# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 1 Introduction  
The global wind energy installations expanded by about 14% annually from 2001 to 2020 [1]. The total wind power capacity increased from 650.8 GW in 2019 to 742.7 GW in 2020, with a spectacular growth of 53% (over 90 GW) since 2019 [2, 3]. Due to plentiful wind resources and abundant construction sites in offshore areas, more wind farms are installed with increased seabed depths and remote distances to shore [4]. Using the same commercial wind turbine (WT), offshore power production is at least 1.34 times more than the onshore site with the highest wind energy potential due to stronger and more uniform wind resources in offshore areas [5]. However, offshore WT installation costs are about 2.64 times those of their onshore counterpart [6]. And harsher weather conditions are challenging for the operation and maintenance (O&M) tasks of offshore WTs. Moreover, O&M costs account for a large fraction of total lifecycle costs, with 10-15% and 25-30% for onshore and offshore wind farms, respectively [6, 7]. Unexpectedly, sudden faults from high-risk WT components contribute significantly to the increase in O&M costs related to downtimes and discounted revenues [8]. To reduce O&M costs and enhance system reliability, condition monitoring (CM), fault diagnosis, and prognosis are of prior importance through the detection of certain faults before they reach catastrophic fault severity levels. Hence, O&M costs can be decreased along with maintenance interval optimisation [9]. Condition Monitoring System (CMS) can facilitate system failure prevention and WT availability improvement through early-stage fault detections. To diagnose fault-free conditions of WT components, such as the gearbox and drivetrain, CMS has been implemented via vibration analysis [10], oil analysis [11], electrical signature analysis [12], and acoustic emission analysis [13]. CMS-based monitoring is capable of both fault diagnosis and prognosis with a high-frequency resolution, but this approach is more expensive compared to supervisory control and data acquisition (SCADA) [14]. Thus, SCADA systems become more favourable for WT operators to apply the CM technique due to cheaper costs; however, these have a low-frequency resolution [15]. SCADA data are normally collected under a 10 min sampling rate. A number of data-driven studies on SCADA-based monitoring have been utilised for performance monitoring of WT operational conditions in recent years without retrofitting additional sensors. [PERSON] et al. [16] investigated the machine learning (ML) applications for CM in WTs, including CM for diagnosis and CM for prognosis. Diagnosis focuses on real-time fault identifications, whilst prognosis is to predict the faults before their occurrences [17]. Classification is a supervised ML approach, applicable for fault detection, diagnosis, and prognosis, to train a classifier by predicting its categorised outputs based on input variables, thus differentiating between healthy and faulty operations. [PERSON] et al. [18] proposed an online fault diagnosis for WT planetary gearbox faults employing a self-powered wireless sensor for signal acquisition. [PERSON] et al. [19] applied support vector machine (SVM) models to detect, diagnose, and predict faults in a 3 MW direct-drive turbine. However, the classification results on feeding and air-cooling faults had deficient performances due to the problematic classification of the SVM hyperplane. [PERSON] and [PERSON] [20] adopted the k-nearest neighbour (\(k\)-NN) classifier-based protection to detect and classify multiple types of faults in AC/HVDC transmission systems by varying fault resistance and inception angles with a classification accuracy of 100%. [PERSON] et al. [21] investigated several automatic feature selection approaches based on the \(k\)-NN classifier for fault prognostics with the use of 36 sensor variables on gearbox and transmission systems. Artificial Neural Network (ANN) was trained by [PERSON] et al.