Gearbox fault identification using auto-encoder without training data from the damaged machine

• New diagnostic method for rotating machines under variable conditions. • Auto-encoder based Fault Identification Technique was developed. • Deep learning method was used without needing data from the damaged machine. • Two independent experiments confirmed the effectiveness of the proposed method. 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 AE-IOS). 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.