  The monitoring of rotating machinery is an essential task in today's
production processes. Currently, several machine learning and deep
learning-based modules have achieved excellent results in fault detection and
diagnosis. Nevertheless, to further increase user adoption and diffusion of
such technologies, users and human experts must be provided with explanations
and insights by the modules. Another issue is related, in most cases, with the
unavailability of labeled historical data that makes the use of supervised
models unfeasible. Therefore, a new approach for fault detection and diagnosis
in rotating machinery is here proposed. The methodology consists of three
parts: feature extraction, fault detection and fault diagnosis. In the first
part, the vibration features in the time and frequency domains are extracted.
Secondly, in the fault detection, the presence of fault is verified in an
unsupervised manner based on anomaly detection algorithms. The modularity of
the methodology allows different algorithms to be implemented. Finally, in
fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret
black-box models, is used. Through the feature importance ranking obtained by
the model explainability, the fault diagnosis is performed. Two tools for
diagnosis are proposed, namely: unsupervised classification and root cause
analysis. The effectiveness of the proposed approach is shown on three datasets
containing different mechanical faults in rotating machinery. The study also
presents a comparison between models used in machine learning explainability:
SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-
DIFFI). Lastly, an analysis of several state-of-art anomaly detection
algorithms in rotating machinery is included.
