An intelligent composite system for statistical process control

Combining statistical process control, artificial neural networks and an expert system for the intelligent analysis and control of a plastic extruder facility is described. Statistical methodology is compared and contrasted to the exploratory neural network technique, which learns to relate and classify dependent production variables based on measurements taken on-line during the process. Integrating the neural network analysis into a composite control system using an expert system is presented.     

A component-based simulation environment for statistical process control systems analysis

This paper describes a simulation environment, called Prosim, which permits a user to define components, subsystems, and their interconnections to analyse a statistical process control (SPC) system. The components and systems are defined and analysed interactively. A library of standard SPC objects containing models for the Xbar, range, exponential weighted moving average, p-chart and other SPC techniques have been created which help define the control application. The PC-based tool is tested on theoretical, and real data, and is useful for the design and trouble shooting of a manufacturing system. It is also an effective teaching and research tool.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

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.     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

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.     

A framework for knowledge-based control

A framework for knowledge-based control is proposed. The approach presented is suitable for control systems and control support of systems which have no adequate mathematical models. Thus, the control is performed by using knowledge engineering methods rather than pure mathematical control methods. The domain expert's knowledge is assumed to be encoded in the form of simple statements (facts) and special reasoning rules, which form the core of the Knowledge-Based Control System (KBCS). The control system reads the input information, and on the basis of the current state of its knowledge base, together with the application of supplied inference rules updates the knowledge base and performs the required control actions. Moreover, some inference control knowledge, reflecting the expert's way of reasoning, is to be incorporated in the KBCS. The main idea of the system consists of selecting an appropriate set of actions to be executed, with regard to the current state specification and the control goal given. An abstract mathematical model of the control process is formulated and a suitable language for knowledge representation is proposed. The reasoning scheme is discussed and the structure of the control system is outlined. A representative application example is provided.     

Design approach to low-cost process control

The design of a control package for industrial use on small-scale processes is discussed. A modular system is described, made up of intelligent satellite and peripheral controllers plus various cards to provide communication with the industrial plant itself. The design is intended for operators who have little or no experience of computing. It is based on a language called paracode, an interpretive language structured to provide a number of control sequences which can run independently of each other or in parallel. Two applications of the design—a clean-in-place system and a beer-fermentation controller—are used as illustrations.     

Mean shifts detection and classification in multivariate process: a neural-fuzzy approach

For monitoring multivariate quality control process, traditional multivariate control charts have been proposed to detect mean shifts. However, a persistent problem is that such charts are unable to provide any shift-related information when mean shifts occur in the process. In fact, the immediate classification of the magnitude of mean shifts can greatly narrow down the set of possible assignable causes, hence facilitating quick analysis and corrective action by the technician before many nonconforming units are manufactured. In this paper, we propose a neural-fuzzy model for detecting mean shifts and classifying their magnitude in multivariate process. This model is divided into training and classifying modules. In the training module, a neural network (NN) model is trained to detect various mean shifts for multivariate process. Then, in the classifying module, the outputs of NN are classified into various decision intervals by using a fuzzy classifier and an additional two-point-in-an-interval decision rule to determine shift status. An example is presented to illustrate the application of the proposed model. Simulation results show that it outperforms the multivariate T²control chart in terms of out-of-control average run length under fixed type I error. In addition, the correct classification percentages are also studied and the general guidelines are given for the proper use of the proposed model.     

A knowledge-based approach for handling exceptions in business processes

This paper describes a novel knowledge-based methodology and toolset for helping business process designers and participants better manage exceptions (unexpected deviations from an ideal sequence of events caused by design errors, resource failures, requirement changes, etc.) that can occur during the enactment of a process. This approach is based on an on-line repository exploiting a generic and reusable body of knowledge, which describes what kinds of exceptions can occur in collaborative work processes, how these exceptions can be detected, and how they can be resolved. This work builds upon previous efforts from the MIT Process Handbook project and from research on conflict management in collaborative design. This revised version was published online in July 2006 with corrections to the Cover Date.     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

Despite their capability in monitoring the variability of the processes, control charts are not effective tools for identifying the real time of such changes. Identifying the real time of the change in a process is recognized as change-point estimation problem. Most of the change-point models in the literature are limited to fixed sampling control charts which are only a special case of more effective charts known as variable sampling charts. In this paper, we develop a general fuzzy-statistical clustering approach for estimating change-points in different types of control charts with either fixed or variable sampling strategy. For this purpose, we devise and evaluate a new similarity measure based on the definition of operation characteristics and power functions. We also develop and examine a new objective function and discuss its relation with maximum-likelihood estimator. Finally, we conduct extensive simulation studies to evaluate the performance of the proposed approach for different types of control charts with different sampling strategies.     

应用统计过程控制监控软件测试过程

统计过程控制(SPC)是通过使用控制图来制定过程决策和预测过程行为的一种质量控制方法.SPC的方法用于软件过程,可以通过描述过程行为来监控过程的稳定性.讨论了将SPC应用于软件测试过程,针对测试过程中所度量的不同分布形式的数据而采用不同计算方式应用SPC的控制图,然后根据控制图判断测试过程是否稳定,并分析可能存在的可归属原因.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

Fault tolerance in supervisory control systems: a knowledge-based approach

Fault tolerance in computerized systems involved in production has become an ever more important requirement. Existing fault tolerance approaches, wherever used, deal mainly with hardware faults. Nevertheless, the vast majority of contemporary system failures are software related. This paper introduces a knowledge-based approach to handling software related faults occurring in supervisory control systems. These systems are event driven and use data, stored in complex databases, to react to events coming from different kinds of devices by identifying, scheduling, initiating and monitoring operations. Failure of part of the supervisory control system's software to behave rationally when unexpected events occur is called an application fault. The approach introduced in this paper is based on a supervisory control system reference model which reveals the set of all possible application faults together with the major functions of the recovery processes associated with each fault, and leads to a high-level knowledge-based system architecture capable of handling every fault-related condition. This system is called PROFIT (Intelligent PROduction systems Fault Tolerance) and consists of three main components: the fault diagnosis module, the instant fault correction module and the learning module, co-ordinated by a PROFIT meta-level module. The prototype version of PROFIT is analysed and the development as well as the run-time environment that prove the applicability and effectiveness of the system are presented.     

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.     

Utilization of statistical process control (SPC) in emergent software organizations: Pitfalls and suggestions

Common wisdom in the domain of software engineering tells us that companies should be mature enough to apply Statistical Process Control (SPC) techniques. Since reaching high maturity levels (in CMM or similar models such as ISO 15504) usually takes 5–10 years, should software companies wait years to utilize Statistical Process Control techniques? To answer this question, we performed a case study of the application of SPC techniques using existing measurement data in an emergent software organization. Specifically, defect density, rework percentage and inspection performance metrics are analyzed. This paper provides a practical insight on the usability of SPC for the selected metrics in the specific processes and describes our observations on the difficulties and the benefits of applying SPC to an emergent software organization.     

Validation and verification of knowledge-based systems: A survey

Validation and verification of expert systems or knowledge-based systems is a critical issue in the development and deployment of robust systems. This article is a comprehensive survey of the developments and trends in this field. More than 300 references are included in the References and Additional Readings at the end of article.