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Gear transmissions are widely used in industrial applications and are considered to be critical components. To date, the capabilities of gear condition indicators are controversial as some condition indicators can diagnose one type of fault at the early stages, yet cannot diagnose other types of faults. This study focused on fault detection and characterization based on vibrations in a spur gear transmission. Three different common local faults were examined: tooth face fault, broken tooth, and cracks at the tooth root. The faults were thoroughly analyzed to understand the fault manifestation in the vibration signature and to find condition indicators that are robust and sensitive to the existence and severity of the fault. The analysis was based on both experimental data and simulated signals from a well-established dynamic model of the gear system. The fault detection capability of common condition indicators, as well as newly defined condition indicators, was examined and measured using statistical distances. For each fault type, the investigated condition indicators were categorized according to their discrimination power between faulted and healthy states and the ability to rank the fault severity. It was concluded that faults that affect the involute profile throughout the tooth are easily detectable. Faults such as root cracks or chipped tooth, in which mainly the tooth stiffness is affected, are much more challenging to detect. It has been shown that while using a realistic model, the capabilities of different condition indicators can be tested, and the experiments can be replaced by simulations.

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