Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Online fault diagnosis and prevention expert system for dredgers
Expert Systems with Applications: An International Journal
A fuzzy inference system for fault detection and isolation: Application to a fluid system
Expert Systems with Applications: An International Journal
An architecture for fault detection and isolation based on fuzzy methods
Expert Systems with Applications: An International Journal
Process Automation Handbook: A Guide to Theory and Practice
Process Automation Handbook: A Guide to Theory and Practice
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Support vector machine classifier for diagnosis in electrical machines: Application to broken bar
Expert Systems with Applications: An International Journal
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 12.05 |
An adaptive clustering procedure specifically designed for process monitoring, fault detection and isolation is presented in this paper. The key feature of the proposed procedure can be identified as its underlying capability to detect novelties in the system's mode of operation and, thus, to identify previously unseen functioning modes of the process. Once a novelty is detected, relevant informations are used to enrich the knowledge-base of the algorithm and as a result the proposed clustering procedure evolves and learns the new features of the monitored process in accordance with the available process data. The suggested clustering procedure is theoretically illustrated and its effectiveness has been investigated experimentally. Particularly, the on-line implementation of the algorithm and its integration with a fault detection expert system have been considered by making reference to a pneumatic process.