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Deep learning methods work well in machine diagnostics where operating conditions affect diagnostic signals.
Classifiers are often used for fault identification, but these methods require training data sets measured for each
fault. The solution to the lack of data is autoencoder-based network models, but these models can only detect, not
identify faults.
This article presents a new fault identification method based on auto-encoders (AE-based Fault Identification
Technique AE-FIT) that does not require training data from the damaged machine. This method diagnoses pinion
gearboxes operating under variable conditions (variable load, load-induced rotational speed, and temperature).
The result of the technique is an interpretable diagnostic spectrum (AE-based Interpretable Order Spectrum AEIOS).
The method has been tested on two laboratory benches to detect misalignment, unbalance, and gearbox
degradation. The damages introduced were used to validate a technique based on an auto-encoder trained only
with data from undamaged machines.
