Machine learning-based techniques for incremental functional diagnosis: A comparative analysis

Incremental functional diagnosis is the process of iteratively selecting a test, executing it and based on the collected outcome deciding either to execute one more test or to stop the process since a faulty candidate component can be identified. The aim is to minimise the cost and the duration of the diagnosis process. In this paper we compare six engines based on machine learning techniques for driving the diagnosis. The comparison has been carried out under a twofold point of view: on the one hand, we analysed the issues related to the use of the considered techniques for the design of incremental diagnosis engines; on the other hand, we carried out a set of experiments on three synthetic but realistic scenarios to assess accuracy and efficiency.