CIM (computer-integrated manufacturing) is viewed as an emerging technology in the domain of manufacturing. The concept of CIM is that the whole performs better than the sum of the individual parts. In order to make a system run smoothly with minimum delay (i.e., continuous flow manufacturing) it is necessary to have a diagnostic system for discovering any cause of system failure. It is desirable that this diagnostic system be capable of performing its task as fast as possible since the duration of the system's down time is very closely related to productivity and cost. Thereby, there is a need for intelligent diagnostic systems. In artificial intelligence and expert systems, diagnosis has been given more attention in recent years, including trouble-shooting in electronic circuits and medical diagnosis.
There have been a variety of approaches taken by various researchers for creating intelligent diagnostic expert systems. These approaches range from shallow reasoning using compiled rules to model-based deep reasoning systems that reason by exploiting causal, structural, and functional relationships. Some approaches even combine shallow and deep knowledge reasoning. It has been hypothesized by some that the use of deep representations of entities to be diagnosed is superior to empirical knowledge about associations between malfunctioning parts of an entity and symptoms. The rationale for such a conclusion is that one could not exhuastively catalog all such associations, and without such a catalog, a heuristic-based diagnostic system becomes brittle and fails when presented with a case that it does not understand.
On the other hand, deep knowledge representation is based on models that are difficult to construct, especially models that exhibit the technology intent of the designer. Further, it is unlikely that models will mirror the future behavior of entities of any complexity, particularly with regard to providing information about multiple perspectives. Moreover, models are likely to be domain specific and only some fraction of knowledge will be transferable from one diagnostic system's knowledge base to another.
It is also recognized that rule-based systems become increasingly difficult to understand and maintain as the number of rules grow. While a reasonable rule-based expert system shell can assist a domain expert in formulating cause-and-effect rules, a collection of such rules typically will not function as an expert system, except for the most simple cases. To overcome these limitations, some expert system shells allow the encoding of strategic and object-level knowledge as meta-rules. However, this requires extensive knowledge of the programming paradigm and the development environment. Another class of tools provides a search algorithm for a flat problem-space representation. Although the problem representation is simplified, the search complexity for a problem of solution length L, and a search space branching factor B has a worst case complexity O(B.sup.L).
As an alternative, it has been suggested that it is actually more fruitful in certain domains to concentrate on constructing models of belief organization for diagnosis rather than models of physical entities. The concept of belief potentially has a wider scope than explicitly defined knowledge. A method of organization of beliefs has been proposed for diagnostic problems that provides explicit belief organization with implicit organization of knowledge about physical device characteristics, functionality, and behavior. The method is claimed to provide reasoning about belief among alternatives, is extensible, and can be scaled to problem size. Further, it is asserted that belief manipulation coupled with information about fault history, events, and symptoms is sufficient to secure a good diagnostic result. The approach presented herein modifies and combines behavioral knowledge presentation with structural knowledge presentation to identify a recommended action.
It has long been recognized that hierarchical problem solving can be used to reduce search space within an expert system. It has been shown that the complexity of search space for a problem of solution length `L` and search space branching factor `B`, is reduced from O(B.sup.L) for single-level representation, to O(.sqroot.L) B.sup..sqroot.L for two-level representation, and to O(L) for multi-level representation. This analysis is based on the assumption that the abstraction divides the problem into O(L) constant size problems that can be solved in order, without backtracking. It can be shown that even in domains that do not satisfy the assumption, the use of hierarchical problem solving still produces significant reductions in search space. Hierarchical problem solving and knowledge processing techniques have been used in several reported expert systems. The concepts presented herein provide enhancements over such prior techniques.
Experienced engineers, technicians, and system operators typically develop their own diagnostic flowcharts, procedures and shortcuts over time. Such materials are utilized during daily activities, and only occasionally do such individuals refer to basic principles and documented diagnostic procedures to solve problems. This has been a natural response to the large volume of maintenance and operation manuals offered by tool vendors and process developers of a manufacturing enterprise. Further, the majority of expertise accumulated by technicians and operators is usually not shared among different teams and can be lost due to transfers or job changes. In addition, local upgrades/modifications, which might not be explained in a vendor manual, can be scattered among personal notebooks or other notes near the machines or technician workbenches. The challenge of knowledge engineering in such an environment is to accommodate various sources of expertise and to guide and influence the expert toward considering all aspects of the environment beyond the individual's usual activities and concerns.
The knowledge acquisition task is often complicated by the fact that human experts have not analyzed the contents of their thoughts, so that they are not explicitly aware of the structure of their knowledge. As a result, the intermediate steps and the reasoning seems obvious to them and they cannot clearly provide an overall account of how decisions are made, at least not at a level of detail required for expert system development. There are a number of approaches to knowledge acquisition. The three basic approaches are interview, interaction (supervised) and induction (unsupervised).
Historically, interview has been the most prevalent method of knowledge acquisition. It is, however, highly dependent on the knowledge engineer, is often time consuming and expensive, and is typically viewed as the knowledge acquisition bottleneck. It is also recognized that for knowledge intensive and task oriented applications, supervised knowledge acquisition is more efficient. Large database and data intensive applications are good candidates for unsupervised knowledge acquisition.
On the other hand, many expert systems use task-oriented shallow knowledge. In knowledge acquisition it is sometimes necessary to carry out excursions into deep knowledge in order to understand and validate associated shallow knowledge. This requires involving experts in knowledge acquisition, since they are in a position to understand the deep knowledge and its relation to the shallow knowledge. In addition, studies performed on the quality, efficiency, and accuracy of knowledge bases have shown that knowledge bases developed by the expert (versus the knowledge engineer) tend to be smaller, provide larger number of pathways associating evidence to diagnostic hypothesis, include more critical attributes, and provide richer clusters of knowledge. This is another argument in favor of providing domain experts with a larger role in knowledge acquisition.
As a related problem, existing multimedia training applications are typically developed by a media production specialist, and are usually delivered on read-only storage medium (CD-ROM, video disk). Such applications exhibit huge initial start-up costs, do not provide any intelligent feedback or control, and require specialized hardware and/or software, meaning that any modification and/or upgrade will be expensive and time consuming.
Manufacturing enterprises can employ hundreds and sometimes thousands of permanent and temporary system operators and technicians. Currently, a multitude of methods are used for educating and assisting such manufacturing personnel. These methods include informal, unstructured training sessions, printed `in-house` manuals, classroom instruction, and walk-through orientations to name but a few. In the manufacturing environment, the student population grows with every group of new employees, whether temporary or permanent. This student group is typically large and decentralized and cannot be released from the manufacturing line simultaneously to attend a standardized training course.
Interactive multimedia training and certification can present consistent subject matter, on a flexible twenty-four hour, seven day per week schedule. The subject matter delivered to the trainee is guaranteed to be consistent, thus avoiding reliance on a thorough presentation of material by a knowledgeable technician. Use of an on-line computer based training methodology would eliminate a back-level problem. Currently, with the use of printed documents there is always an uncertainty as to whether the line operator is using the most current version or whether an operator's training in fact covers a latest version of a processor tool upgrade. By making the information available on-line, positive control over the information being disseminated is obtained.
Computer-based training and intelligent tutoring systems are typically based on a single, and rather simple user model. Regardless of the familiarity or lack of familiarity of the trainee with the topic, all trainees go through the same training process. A new employee who has never worked in a similar environment is given the same training material as an employee who has many years of related experience and might have been transferred to a new assignment from a similar area. Certification and qualification procedures are usually conducted orally in an ad-hoc fashion and are subject to a trainer's judgment and biases. In addition, there is no formal methodology for increasing the responsibility of a trainee as the training proceeds. In order for a computer-aided training system to acquire the necessary flexibility, it should distinguish between several types of students and structure the training material according to a student's needs and background.
Multimedia in general, and motion video, animation and audio in particular, are believed very appealing to personnel involved in maintenance, diagnostics and training applications. However, in many cases a major drawback is the cost and logistics of hardware and software required to implement such a multimedia system. This is complicated by the fact that most manufacturing facilities include tens if not hundreds of workstations. If an application requires special hardware and/or software licensing, then cost becomes significant.
All the above drawbacks and problems associated with existing expert systems and multimedia/hypermedia applications are addressed by the systems and methods presented herein.