R ecently, there has been a growing interest in developing and applying knowledgebased technologies to aid hazard identi® cation methods such as Hazop (Hazard and Operability Studies), fault tree analysis and check-lists which have traditionally been carried out manually. A critical factor is the knowledge which is used. Previous experience and cases of failure provide an important source of information which can be used to update knowledge. However, the volume of data is normally too great to

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... arry out manual analysis. Moreover, the data is complex in structure and of diverse types, as well as being noisy and having missing elements. This results in the databases being mainly used for archive and retrieval. This paper reports the application of probabilistic networks and how they can be used for learning about failure diagnosis of process units by extracting knowledge from the databases in the form of rules, which can be used either by experts or in building expert systems. Keywords: data mining; knowledge discovery in databases; failure diagnosis; probabilistic networks. P(x 2 =1| x 1 =1) = 0.8 P(x 2 =0 | x 1 =1) = 0.2 P(x 2 =1 | x 1 =0) = 0.3 P(x 2 =0 | x 1 =0) = 0.7 P(x 3 =1 | x 2 =1) = 0.9 P(x 3 =0 | x 2 =1) = 0.l P(x 3 =1 | x 2 =0) = 0.15 P(x 3 =0 | x 2 =0) = 0.85