Quality control & inspection: Knowledge-based quality control system

The majority of Statistical Quality Control (SQC) microcomputer systems/packages are based on the idea of storing data and producing results such as QC charts from which a user may be able to make inferences regarding Quality. The selection of the type of control charts (e.g. charts for variables vs. charts for attributes) and interpretation of the results are usually left to the user. This is mainly due to the fact that SQC packages are generic systems while interpretations may be product specific.

Presented in this paper is a system which tries through dialogue with the user to direct him to the proper chart(s) in the package. An analysis component for each chart determines statistical phenomena (both good and bad) and provides general explanations. Another component in the system accesses an accompanying knowledge database (DB) — keyed by phenomena — which provide possible explanation as well as advise. The user may add to the knowledge base at any time assisted by the statistical DB Management Subsystem.

The elements of the system are the IIE Microsoftware Statistical Quality Control package and a number of “add-on” routines supporting DB creation and the inference engine.     

A review of neural networks for statistical process control

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.     

Statistics with fuzzy data in statistical quality control

Statistical quality control (SQC) is an important field where both theory of probability and theory of fuzzy sets may be used. In the paper we give a short overview of basic problems of SQC that have been solved using both these theories simultaneously. Some new results on the applications of fuzzy sets in SQC are presented in details. We also present problems which are still open, and whose solution should definitely increase the applicability of fuzzy sets in quality control.     

Applying a coupled expert system to quality control charts

Quality control charts are a common tool used for control of a manufacturing process. Expert systems can serve as a valuable tool to automate the different phases in the use of quality control charts. The authors have developed a coupled expert system to select a proper control chart, optimize the design of the selected control chart, analyze the chart to identify an out-of-control process and suggest the reason(s) for an out-of-control process. The intent of this paper is to discuss the main features of this coupled expert system.

An expert system shell called PCPLUS helps a user select the most appropriate control chart and identify the reason(s) for a process to be out-of-control. FORTRAN was used to implement the modules for design, plotting, and analysis of the selected control chart.     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

A new approach to detecting the process changes for multistage systems

The study aims to develop a new control chart model suitable for monitoring the process quality of multistage manufacturing systems.Considering both the auto-correlated process outputs and the correlation occurring between neighboring stages in a multistage manufacturing system, we first propose a new multiple linear regression model to describe their relationship. Then, the multistage residual EWMA and CUSUM control charts are used to monitor the overall process quality of multistage systems. Moreover, an overall run length (ORL) concept is adopted to compare the detecting performance for various multistage residual control charts. Finally, a numerical example with oxide thickness measurements of a three-stage silicon wafer manufacturing process is given to demonstrate the usefulness of our proposed multistage residual control charts in the Phase II monitoring. A computerized algorithm can also be written based on our proposed scheme for the multistage residual EWMA/CUSUM control charts and it may be further converted to an expert and intelligent system. Hopefully, the results of this study can provide a better alternative for detecting process change and serve as a useful guideline for quality practitioners when monitoring and controlling the process quality of multistage systems with auto-correlated data.     

Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development

Principled development techniques could greatly enhance the understandability of expert systems for both users and system developers. Current systems have limited explanatory capabilities and present maintenance problems because of a failure to explicitly represent the knowledge and reasoning that went into their design. This paper describes a paradigm for constructing expert systems which attempts to identify that tacit knowledge, provide means for capturing it in the knowledge bases of expert systems, and, apply it towards more perspicuous machine-generated explanations and more consistent and maintainable system organization.     

A hierarchical expert system for computer process control

This paper reports on the design and implementation of an expert system for computer process control (HESCPC). The complexity of the expertise necessary for computer process control applications requires that the expert system architecture be structured into a hierarchy of classes of specialized experts. The architecture of HESCPC integrates four classes of expert systems: operator/manager companion expert class, control system algorithm design expert class, hardware expert class, and software expert class. The paper is concerned with the design and implementation of the general system architecture, an operator adviser expert for a nuclear power plant and a control system designer expert using a state space feedback approach. Although the design and implementation aspects of all experts are discussed, the emphasis is on the latter.

At this stage of the HESCPC development, the declarative knowledge represented by 423 metarules and 1261 rules is distributed on a hierarchical structure among 20 experts on different levels of the hierarchy which are able to communicate among themselves to solve difficult control problems.

Examples of control system design sessions of linear mono and multivariable systems using feedback state space approach are given. A run time of an operator-adviser data-driven expert system for a nuclear plant is also presented.     

Integration strategy for distributed intelligent systems

In this article, we describe a new framework for designing real-time intelligent control systems. An integrated intelligent system is a large knowledge integration environment that consists of both symbolic reasoning systems (expert systems) and numerical computation packages. These modular software programs are controlled by a meta-system which manages the selection, operation, and communication of these programs. This new architecture can serve as a universal configuration to develop high-performance intelligent systems for many complicated application domains in the real-time manufacturing process. As an example, an intelligent optimal control is utilized to illustrate the integrated intelligent control system.     

基于人工神经元网络的控制系统模型简化的专家系统

本文研究并实现了一个基于人工神经元网络的控制系统模型简化的专家系统(简称为ESOMRT)。该系统适用于专家和非专家用户,能够针对更体的连续和离散时间的高阶控制系统模型和简化要求选择合适的简化方法,并可对简化质量从时域和频域方面进行评估。在构造这个系统的过程中,作者提出了智能数据库的概念,使用了过程型和人工神经元网络方法相结合的知识表达方式,并利用神经元网络的再学习机制实现了斗自动知识获取,该系统具有三种工作模式和友好的人机界面,使系统的智能水平比较高并有实用价值,现已在IBM-PC/XT和386机上运行。     

一个专家控制系统的Matlab程序实现

本文首先简述基于粗集方法的专家控制系统的基本构成、知识表达方式及学习推理方法,通过研究导师的知识与学习者的知识之间的依赖程度,提出了一个专家控制系统的生产过程中决策控制规律推理的方法,最后给出了利用Matlab实现该生产系统简化及决策控制规律推理的程序。     

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.     

NUREST:一个实时专家系统开发工具

对实时专家系统的特点作了概要论述,将专家系统技术和实时控制技术相结合,设计并实现了应用于实时问题求解的实时专家系统开发工具NUREST。主要讨论了它的知识表示、实时控制机制、推理机制以及实现技术,并给出了开发的炼钢控制实时专家系统实例。     

Statistically appraising process quality of affinity isolation experiments

Quality affinity isolation experiments are necessary to identify valid protein-protein interactions. Biological error, processing error, and random variability can reduce the quality of an experiment, and thus hinder the identification of protein interaction pairs. Appraising affinity isolation assay quality is essential to inferring protein associations. An important step of the assay is the mass spectrometric identification of proteins. To evaluate this step, a known mixture of proteins is processed through a mass spectrometer as a quality control mixture. If the mass spectrometer yields unexpected results, the process is currently qualitatively evaluated, tuned, and reset. Statistical quality control (SQC) procedures, including the use of cumulative sum, the individual measurement, and moving range charts are implemented to analyze the stability of the mass spectrometric analysis. The SQC measures presented can assist in establishing preliminary control limits to identify an out-of-control process and investigate assignable causes for shifts in the process mean in real time.     

Neurofuzzy identification of an autonomous underwater vehicle

Neurofuzzy modelling is ideally suited to many nonlinear system identification and data modelling applications. By combining the attractive attributes of fuzzy systems and neural networks transparent models of ill-defined systems can be identified. Available expert a priori knowledge is used to construct an initial model. Data modelling techniques from the neural network, statistical and conventional system identification communities are then used to adapt these models. As a result accurate parsimonious models which are transparent and easy to validate are identified. Recent advances in the datadriven identification algorithms have now made neurofuzzy modelling appropriate for high-dimensional problems for which the expert knowledge and data may be of a poor quality. In this paper neurofuzzy modelling techniques are presented. This powerful approach to system identification is demonstrated by its application to the identification of an Autonomous Underwater Vehicle (AUV).     

一类专家控制系统的鲁棒性问题

本文讨论在一类专家控制系统中影响控制系统鲁棒性的原因,通过仿真实验证明,对控制系统中的大部分不确定因素来讲,专家控制比常规PID控制有更强的鲁棒性,但对加在系统输出端的有界扰动,专家控制系统的鲁棒性却很差,理论分析表明,这种影响专家控制系统鲁棒性的原因来自产生式规则中证据的不确定性以及由于专家知识的局限性导致策略的不确定性,从而为在专家控制系统中采用不精确推理提供了理论依据。     

Construction of quality control charts by using probability and fuzzy approaches and an application in a textile company

A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. In this study, statistical quality control and the fuzzy set theory are aimed to combine. As known, fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature and humanity; and facilitators for common-sense reasoning in decision making in the absence of complete and precise information. In this basis for a textile firm for monitoring the yarn quality, control charts proposed by Wang and Raz are constructed according to fuzzy theory by considering the quality in terms of grades of conformance as opposed to absolute conformance and nonconformance. And then with the same data for textile company, the control chart based on probability theory is constructed. The results of control charts based on two different approaches are compared. It’s seen that fuzzy theory performs better than probability theory in monitoring the product quality.     

Basic Theoretical Results for Describing Expert Systems. Application to the Supervision of Adaptation Transients in a Planar Robot

This paper deals with the development of an expert system which is valid to optimize the transients arising in adaptive control via the use of a logic formal structure previously described. Its practical experimentation leads to good applicability results in a context of rule-based knowledge. A supervisor to improve the transient performance in the adaptive control of a planar robot is designed based on an expert network organization. Apart form the basic adaptation scheme consisting of an estimation algorithm plus an adaptive controller, two additional coordinated expert systems are used to update an adaptation gain and the sampling period with a master expert system coordinating both above expert systems.