# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 6 Conclusions  
When wind turbines experience faults, a large number of alarms are generated in a short period. These alarm data represent the manifestations of turbine faults and, through proper analysis and utilization, can be used to diagnose the types of faults. Due to its ease of understanding, implementation, and independence from data labels, similarity analysis methods have found widespread application. One efficient implementation approach for these methods involves the identification of alarm sequences triggered by the same fault events, and clustering analysis is one of the effective techniques employed. In existing research, a direct application of Word2 vec to transform alarm codes into numerical vectors resulted in deviations from the actual fault patterns. Furthermore, deficiencies are observed in the distance evaluation methods for vector sequences representing alarm sequences during clustering analysis. In response to these two primary issues, our study was conducted.  
This paper proposed a method using K-means clustering to analyze wind turbine alarm data. Firstly, a preprocessing step was applied to the continuous alarms to obtain alarm sequences triggered by the same fault. Then, the proposed DK-Word2 vec was used to obtain vector representations of individual alarms. Finally, the K-means algorithm based on new distance metrics was used to cluster alarm sequences. The wind turbine alarm data consists of a small portion of labeled data sequences and a large portion of unlabeled alarm sequences. The proposed method's performance was validated using the labeled  
Figure 11: Statistical pie chart depicting the proportion of fault events.  
data. The results show that in DK-Word2 vec, when the weight of the class loss is set to 0.4, and the semantic window length is four, the established alarm vectors match the actual fault patterns most accurately, leading to optimal clustering performance based on these vector representations. The WMD and WRD used in K-means can improve the Purity and ARI values. Moreover, using the optimal parameter combination, all the alarm sequences were clustered, revealing four new fault types in the unlabeled data.  
Existing research mainly focuses on analyzing historical alarm data. However, the generation of an alarm sequence often requires a process. In future research, it will be essential to predict alarms in an alarm sequence for early fault diagnosis in wind turbines.  
Conceptualization, [PERSON] ([PERSON]) and L.W. ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation [PERSON]. ([PERSON]); formal analysis, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), Z.Q. and F.L.; resources and funding acquisition, Z.Q.; supervision, F.L. and Z.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 61573046), the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT1203), and the National Nature Science Foundation of China under Grant Nos. U2233216 and U2033215.  
Not applicable.  
The authors declare no conflict of interest.