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The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results.

In the protocol conformance testing, many existing test methods can effectively detect the possible faults of the implementation under test. However, it is difficult to diagnose the found faults in terms of the test results. This paper presents a diagnosable input/output (DIO) sequence, to differentiate a state from other states under a given condition. We further propose a two-tier protocol conformance testing and diagnosing method based on DIO sequences. The proposed method can effectively detect and diagnose the possible faults of the implementation of a protocol.