IntroductionThe detection and diagnosis of abnormal situations in the operation of industrial processes is a problem of considerable challenge that is attracting wide attention in both academe Ž . and industry. Nimmo 1995 showed that the U.S.-based petrochemical industry alone could save up to $10 billion per year if abnormal situations could be detected, diagnosed, and appropriately dealt with. The consequences of not being able to detect such abnormal situations range from increased operational costs to costly plant shutdowns.Industrial processes often present a large number of process variables, such as temperatures, pressures, flow rates, compositions, which are typically sampled at a frequency of one minute. Identifying and troubleshooting abnormal operating conditions simply by observation are difficult tasks with such a large amount of data, particularly since the process Ž variables are usually highly correlated MacGregor et al.,. 1991 . However, the sampled data have embedded within them information for revealing the state of the process operation.
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