  Artificial Intelligence (AI) is one of the approaches that has been proposed
to analyze the collected data (e.g., vibration signals) providing a diagnosis
of the asset's operating condition. It is known that models trained with
labeled data (supervised) achieve excellent results, but two main problems make
their application in production processes difficult: (i) impossibility or long
time to obtain a sample of all operational conditions (since faults seldom
happen) and (ii) high cost of experts to label all acquired data. Another
limitating factor for the applicability of AI approaches in this context is the
lack of interpretability of the models (black-boxes), which reduces the
confidence of the diagnosis and trust/adoption from users. To overcome these
problems, a new generic and interpretable approach for classifying faults in
rotating machinery based on transfer learning from augmented synthetic data to
real rotating machinery is here proposed, namelly FaultD-XAI (Fault Diagnosis
using eXplainable AI). To provide scalability using transfer learning,
synthetic vibration signals are created mimicking the characteristic behavior
of failures in operation. The application of Gradient-weighted Class Activation
Mapping (Grad-CAM) with 1D Convolutional Neural Network (1D CNN) allows the
interpretation of results, supporting the user in decision making and
increasing diagnostic confidence. The proposed approach not only obtained
promising diagnostic performance, but was also able to learn characteristics
used by experts to identify conditions in a source domain and apply them in
another target domain. The experimental results suggest a promising approach on
exploiting transfer learning, synthetic data and explainable artificial
intelligence for fault diagnosis. Lastly, to guarantee reproducibility and
foster research in the field, the developed dataset is made publicly available.
