## 1 Introduction
A time-sequence method and probability-based method were proposed in [15] for analyzing alarms. The fault cases on the wind turbine converter and pitch system were used to verify the proposed methods. The results showed that both methods had the potential to rationalize alarm data and identify fault locations. However, the issue of time consumption must be solved when the methods are applied to larger data. An improved Apriori algorithm was proposed in [21] to analyze alarms, which occurred during blade angle asymmetry fault. The results showed that the related alarms could be integrated into one critical alarm to reduce the number of alarms. The accuracy of the method is limited due to its dependence on sufficient sample data. A clustering analysis of alarm sequences for characterizing and classifying wind stoppages was conducted in [22]. Despite recent progress, the accuracy of the clustering requires improvement. A multi-dimensional information fusion method based on the Dempster-Shafer evidence theory was proposed in [23], which obtained a higher diagnosis accuracy of alarm sequences. The results showed that the diagnosis accuracy was affected by the quality of recorded fault labels in maintenance records. A weighted Hamming distance was proposed and applied in the similarity analysis of alarm lists to identify the fault category [24]. It did not require a time-consuming training procedure and was easy to apply. However, the improvement in accuracy is limited by the number of labeled alarm sequences. The above research status shows that when diagnosing the wind turbine faults using triggered alarms, the few fault samples and low-quality fault labels have limited the improvement of diagnosis accuracy. Both factors are related to maintenance records because the fault types that trigger alarms are recorded in maintenance records. However, due to the self-inspection function of wind turbines and the irregular work of the operator, a large proportion of alarms has no corresponding maintenance record. That is to say, a large proportion of alarms have no recorded fault labels. Therefore, the actual alarm data contain few labeled alarms and many unlabeled alarms. The existing studies mainly focus on the analysis of labeled alarms. As far as we know, there is no research about how to improve diagnosis accuracy with few labeled and many unlabeled alarms. In addition, the existing literature does not delve deeply into the relationship between individual alarms. Some studies only consider the temporal order or occurrence probability of individual alarms [15; 21], while others only focus on the relationship between one alarm sequence and another [20; 22; 24], without considering individual alarms. To fill this gap, this paper proposes a new fault diagnosis method for wind turbines with alarms. The proposed diagnosis method is designed based on the word embedding technique and a Siamese neural network. Firstly, the Skip-gram model in word embedding is employed to convert non-numerical alarm codes into real-valued vector representations, considering their sequential relationships and frequencies within the alarm sequence (the Skip-gram model will be described in detail in Section 3.2). Additionally, the pretraining technique in word embedding is utilized to explore the relationships among individual alarms in unlabeled alarm data. Subsequently, by further optimizing the alarm vectors obtained from pretraining using labeled alarm data, the joint utilization of labeled and unlabeled data is achieved. Secondly, the designed fault type diagnostic model based on the Siamese neural network for unknown alarm sequences can delve into the similarity features among alarm sequences (the diagnostic model based on the Siamese neural network will be specifically described in Section 3.3) and produce similarity scores. In this study, the criterion used to diagnose the fault type of unknown alarm sequences is the similarity score between the unknown alarm sequence and known alarm sequences.