5th. International Conference on Engineering and Natural Sciences (ICENS 2019), Praha, Czech Republic, 12 - 16 June 2019, pp.318-324
Rotating machine elements in mechanical systems such as gears and bearings have a major impact to
maintain machine power transmission and machine working life in a healthy situation. Some mechanical
failures including cracked tooth and pitting faults especially in gears may have a crucial effect on system
failure and safety. However, fault condition monitoring (FCM) at rotating machine parts was studied mostly
by using classical vibration monitoring in the literature. In this study, acoustic measurement results were
obtained experimentally from a single stage gearbox. Experiments were performed by measuring different
running speeds, loading conditions and fault characteristics under dry friction conditions of gears. According
to results, an artificial neural network (ANN) design for predicting and modelling different possible faults of
the system was created. Acoustic results were evaluated by using training data, test data and validation data.
This work shows that ANN design represents a good accordance with experimental results and specify
predictable information about fault varieties occurred at gearbox.