As a knowledge representation model

fuzzy Petri nets can be potentially used in industrial process systems for fault reasoning and diagnosis. The establishment of fuzzy Petri nets model usually demands for a priori knowledge

which discourages the use in practice. To utilize industrial process data effectively

association rules based on conditional state fuzzy Petri nets are proposed

which are subsequently applied to industrial process fault reasoning and diagnosis. Fuzzy rules along with confidences of fuzzy Petri nets are extracted by association rule algorithms of data mining. Key principal components (conditional variables) affecting the confidence are extracted by correlation analysis between variables and conditional states

thus creating the conditional state fuzzy Petri nets. The reverse reasoning of dynamic confidence is performed with the iterative algorithm of the maximal algebra

obtaining the probability of fault occurrence in industrial processes. This approach realizes data driven fault diagnosis

so as enhancing the speed and accuracy of fault diagnosis. A case study on chemical reactions shows the effectiveness of the proposed method.