Amjed M. Al-Ghanim

A heuristic technique for generating minimal path and cutsets of a general network

Reliability evaluation techniques employ a variety of tools for system modeling and calculation of reliability indices. Amongst the most popular tools are network-based algorithms founded on the concept of minimal paths and minimal cutsets. This paper presents a heuristic programming technique for deducing minimal paths of a network. In this technique, only minimal paths are immediately generated without explicitly determining whether or not a path is minimal. This technique has been implemented on a digital computer to generate a minimal path matrix, which is in turn utilized to generate minimal cutsets of the network. In terms of computational speed, the results obtained compare well with existing algorithms. The technique requires minimum memory storage and minimum user-defined data to represent the topology of a network and follows a modular design strategy. Use of the algorithm is illustrated by examples.

An unsupervised learning neural algorithm for identifying process behavior on control charts and a comparison with supervised learning approaches

Abstract The applications of supervised pattern recognition techniques on control charts have shown a substantial improvement in the ability to utilize the information of the chart more effectively than conventional run rules. One major assumption underlying this methodology is that the user has a set of well-defined patterns to detect and a sufficient number of training examples. In practice, however, sufficient training examples may not be readily available, owing either to the inability to simulate these patterns or to the lack of real process data. This paper presents a new approach to detect and identify unnatural patterns on control charts based on the unsupervised self-organizing neural paradigm. The unsupervised methodology is based on ART1 networks. The paper discusses training and testing algorithms to train and test the network using a set of unlabelled natural patterns obtained from the process during normal operation. A comparison is also presented between this unsupervised approach and a major supervised methodology, namely, the statistical learning technique. For the unsupervised methodology, the false alarm rate has substantially improved over that of the supervised methodology, while the rate of identification is higher for the supervised system. The higher rate of identification has been achieved at the cost of providing additional unnatural pattern information to implement the supervised system strategy.

Automated unnatural pattern recognition on control charts using correlation analysis techniques

Abstract Pattern recognition techniques are currently pursued to identify unnatural patterns on quality control charts. This approach has been shown to enhance the ability to utilize the information of the chart more effectively than conventional run rules. This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors i presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results, based on extensive simulation runs, have proved the effectiveness of correlation analysis for control chart pattern recognition.

Unnatural pattern recognition on control charts using correlation analysis techniques

Abstract This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors is presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results have shown the effectiveness of correlation analysis for control chart pattern recognition.

A statistical approach linking energy management to maintenance and production factors

This research has addressed a quantitative approach for improving energy management through applying statistical techniques aimed at identifying and controlling factors linked to energy consumption rates at manufacturing plants. The paper presents analysis and results of multiple linear regression models used to establish the significance of a number of energy related management factors in controlling energy usage. Regression models constructed for this purpose proved the existence of statistically valid relationships between electrical energy consumption and maintenance and production management factors, namely, failure rate and production rate, where R2 values of the magnitude of 65 per cent were obtained. Furthermore, an economical treatment based on the derived regression models was formulated and demonstrated that effective management practices associated with proper maintenance, cost accounting and reporting systems can result in highly significant savings in energy usage.

Automated process monitoring using statistical pattern recognition techniques on X‐bar control charts

Quality control charts are statistical process control tools aimed at monitoring a (manufacturing) process to detect any deviations from normal operation and to aid in process diagnosis and correction. The information presented on the chart is a key to the successful implementation of a quality process correction system. Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts. This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation, thus facilitating process diagnosis and maintenance. Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts. Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis. Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns, and presents a classification procedure for real‐time operation. Demonstrates the system performance using a set of newly defined measures, and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control charts.

A neural network-based methodology for machining knowledge acquisition

Abstract The paper presents results of a study on collecting machining strategies for machining assistants and process planning. These efforts are being conducted at the NMSU-Integrated Manufacturing Systems Laboratory (IMSL). Goals of the project aim at improving and advancing the solicitation, documentation, and automation of machining knowledge/data acquisition, and integration with CAD/CAM/CAE systems. This paper emphasizes the knowledge acquisition phase of the study utilizing artificial neural networks.

A programming technique for generating minimal paths a general network

This paper presents a heuristic technique for deducing minimal paths of a network. It generates only minimal paths, without explicitly determining whether or not a path is minimal This technique has been implemented on a digital computer to generate a minimal path matrix. It terms of computational speed, the results obtained compare well with existing algorithms. The technique requires minimum storage in memory and minimum user-defined data to represent the topology of a network and follows a modular design strategy. Use of the algorithm is illustrated by examples.