An intelligent fault diagnosis system for process plant using a functional HAZOP and DBN integrated methodology

• An integration of functional HAZOP and DBN is presented for fault diagnosis.
• Functional HAZOP study helps for hazard scenario analysis with means-end concepts.
• DBN model provides a probability-based knowledge representation with uncertainty.
• The IFDS is validated with pilot application on a real petrochemical plant.

Integration of a functional HAZOP approach with dynamic Bayesian network (DBN) reasoning is presented in this contribution. The presented methodology can unveil early deviations in the fault causal chain on line. A functional HAZOP study is carried out firstly where a functional plant model (i.e., MFM) assists in a goal oriented decomposition of the plant purpose into the means of achieving the purpose. DBN model is then developed based on the functional HAZOP results to provide a probability-based knowledge representation which is appropriate for the modeling of causal processes with uncertainty. An intelligent fault diagnosis system (IFDS) is proposed based on the whole integrated framework, and investigated in a case study of process plants at a petrochemical corporation. The study shows that the IFDS provides a very efficient paradigm for facilitating HAZOP studies and for enabling reasoning to reveal potential causes and/or consequences far away from the site of the deviation online.