The increasing demand for higher efficiency and lower environmental impact of transmissions, used in automotive and wind energy industries has created a need for more advanced technical solutions to fulfil those requirements. Condition monitoring plays an important role in the transmission life cycle, saving resources and time. Recently condition monitoring, using machine learning has shifted from reactive to proactive action, predicting minor faults before they become significant.

This thesis intends to develop a methodology that can be used to predict faults like pitting initiation, before propagating in FZG test rig, available at KTH Machine Design department. Standard sensor measurements already available like temperature, rotation speed and torque are used in this project. Four kinds of gears were used, two made of wrought, and two – of powder metal steel, each with ground or superfinish surface.

After a literature review about pitting fatigue, condition indicators for these failures and machine learning were done, a statistical analysis was done, to see how the transmission behaves during testing and to have comparison material, helpful when having machine learning results. Two machine learning models, Decision Tree and Support Vector Machine were selected and trained in two combinations, either with Root Mean Square only, or with Crest Factor, Standard Deviation and Kurtosis in addition.

As a result, 64 models were trained, 32 for all tests and another 32 to investigate two particular tests due to a longer pitting propagation period. New condition indicators like Standard Deviation and Signal – to – noise ratio was calculated to get more nuanced trends than just using one measurement to monitor the gearbox behavior. After comparing with the results from statistical analysis and previously done tooth profile measurements, it was concluded that the new indicators could indicate the change in gearbox operation before the first pitting initiation is detected, using tooth profile measurement.