  Fault diagnostics and prognostics are important topics both in practice and
research. There is an intense pressure on industrial plants to continue
reducing unscheduled downtime, performance degradation, and safety hazards,
which requires detecting and recovering potential faults in its early stages.
Intelligent fault diagnosis is a promising tool due to its ability to rapidly
and efficiently processing collected signals and providing accurate diagnosis
results. Although many studies have developed machine leaning (M.L) and deep
learning (D.L) algorithms for detecting the bearing fault, the results have
generally been limited to relatively small train and test datasets and the
input data has been manipulated (selective features used) to reach high
accuracy. In this work, the raw data, collected from accelerometers
(time-domain features) are taken as the input of a novel temporal sequence
prediction algorithm to present an end-to-end method for fault detection. We
use equivalent temporal sequences as the input of a novel Convolutional
Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing
fault with the highest accuracy in the shortest possible time. The method can
reach the highest accuracy in the literature, to the best knowledge of the
authors of the present paper, voiding any sort of pre-processing or
manipulation of the input data. Effectiveness and feasibility of the fault
diagnosis method are validated by applying it to two commonly used benchmark
real vibration datasets and comparing the result with the other intelligent
fault diagnosis methods.
