnosis of faults and malfunctions, and on-line optimization of fermentation processes. The main purpose of the expert system is to assist the operators in running and improving fermentation conditions with minimum amount of supervision. The expert system consists of a data base which includes physical attributes of the process (color, and general appearance of the fermentation broth) and includes operator/user given off-line data like microscopic states of the microorganisms. Optimization and estimation algorithms can be interfaced with the expert system. These can be used in conjunction with the fault detection capabilities of the expert system for making intelligent decisions about the abnormalities in fermentation experiments, and rule out contradictory sensor information.

This paper uses two modeling tools to analyze the reliability of real-time expert systems: (1) a stochastic Petri net (SPN) for computing the conditional response time distribution given that a fixed number of expert system match-select-act cycles are executed, and (2) a simulation search tree for computing the distribution of expert system match-select-act cycles for formulating a control strategy in response to external events. By modeling the intrinsic match-select-act cycle of expert systems and associating rewards rates with markings of the SPN, the response time distribution for the expert system to reach a decision can be computed as a function of design parameters, thereby facilitating the assessment of reliability of expert systems in the presence of real-time constraints. The utility of the reliability model is illustrated with an expert system characterized by a set of design conditions under a real-time constraint. This reliability model allows the system designers to: (1) experiment with a range of selected parameter values; and (2) observe their effects on system reliability.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Private Sector-led Urban Development Projects. Management, Partnerships and Effects in the Netherlands and the UK

A real-time expert system has recently been installed at British Steel Sections, Plates and Commercial Steels, Scunthorpe Works. The system is at the bloom and billet mill and has been designed to allow engineers to add and modify rules through a generic graphical rule editor. The mill has 26 soaking pits which reheat the steel prior to rolling. The expert system covers the soaking pits and the gas mixing station. It currently diagnoses operational problems with the soaking pits and generates messages which are displayed to the process operator via an existing message screen. Combustion and heat balance information is also calculated by the system for each pit every five minutes. Prior to installing the expert system, the calculations were performed manually for individual pits on an irregular basis. The on-line calculations are to be used to derive new diagnostic rules and provide supporting evidence to existing rules. (2 pages)

Fault prediction and fault compensation are beneficial for the production technology and give a new dimension to fault diagnosis in technical systems. The overall goal of this paper is the presentation of fault prediction and fault compensation procedures as they are studied, implemented and embedded in a real time expert system. This expert system detects and diagnoses faults in hydraulic systems. For this purpose dynamic modelling information, on-line sensor information, special features of the domain of hydraulic systems and expert systems technology are used co-operatively.

Information systems development projects are a significant expenditure of time, effort and money for many enterprises. Historically it has been estimated that 50-80% of projects fail to achieve their objectives for a variety of reasons. Researchers have identified numerous factors associated with system development failure. In this paper, we first synthesize the vast research regarding systems development risk factors and provide a framework that illustrates interactions between risk factors. The framework was used to develop an open-ended questionnaire that was answered by an inter-industry group of experienced systems development engineers and project managers. Analysis of their reports indicates that experienced professionals perceive that all risk factors (technical, resource, etc.) ultimately derive from organizationally-oriented factors, to be solved with organizational responses. This holistic viewpoint of risk assessment is counter to that of systems professionals more involved in day-to-day development decision making. For these developers, risks are more likely to be characterized as fitting into traditional discrete categories. This apparent dichotomy of risk importance was further investigated through an intra-organizational study which directly assessed how professionals recognize and treat risks in the development process. Results illustrate that a successful project environment may be characterised as one in which all systems professionals maintain a holistic view of organizational risk and that organizational culture, as opposed to experience, may predicate such an environment. Implications and future research directions are discussed.

The Gensym real-time expert system G2 integrates dynamic models and rules to combine analytic and heuristic knowledge representation. The inference engine can reason about object connectivity and can use generic forms of knowledge, interpreted for specific instances in the domain. Designed for process control and related applications, G2 allows the process engineer to implement and manage the expert system. A network architecture allows hierarchical or co-operating expert systems in the plant.The first Expert Control System using G2 is described. The Reliable Water Company in Boston has implemented G2 for control and on-line diagnostics for a water purification process. The system was first demonstrated in June 1987, in prototype form, purifying water from the Boston Harbor. Delivery versions are now being deployed.

A Hybrid Expert System, GIS, and Simulation Modeling for Environmental and Technological Risk Management

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents the way that expert system technology has been used for the development of a consulting tool in the National Technical University of Athens, aiming to support those who want to apply Production Planning and Control (PPC) software in their manufacturing firm. In the knowledge base that has been created, both a production systems taxonomy and a generic software package with specific modules and functions are presented. The inferences are based on rules, that express the need for the existence of certain software, when a set of production systems characteristics is found in the production system that is under consideration. The results that are given by the expert system include the whole architecture of PPC software package that is applicable in a certain manufacturing setting.KeywordsProduction SystemExpert SystemSemantic NetworkCustomer OrderSoftware FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Vehicle breakdowns are a growing problem worldwide, often caused by overheating, oil leaks, battery problems, flat tires, fuel system failures, and other issues. These incidents frequently result in delays, safety hazards, and costly repairs. Existing systems mainly focus on locating nearby mechanics but lack self-diagnostic capabilities. This study presents a mobile-based expert system that offers step-by-step repair instructions, troubleshooting flowcharts, and safety guidelines. The system integrates ensemble machine learning models and rule-based inference to empower users to independently diagnose and resolve minor vehicle faults. The system is designed with offline capability and a user-friendly interface, this tool ensures accessibility and reliability, especially in remote areas. Initial testing demonstrated a classification accuracy of 88% in diagnosing common faults, confirming the system’s effectiveness and potential for real-world deployment.

Expert systems, a branch of artificial intelligence (AI), are showing tremendous potential for application to power electronics and AC motor drive systems. The paper describes expert system-aided automated design, simulation and controller tuning of an AC motor drive system. It is an extension of earlier work that discussed the design and optimization of power converter systems. Based on the application and performance specifications from the user, the expert system selects a motor drive configuration consisting of the AC machine type, power converter and control topologies. The expert system embeds the design expertise of an experienced AC motor drive system designer to configure, design and fine-tune the AC motor drive system. Next, the expert system supervises an autonomous hybrid simulation where the controller software is fine-tuned in real-time before loading to an experimental AC motor drive system. The paper discusses the design methodology for AC motor drive systems, with particular emphasis on the configuration selection, AC motor sizing and control design and tuning. The expert system based control design is experimentally verified on an indirect vector controlled induction motor drive system with synchronous current control, using a 5 hp IGBT inverter, with the control implemented by a Texas Instruments DSP 320C25. The same principles can be extended to larger and more complex AC motor drive system applications.

This paper introduces a new approach to the power system restoration problem. The continuous restoration means an online function that supports all the dispatcher actions. It is active and alerts not only in the restorative network state, but also in normal state. This real time expert system is strongly based on the cooperation with the SCADA-EMS functions and the different offline network calculations. The idea is proposed to be implemented in the Hungarian National Control Centre.