# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 6 Conclusions  
By integrating the vanilla LSTM model with a model-agnostic vector representation for time, Time2 Vec, a novel neural network model, T2V-LSTM, is developed to predict multivariate faults with a 7 MW offshore wind turbine based on SCADA data. This approach has shown its efficacy on both overall and specific fault predictions by outperforming LSTM and other ML classifiers in most test cases. It has been proven that all classification models can be implemented prior to the next prediction window in all cases under the 10 min SCADA resolution. Using a feature selection method, RFE, to assess the importance of features for dimension reduction, 33 optimal features are extracted to improve the prediction accuracy and computing efficiency of neural networks. Regarding the T2V-LSTM prediction results, the following conclusions can be noted:  
* As there are eight specific faults and massive data imbalances studied in this research, T2V-LSTM can successfully predict all faults 160 min before their occurrence with an overall recall score (MAR) of over 87.5%. T2V-LSTM outperforms LSTM and other classifiers in terms of accuracy, recall scores (both MARs and MIRs), and F-scores in all test cases, but with a longer lagged time, the MAR abruptly falls to roughly 85%. * T2V-LSTM has satisfactory predictions on (Demoted) gearbox pressure 2 faults, Blade 3 slow response, gearbox cooling faults, and sub-pitch fatal faults, due to its minimum MIP over 88.28% and minimum MIR over 86.27%, shown in Appendix A. Approximately 80% of the HPU 2 pump active faults are correctly predicted along with the relevant MIPs scoring roughly 80%. PcsOff faults exhibit excellent prediction results 160 min before the occurrence, with both recall and precision scores over 89%, but the significantly curtailed MIPs and MIRs take place over a longer prediction horizon. The F-scores on those faults are balanced due to their unbiased and promising precision and recall decisions. * However, the balance between MIPs and MIRs is demolished under PcsTrip and pitch fatal faults due to their F-scores being brought down by poorer recall scores. PcsTrip and pitch fatal faults behold upper MIP ranges than MIR ranges and degraded predictions over time. PcsTrip faults are successfully predicted 30 min in advance due to their optimal MIR (88.88%), but the minimum MIR (75.30%) is obtained 3 h before occurrence. By comparison, MIRs on pitch fatal faults have an even more critical downtrend, reducing from 69.48% to 44.15% over time. Particularly, over half of pitch fatal faults are misdiagnosed >170 min before occurrence. The curtailments in MIRs on pitch fatal faults over 40 min ahead predominately contribute to the significant degradations of overall accuracy, MARS, and F-scores. Hence, the poorest predictions on pitch fatal faults bear a considerable burden for overall prediction accuracy. * The confusion matrices visually study the balance between recall and precision scores by predicting the faults 10, 30, 60, 120, and 180 min in advance. Apart from PcsTrip and pitch fatal faults having more biases in FNs over FPs, the other faults can acquire the balanced F-scores due to their FNs roughly equalising FPs. For those faults with balanced F-scores, the resultant MIPs and MIRs mostly surpass 80%, except for the MIP (74.56%) and MIR (76.57%) from the 3 h ahead prediction on HPU 2 pump active faults. PcsTrip faults mainly have excellent MIPs over 90%, but the degradations on their MIRs are expected over time. Hence, the prediction curtailments provided by HPU 2 pump active faults and PcsTrip faults over a longer prediction horizon also contribute to the degradation of overall performance metrics. * As T2V-LSTM fails to predict over 40% of pitch fatal faults 40 min prior to occurrence, future studies should critically focus on building a performance curve of pitch angle to improve the predictions on pitch fatal faults. Conceptualisation, S.Z. ; methodology, S.Z. ; software, S.Z. ; validation, S.Z. ; formal analysis, S.Z. ; investigation, S.Z. ; resources, S.Z.