Skip to content

Commit f4a52c2

Browse files
committed
fix references
1 parent dddfbcd commit f4a52c2

File tree

2 files changed

+72
-205
lines changed

2 files changed

+72
-205
lines changed

docs/conf.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -39,7 +39,7 @@
3939

4040
html_theme = "pydata_sphinx_theme"
4141

42-
html_static_path = ["_static"]
42+
# html_static_path = ["_static"]
4343

4444
html_theme_options = {
4545
"icon_links": [

docs/report.rst

Lines changed: 71 additions & 204 deletions
Original file line numberDiff line numberDiff line change
@@ -1090,207 +1090,74 @@ transitions from red (lower scores) to yellow (intermediate) to green
10901090
References
10911091
----------
10921092

1093-
.. [1]
1094-
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S. &
1095-
Fleming, P. (2012). `Use of SCADA Data for Failure Detection in Wind
1096-
Turbines <https://doi.org/10.1115/ES2011-54243>`__. 2071-2079.
1097-
1098-
.. [2]
1099-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1100-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1101-
learning techniques applied to operational
1102-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1103-
International Conference on Prognostics and Health Management
1104-
(ICPHM), 1-8.
1105-
1106-
.. [3]
1107-
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S. &
1108-
Fleming, P. (2012). `Use of SCADA Data for Failure Detection in Wind
1109-
Turbines <https://doi.org/10.1115/ES2011-54243>`__. 2071-2079.
1110-
1111-
.. [4]
1112-
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S. &
1113-
Fleming, P. (2012). `Use of SCADA Data for Failure Detection in Wind
1114-
Turbines <https://doi.org/10.1115/ES2011-54243>`__. 2071-2079.
1115-
1116-
.. [5]
1117-
Dienst, S. & Beseler, J. (2016). `Automatic Anomaly Detection in
1118-
Offshore Wind SCADA
1119-
Data <https://windeurope.org/summit2016/conference/submit-an-abstract/pdf/626738292593.pdf>`__.
1120-
1121-
.. [6]
1122-
Dienst, S. & Beseler, J. (2016). `Automatic Anomaly Detection in
1123-
Offshore Wind SCADA
1124-
Data <https://windeurope.org/summit2016/conference/submit-an-abstract/pdf/626738292593.pdf>`__.
1125-
1126-
.. [7]
1127-
Tautz-Weinert, J. & Watson, S. J. (2017). `Using SCADA data for wind
1128-
turbine condition monitoring - a
1129-
review <https://doi.org/10.1049/iet-rpg.2016.0248>`__. IET Renewable
1130-
Power Generation, 11(4), 382-394.
1131-
1132-
.. [8]
1133-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1134-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1135-
learning techniques applied to operational
1136-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1137-
International Conference on Prognostics and Health Management
1138-
(ICPHM), 1-8.
1139-
1140-
.. [9]
1141-
Wind Turbine Condition Monitoring. (2015). [White Paper]. National
1142-
Instruments.
1143-
1144-
.. [10]
1145-
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S. &
1146-
Fleming, P. (2012). `Use of SCADA Data for Failure Detection in Wind
1147-
Turbines <https://doi.org/10.1115/ES2011-54243>`__. 2071-2079.
1148-
1149-
.. [11]
1150-
Wind Turbine Condition Monitoring. (2015). [White Paper]. National
1151-
Instruments.
1152-
1153-
.. [12]
1154-
García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M. & Papaelias,
1155-
M. (2012). `Condition monitoring of wind turbines: Techniques and
1156-
methods <https://doi.org/10.1016/j.renene.2012.03.003>`__. Renewable
1157-
Energy, 46, 169-178.
1158-
1159-
.. [13]
1160-
Godwin, J. L. & Matthews, P. (2013). Classification and Detection of
1161-
Wind Turbine Pitch Faults Through SCADA Data Analysis. International
1162-
Journal of Prognostics and Health Management, 4.
1163-
1164-
.. [14]
1165-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1166-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1167-
learning techniques applied to operational
1168-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1169-
International Conference on Prognostics and Health Management
1170-
(ICPHM), 1-8.
1171-
1172-
.. [15]
1173-
García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M. & Papaelias,
1174-
M. (2012). `Condition monitoring of wind turbines: Techniques and
1175-
methods <https://doi.org/10.1016/j.renene.2012.03.003>`__. Renewable
1176-
Energy, 46, 169-178.
1177-
1178-
.. [16]
1179-
Tautz-Weinert, J. & Watson, S. J. (2017). `Using SCADA data for wind
1180-
turbine condition monitoring - a
1181-
review <https://doi.org/10.1049/iet-rpg.2016.0248>`__. IET Renewable
1182-
Power Generation, 11(4), 382-394.
1183-
1184-
.. [17]
1185-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1186-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1187-
learning techniques applied to operational
1188-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1189-
International Conference on Prognostics and Health Management
1190-
(ICPHM), 1-8.
1191-
1192-
.. [18]
1193-
Tautz-Weinert, J. & Watson, S. J. (2017). `Using SCADA data for wind
1194-
turbine condition monitoring - a
1195-
review <https://doi.org/10.1049/iet-rpg.2016.0248>`__. IET Renewable
1196-
Power Generation, 11(4), 382-394.
1197-
1198-
.. [19]
1199-
Tautz-Weinert, J. & Watson, S. J. (2017). `Using SCADA data for wind
1200-
turbine condition monitoring - a
1201-
review <https://doi.org/10.1049/iet-rpg.2016.0248>`__. IET Renewable
1202-
Power Generation, 11(4), 382-394.
1203-
1204-
.. [20]
1205-
Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y. & Qiu, Y. (2014).
1206-
`Wind turbine condition monitoring: Technical and commercial
1207-
challenges <https://doi.org/10.1002/we.1508>`__. Wind Energy, 17(5),
1208-
673-693.
1209-
1210-
.. [21]
1211-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1212-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1213-
learning techniques applied to operational
1214-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1215-
International Conference on Prognostics and Health Management
1216-
(ICPHM), 1-8.
1217-
1218-
.. [22]
1219-
Gill, S., Stephen, B. & Galloway, S. (2012). `Wind turbine condition
1220-
assessment through power curve copula
1221-
modeling <https://doi.org/10.1109/TSTE.2011.2167164>`__. IEEE
1222-
Transactions on Sustainable Energy, 3, 94-101.
1223-
1224-
.. [23]
1225-
Kusiak, A. & Li, W. (2011). `The prediction and diagnosis of wind
1226-
turbine faults <https://doi.org/10.1016/j.renene.2010.05.014>`__.
1227-
Renewable Energy, 36(1), 16-23.
1228-
1229-
.. [24]
1230-
Godwin, J. L. & Matthews, P. (2013). Classification and Detection of
1231-
Wind Turbine Pitch Faults Through SCADA Data Analysis. International
1232-
Journal of Prognostics and Health Management, 4.
1233-
1234-
.. [25]
1235-
Kusiak, A. & Li, W. (2011). `The prediction and diagnosis of wind
1236-
turbine faults <https://doi.org/10.1016/j.renene.2010.05.014>`__.
1237-
Renewable Energy, 36(1), 16-23.
1238-
1239-
.. [26]
1240-
Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. &
1241-
Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine
1242-
learning techniques applied to operational
1243-
data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE
1244-
International Conference on Prognostics and Health Management
1245-
(ICPHM), 1-8.
1246-
1247-
.. [27]
1248-
`Welcome to Python.org <https://www.python.org/>`__. (n.d.).
1249-
1250-
.. [28]
1251-
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B.,
1252-
Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.,
1253-
Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M. & Perrot, M.
1254-
(2011). `Scikit-learn: Machine Learning in
1255-
Python <https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf>`__.
1256-
Journal of Machine Learning Research, 12, 2825-2830.
1257-
1258-
.. [29]
1259-
`1.12. Multiclass and multilabel algorithms - Scikit-learn 0.18.2
1260-
documentation <https://scikit-learn.org/0.18/modules/multiclass.html>`__.
1261-
(n.d.).
1262-
1263-
.. [30]
1264-
`Decision Tree
1265-
Classifier <http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/lguo/decisionTree.html>`__.
1266-
(2010).
1267-
1268-
.. [31]
1269-
`Random forests - Classification
1270-
description <https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm>`__.
1271-
(n.d.).
1272-
1273-
.. [32]
1274-
Sutton, O. (2012). `Introduction to k Nearest Neighbour
1275-
Classification and Condensed Nearest Neighbour Data
1276-
Reduction <http://www.math.le.ac.uk/people/ag153/homepage/KNN/OliverKNN_Talk.pdf>`__.
1277-
1278-
.. [33]
1279-
`1.6. Nearest Neighbors - Scikit-learn 0.19.0
1280-
documentation <https://scikit-learn.org/0.19/modules/neighbors.html>`__.
1281-
(n.d.).
1282-
1283-
.. [34]
1284-
`1.10. Decision Trees - Scikit-learn 0.18.2
1285-
documentation <https://scikit-learn.org/0.18/modules/tree.html>`__.
1286-
(n.d.).
1287-
1288-
.. [35]
1289-
Lemaitre, G., Nogueira, F., Oliveira, D. & Aridas, C. (n.d.).
1290-
`Welcome to imbalanced-learn
1291-
documentation! <https://imbalanced-learn.org/stable/>`__.
1292-
1293-
.. [36]
1294-
`6.4. Introduction to Time Series
1295-
Analysis <https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm>`__.
1296-
(n.d.).
1093+
.. [1] Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S. & Fleming, P. (2012). `Use of SCADA Data for Failure Detection in Wind Turbines <https://doi.org/10.1115/ES2011-54243>`__. 2071-2079.
1094+
1095+
.. [2] Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. & Agogino, A. M. (2016). `Diagnosing wind turbine faults using machine learning techniques applied to operational data <https://doi.org/10.1109/ICPHM.2016.7542860>`__. 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), 1-8.
1096+
1097+
.. [3] Dienst, S. & Beseler, J. (2016). `Automatic Anomaly Detection in Offshore Wind SCADA Data <https://windeurope.org/summit2016/conference/submit-an-abstract/pdf/626738292593.pdf>`__.
1098+
1099+
.. [4] Tautz-Weinert, J. & Watson, S. J. (2017). `Using SCADA data for wind turbine condition monitoring - a review <https://doi.org/10.1049/iet-rpg.2016.0248>`__. IET Renewable Power Generation, 11(4), 382-394.
1100+
1101+
.. [5] Wind Turbine Condition Monitoring. (2015). [White Paper]. National Instruments.
1102+
1103+
.. [6] García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M. & Papaelias, M. (2012). `Condition monitoring of wind turbines: Techniques and methods <https://doi.org/10.1016/j.renene.2012.03.003>`__. Renewable Energy, 46, 169-178.
1104+
1105+
.. [7] Godwin, J. L. & Matthews, P. (2013). Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis. International Journal of Prognostics and Health Management, 4.
1106+
1107+
.. [8] Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y. & Qiu, Y. (2014). `Wind turbine condition monitoring: Technical and commercial challenges <https://doi.org/10.1002/we.1508>`__. Wind Energy, 17(5), 673-693.
1108+
1109+
.. [9] Gill, S., Stephen, B. & Galloway, S. (2012). `Wind turbine condition assessment through power curve copula modeling <https://doi.org/10.1109/TSTE.2011.2167164>`__. IEEE Transactions on Sustainable Energy, 3, 94-101.
1110+
1111+
.. [10] Kusiak, A. & Li, W. (2011). `The prediction and diagnosis of wind turbine faults <https://doi.org/10.1016/j.renene.2010.05.014>`__. Renewable Energy, 36(1), 16-23.
1112+
1113+
.. [11] `Welcome to Python.org <https://www.python.org/>`__. (n.d.).
1114+
1115+
.. [12] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M. & Perrot, M. (2011). `Scikit-learn: Machine Learning in Python <https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf>`__. Journal of Machine Learning Research, 12, 2825-2830.
1116+
1117+
.. [13] `1.12. Multiclass and multilabel algorithms - Scikit-learn 0.18.2 documentation <https://scikit-learn.org/0.18/modules/multiclass.html>`__. (n.d.).
1118+
1119+
.. [14] `Decision Tree Classifier <http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/lguo/decisionTree.html>`__. (2010).
1120+
1121+
.. [15] `Random forests - Classification description <https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm>`__. (n.d.).
1122+
1123+
.. [16] Sutton, O. (2012). `Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction <http://www.math.le.ac.uk/people/ag153/homepage/KNN/OliverKNN_Talk.pdf>`__.
1124+
1125+
.. [17] `1.6. Nearest Neighbors - Scikit-learn 0.19.0 documentation <https://scikit-learn.org/0.19/modules/neighbors.html>`__. (n.d.).
1126+
1127+
.. [18] `1.10. Decision Trees - Scikit-learn 0.18.2 documentation <https://scikit-learn.org/0.18/modules/tree.html>`__. (n.d.).
1128+
1129+
.. [19] Lemaitre, G., Nogueira, F., Oliveira, D. & Aridas, C. (n.d.). `Welcome to imbalanced-learn documentation! <https://imbalanced-learn.org/stable/>`__.
1130+
1131+
.. [20] `6.4. Introduction to Time Series Analysis <https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm>`__. (n.d.).
1132+
1133+
.. [21] `3.1. Cross-validation: Evaluating estimator performance - Scikit-learn 0.18.2 documentation <https://scikit-learn.org/0.18/modules/cross_validation.html>`__. (n.d.).
1134+
1135+
.. [22] Puget, J. F. (2016, July 5). Overfitting In Machine Learning - IT Best Kept Secret Is Optimization - CT904.
1136+
1137+
.. [23] Liang, Y. (2016). `Machine Learning Basics - Lecture 6: Overfitting <https://www.cs.princeton.edu/courses/archive/spring16/cos495/slides/ML_basics_lecture6_overfitting.pdf>`__.
1138+
1139+
.. [24] `Normalize Data - ML Studio (classic) - Azure <https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/normalize-data>`__. (2017, June 2).
1140+
1141+
.. [25] `4.3. Preprocessing data - Scikit-learn 0.19.0 documentation <https://scikit-learn.org/0.19/modules/preprocessing.html>`__. (n.d.).
1142+
1143+
.. [26] `3.3. Model evaluation: Quantifying the quality of predictions-Scikit-learn 0.19.2 documentation <https://scikit-learn.org/0.19/modules/model_evaluation.html>`__. (n.d.).
1144+
1145+
.. [27] de Ruiter, A. (2015, February 9). Performance measures in Azure ML: Accuracy, Precision, Recall and F1 Score.
1146+
1147+
.. [28] `Performance Measures for Machine Learning <https://www.cs.cornell.edu/courses/cs578/2003fa/performance_measures.pdf>`__. (n.d.).
1148+
1149+
.. [29] SAS Help Center: Precision, Recall, and the F1 Score.
1150+
1151+
.. [30] Rudy, J. (2013). `Plotting feature importance - Py-earth 0.1.0 documentation <https://contrib.scikit-learn.org/py-earth/auto_examples/plot_feature_importance.html>`__.
1152+
1153+
.. [31] Gutierrez-Osuna, R. (n.d.). L8: Nearest neighbors - CSCE 666 Pattern Analysis. CSE@TAMU.
1154+
1155+
.. [32] Maitra, R. (n.d.). Distribution-free Predictive Approaches.
1156+
1157+
.. [33] Reactive power - npower Business. (n.d.).
1158+
1159+
.. [34] Overbye, T. & Baldick, R. (n.d.). `EE369 POWER SYSTEM ANALYSIS - Lecture 18: Fault Analysis <https://users.ece.utexas.edu/~baldick/classes/369/Lecture_18.ppt>`__.
1160+
1161+
.. [35] `Statistics Show Bearing Problems Cause the Majority of Wind Turbine Gearbox Failures <https://www.energy.gov/eere/wind/articles/statistics-show-bearing-problems-cause-majority-wind-turbine-gearbox-failures>`__. (2015, September 17). Energy.gov.
1162+
1163+
.. [36] Sheng, S., McDade, M. & Errichello, R. (2011, October). `Wind Turbine Gearbox Failure Modes - A Brief <https://www.nrel.gov/docs/fy12osti/53084.pdf>`__. ASME/STLE 2011 International Joint Tribology Conference, Los Angeles, CA, USA.

0 commit comments

Comments
 (0)