Fault detection and diagnosis in industrial processes are challenging tasks that demand effective and timely decision making procedures. The multivariate statistical approaches for fault detection based on data have been very useful. However, they are known to be less powerful for fault diagnosis because they normally require prior knowledge of the problem involved. In this context, this proposal is based on an on-line, distributed fault isolation approach to provide a scored rank of variables considered as responsible for the faults in a more robust and earlier way than usual approaches. The fault isolation is carried out considering some top Fault Isolation (FI) methods, without prior knowledge regarding faults, in a distributed and collaborative way by a linguistic based decision making. The isolation of faulty variables provided by each FI approach is aggregated to provide a fault identification based on a scored ranking at two time points: after the fault detection and when the plant has recovered. In both cases, the final fault isolation is provided as a scored ranking obtained by Ordered Weighted Average operators (OWA) and Regular Increasing Monotone (RIM) aggregation functions, which permit the implementation of linguistic aggregation functions. The risk aversion during this multicriteria isolation is tuned by the user and can provide several strategies or policies. The fault isolation at two key times searches for the origin of faults and evaluates the evolution of the system after the fault's occurrence in the new working position of the plant. This is because faults in an industrial plant are propagated to different variables due to the actions of the process controllers. This method has been applied to two complex benchmark plants obtaining an earlier and more robust isolation. (C) 2018 Elsevier Ltd. All rights reserved.