## 1 Introduction  
The installed capacity of wind power in the global market in 2021 was 93.6 GW, bringing the global total capacity to 837 GW [1]. As wind turbine technology continues to evolve, sophisticated multi-MW wind turbines have been applied for onshore and offshore wind farms [2]. However, larger wind turbines have proven to develop more failures than small ones [3]. Moreover, wind farms are generally located in remote areas with a harsh operational environment, the limited accessibility of which leads to high costs for operation and maintenance (O&M). Statistics show that the O&M costs account for 10-15% of total onshore wind farm project costs [4]. For an offshore wind farm, the O&M costs account for up to 14-30% [5]. Therefore, it is vital to reduce O&M costs for enhancing the competitiveness of wind farms. Condition monitoring and fault diagnosis of wind turbines aiming at detecting incipient faults can improve the reliability of wind turbines and reduce O&M costs [6]. Recently, many techniques have been presented and achieved some success. Vibration analysis [7; 8; 9], oil analysis [10], and strain measurement [11] have been widely studied, and are mainly used to monitor the highest-cost subcomponents of wind turbines, (e.g., main bearing, gearbox, and electric generators) due to the costs associated with mounting additional sensors and maintaining. On the other hand, supervisory control and data acquisition (SCADA) systems have become a standard installation on large wind turbines, which provide a wide range of wind turbines' operational signals. As a potentially low-cost and wide-coverage solution, plentiful studies using SCADA data for fault diagnosis have been developed [12; 13; 14]. In addition, analysis of alarms generated by wind turbine alarm systems is a promising way of fault diagnosis. Typically, alarms are triggered and recorded when key component signals exceed threshold limits [15], which indicates the need for the operator's emergency action to protect a wind turbine from running into risky conditions. Alarm systems are critically important for the safety and efficiency of wind turbines. Due to the high requirements for condition monitoring of modern large wind turbines, more and more alarm configurations are added to alarm systems, which can provide a large number of alarm data that cover almost all wind turbine subcomponents. The performance of a wind turbine can be monitored through a proper analysis of these collected alarms. However, it is not easy for on-site operators to diagnose wind turbine faults through alarms. Alarms typically contain descriptive information about an abnormal situation, which cannot directly indicate the fault types. Moreover, large numbers of alarms are usually triggered in a short period once a specific fault occurs. The operator is easily overwhelmed by these alarms because it exceeds his response capability. There are three main reasons for the situation. First, irrational and redundant alarm configurations commonly exist [16] in alarm systems, which will cause false alarms and repeated alarms. Second, modern turbines present a high level of interconnectivity due to the mechanical structures, electrical connections, and complex control systems [17]. The propagation of faults in wind turbines will trigger many consequential alarms and related alarms [18]. Third, the operating conditions of wind turbines are complex and changeable. Under different operating conditions, the same fault could trigger different alarms [19]. As a result, when overwhelmed by alarms, the operator needs to rely on extra expert consultation for fault analysis. Some researchers have focused on the use of alarms for wind turbine fault diagnosis. A feasibility study of the wind turbine alarm diagnosis method using an artificial neural network was presented in [20]. To find alarm patterns, the alarms triggered by a fault were transformed into an alarm matrix. However, the actual fault samples are difficult to satisfy its exponential dependence on data volume.