Interpretative identification of the faulty conditions in a cyclic manufacturing process

In this work a method for systematic data analysis for cyclic manufacturing processes is presented. The proposed data-analysis method integrates well-known heuristic algorithms, i.e., decision trees and clustering, with the purpose of identifying types of faulty operating conditions. The result of the analysis is an interpretable model for decision support that can be used for fault identification, to search for root causes, and to develop prognostic systems. A holistic approach of applying the proposed data-analysis method, along with suggestions and guidelines for implementation, is presented. A case study is presented in which the proposed method is applied to real industrial data from a plastic injection-moulding process.