Granular computing studies a novel approach to computing system modeling and information processing. Although a rich set of work has advanced the understanding of granular computing in dealing with the “to be” and “to have” problems of systems, the “to do” aspect of system modeling and behavioral implementation has been relatively overlooked. On the basis of a recent development in denotational mathematics known as system algebra, this paper presents a system metaphor of granules and explores the theoretical and mathematical foundations of granular computing. An abstract system model of granules is proposed in this paper. Rigorous manipulations of granular systems in computing are modeled by system algebra. The properties of granular systems are analyzed, which helps to explain the magnitudes and complexities of granular systems. Formal representation of granular systems for computing is demonstrated by real-world case studies, where concrete granules and their algebraic operations are explained.

On the System Algebra Foundations for Granular Computing

........................................................................................................................... i ACKNOWLEDGEMENTS .................................................................................................. iii TABLE OF CONTENT ........................................................................................................ iv LIST OF FIGURES ............................................................................................................... x LIST OF TABLES ............................................................................................................... xv LIST OF ABBREVIATIONS ............................................................................................. xvi PUBLICATIONS AND AWARDS .................................................................................... xix CHAPTER

Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot

Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.

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Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: a access to the machines on site is sometimes impracticable, and b the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system Melvin I, which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators KPIs. A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Functional complexity is one of the most fundamental properties of software because almost all other software attributes and properties such as functional size, development effort, costs, quality, and project duration are highly dependent on it. The functional complexity of software is a macro-scope problem concerning the semantic properties of software and human cognitive complexity towards a given software system; while the computational complexity is a micro-scope problem concerning algorithmic analyses towards machine throughput and time/space efficiency. This paper presents an empirical study on the functional complexity of software known as cognitive complexity based on large-scale samples using a Software Cognitive Complexity Analysis Tool SCCAT. Empirical data are obtained with SCCAT on 7,531 programs and five formally specified software systems. The theoretical foundation of software functional complexity is introduced and the metric of software cognitive complexity is formally modeled. The functional complexities of a large-scale software system and the air traffic control systems ATCS are rigorously analyzed. A novel approach to represent software functional complexities and their distributions in software systems is developed. The nature of functional complexity of software in software engineering is rigorously explained. The relationship between the symbolic and functional complexities of software is quantitatively analyzed.

Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems

Monitoring industrial machine health in real-time is not only highly demanded but also significantly complicated and difficult. Possible reasons for this include: (a) Access to the machines on site is sometimes impracticable; and (b) The environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite the theoretically sound findings on developing intelligent solutions for machine condition based monitoring, there are few commercial tools in the market that can readily be used. This paper reports on the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor in order to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on the set of KPIs. The system implemented in our laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Preliminary results have demonstrated that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

An intelligent fault recognizer for rotating machinery via remote characteristic vibration signal detection

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Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

An intelligent fault recognizer for rotating machinery via remote characteristic vibration signal detection