Automaton based on fuzzy clustering methods for monitoring industrial processes

Fuzzy clustering allows finding classes through the historical data in order to associate them with functional states useful to represent the complex industrial processes behavior. By means of classes, an automaton can be established that determines the current and the next connections of functional states of a process. When fuzzy clustering is used, the connections in the historical data are considered but it does not find other important connections. To solve this limitation, a new method to seek the most important connections among functional states is proposed. Initially, the approach defines an initial transition degrees matrix, where all connections are taken into account. Through a proposed update step, the most important connections are obtained, which they describe the real behavior of a process. In addition, a new distance criterion is defined to improve the update step. The final transition degrees matrix is used to construct a fuzzy automaton that it is validated by human operatorʹs experience. The approach was tested in a steam generator process. Applying three fuzzy clustering algorithms in case of study, the proposed method finds the same transition matrix. The new connections were validated by the human operator.