ashutosh%euler.Berkeley.EDU@UCBVAX.BERKELEY.EDU.UUCP (02/03/87)
CS 298 Seminar Expert Systems for Diagnostic and Control in Manufacturing Prof. Alice M. Agogino Dept. of Mechanical Engineering, UC Berkeley 608-7 Evans, Tuesday Feb.3, 5 - 6 pm. Abtract : An architecture for the hierarchical integration of sensors and diagnostic reasoning in expert systems for automated manufacturing and process control is described. The system architecture uses influence diagrams to provide a symbolic representation of the knowledge obtained from experts with varying degrees of technical proficiency and from diverse domains of expertise. The symbolic representation also maps to a functional level of knowledge which can be used by the knowledge acquistion system to obtain a more detailed numerical level of information from experts , maintenance records, statistical data bases or sensor signals. The diagnostic implementation uses probailistic inference to answer questions concerning possible failures in an automated manufacturing or process system based on observable sensor readings. A search through the influence diagram network provides the topological solution or calculation sequence to answer any such diagnostic query. Once the topological and numerical solution to the influence diagram has been determined, qualitative and quantitative advice can be relayed to the controller , operator or diagnostician. A description of an implementation of such an architecture will be provided.
SECAM@SIERRA.STANFORD.EDU.UUCP (02/26/87)
Jane Frederick Friday 27 February G.E. Industrial Automation Systems Terman 556 1:30-3:00pm "Expert Systems in Electronic Manufacturing" Manufacturing appears to be one of the fertile fields for expert system applications. The tasks are bounded and repetitive in nature. There exists a set of experts which regularly perform the tasks. These tasks can be defined in process steps and last but not least, manufacturing is a direct pay point. The payback for quality and productivity improvements can be specifically determined. This last issue is very important and often overlooked, but expert systems development and implementation is an expensive and ongoing process. Therefore, one of the challenges for expert systems in manufacturing is selecting the correct application and the one with the greatest payback. Refreshments will be served. -------