Data mining techniques can discover experience, knowledge, and operational rules from a large industrial data set to recognize process abnormal situations or faults, further improve production-level, and optimize operational conditions. In this paper, a rough set-based fuzzy rule acquisition approach and a fault diagnosis scheme of industrial process are studied in detail. A new heuristic reduct algorithm is proposed to obtain the optimum reduction set of decision information system. Moreover, a fuzzy discretization model for continuous data based on normal distribution of process variables is put forward to overcome the subjective of selecting fuzzy membership functions and decrease the sensitivity to noise signals. Furthermore, the proposed data mining algorithm and fault diagnosis scheme are applied into a petrochemical process. The validity of the proposed strategy is verified by application of a practical ethylene cracking furnace system, which can discover abnormal process situations and improve plant safety in petrochemical industry.