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Most of process fault monitoring systems suffer from offline computations and confronting with novel faults that limit their applicabilities. This paper presents a new online fault detection and isolation (FDI) algorithm based on distributed online clustering approach. In the proposed approach, clustering algorithm is used for online detection of a new trend of time series data which indicates faulty condition. On the other hand, distributed technique is used to decompose the overall monitoring task into a series of local monitoring sub-tasks so as to locally track and capture the process faults. This algorithm not only solves the problem of online FDI, but also can handle novel faults. The diagnostic performances of the proposed FDI approach is evaluated on the Tennessee Eastman process plant as a large-scale benchmark problem. [ABSTRACT FROM AUTHOR]

I present some early history of Los Alamos, modern computing, and the Monte Carlo method to describe the likely context in which the Metropolis algorithm was developed and to support the special creativity of the development. I also note the scant immediate use of the algorithm over the 10 to 15 years after its development and speculate why. This sparse use however did include many seminal applications and led to many of the techniques still used today. This is the heritage enjoyed by us who today unhesitatingly use the Metropolis algorithm and the Monte Carlo method more generally, for exploring the properties of physical systems. © 2003 American Institute of Physics [ABSTRACT FROM AUTHOR]

A personal view of the use of the Metropolis Algorithm in statistical mechanics calculations at Los Alamos during the 1950s will be presented, based on [1] and [2]. © 2003 American Institute of Physics [ABSTRACT FROM AUTHOR]