Over the past decade, wind energy has become increasingly significant in the global energy sector. Nonetheless, operation and maintenance (O&M) account for at least one-third of the overall energy generation cost. Condition-based maintenance (CBM) provides a remedy for this issue: instead of scheduling maintenance, it monitors turbine components and performs maintenance only when warnings of possible malfunctions are provided. All strategies related to fault detection and diagnosis of wind turbine generators (WTGs) can be categorized as model-based approaches and data-driven approaches. Model-based techniques rely mostly on a precise mathematical model of the WTG and its subsystems. In contrast, data-driven systems do not require physical or exact mathematical models but infer the defect detection system from observed sensor data.