  Deep learning and big data algorithms have become widely used in industrial
applications to optimize several tasks in many complex systems. Particularly,
deep learning model for diagnosing and prognosing machinery health has
leveraged predictive maintenance (PdM) to be more accurate and reliable in
decision making, in this way avoiding unnecessary interventions, machinery
accidents, and environment catastrophes. Recently, Transformer Neural Networks
have gained notoriety and have been increasingly the favorite choice for
Natural Language Processing (NLP) tasks. Thus, given their recent major
achievements in NLP, this paper proposes the development of an automatic fault
classifier model for predictive maintenance based on a modified version of the
Transformer architecture, namely T4PdM, to identify multiple types of faults in
rotating machinery. Experimental results are developed and presented for the
MaFaulDa and CWRU databases. T4PdM was able to achieve an overall accuracy of
99.98% and 98% for both datasets, respectively. In addition, the performance of
the proposed model is compared to other previously published works. It has
demonstrated the superiority of the model in detecting and classifying faults
in rotating industrial machinery. Therefore, the proposed Transformer-based
model can improve the performance of machinery fault analysis and diagnostic
processes and leverage companies to a new era of the Industry 4.0. In addition,
this methodology can be adapted to any other task of time series
classification.
