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results_fault_diagnosis_1_v_all.py
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results_fault_diagnosis_1_v_all.py
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import winfault
import warnings
import numpy as np
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
%matplotlib inline
Turbine = winfault.WT_data()
scada = Turbine.scada_data
# warnings suppressed because there's loads of UndefinedMetricWarnings
warnings.filterwarnings("ignore")
features = ['WEC_ava_windspeed',
'WEC_ava_Rotation',
'WEC_ava_Power',
'WEC_ava_reactive_Power',
'WEC_ava_blade_angle_A',
'Inverter_averages',
'Inverter_std_dev',
'CS101__Spinner_temp',
'CS101__Front_bearing_temp',
'CS101__Rear_bearing_temp',
'CS101__Pitch_cabinet_blade_A_temp',
'CS101__Pitch_cabinet_blade_B_temp',
'CS101__Pitch_cabinet_blade_C_temp',
'CS101__Rotor_temp_1',
'CS101__Rotor_temp_2',
'CS101__Stator_temp_1',
'CS101__Stator_temp_2',
'CS101__Nacelle_ambient_temp_1',
'CS101__Nacelle_ambient_temp_2',
'CS101__Nacelle_temp',
'CS101__Nacelle_cabinet_temp',
'CS101__Main_carrier_temp',
'CS101__Rectifier_cabinet_temp',
'CS101__Yaw_inverter_cabinet_temp',
'CS101__Fan_inverter_cabinet_temp',
'CS101__Ambient_temp',
'CS101__Tower_temp',
'CS101__Control_cabinet_temp',
'CS101__Transformer_temp']
# This gets all the data EXCEPT the faults listed. Labels as nf for "no-fault"
nf = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",
'fault_case_1', True, 600, 600, [62, 9, 80])
# feeding fault
ff = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",
'fault_case_1', False, 600, 600, 62)
# generator heating fault
gf = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",
'fault_case_1', False, 600, 600, 9)
# excitation fault
ef = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",
'fault_case_1', False, 600, 600, 80)
print("=============================================================")
print("----------Training for detection of specific faults----------")
print("=============================================================")
print("=============================================================", "\n")
# select the faults to include.
faults = [ff, ef, gf]
# label and split into train, test and balanced training data
xtrain, xtest, ytrain, ytest, xbaltrain, ybaltrain = \
Turbine.get_test_train_data(features, faults, nf)
# labels for confusion matrix
labels = ['no-fault', 'feeding fault', 'excitation fault', 'generator fault']
print("========================================================")
print("------Building models using balanced training data------")
print("========================================================")
# set the parameter space (class_weight is None for the balanced training data)
parameter_space_bal = {
'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],
'C': [0.01, .1, 1, 10, 100, 1000], 'class_weight': [None]}
# train and test svm
clf_bal, bgg_bal = winfault.svm_class_and_score(
xbaltrain, ybaltrain, xtest, ytest, labels,
parameter_space=parameter_space_bal, bagged=True, score='recall_weighted',
search_type=GridSearchCV)
print("==========================================================")
print("------Building models using imbalanced training data------")
print("==========================================================")
# set the parameter space (class_weight is None for the balanced training data)
parameter_space = {
'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],
'C': [0.01, .1, 1, 10, 100, 1000],
'class_weight': [
{0: 0.01}, {1: 1}, {1: 2}, {1: 10}, {1: 50}, 'balanced']}
# train and test svm
clf, bgg = winfault.svm_class_and_score(
xtrain, ytrain, xtest, ytest, labels,
parameter_space=parameter_space, bagged=True, score='recall_weighted',
search_type=RandomizedSearchCV)
print("============================================================")
print("----------Training for detection of general faults----------")
print("============================================================")
print("============================================================", "\n\n")
# need to change this to the original way it was done!!!
# af = np.append(ff, ef)
# af = np.append(af, gf)
# xtrain, xtest, ytrain, ytest, xbaltrain, ybaltrain = \
# Turbine.get_test_train_data(features, [af], nf)
# # labels for confusion matrix
# labels = ['no-fault', 'fault']
# print("========================================================")
# print("------Building models using balanced training data------")
# print("========================================================")
# set the parameter space (class_weight is None for the balanced training data)
# parameter_space_bal = {
# 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],
# 'C': [0.01, .1, 1, 10, 100, 1000], 'class_weight': [None]}
# train and test svm
# clf_bal, bgg_bal = winfault.svm_class_and_score(
# xbaltrain, ybaltrain, xtest, ytest, labels,
# parameter_space=parameter_space_bal, bagged=True, score='recall_weighted',
# search_type=GridSearchCV)
# print("==========================================================")
# print("------Building models using imbalanced training data------")
# print("==========================================================")
# set the parameter space (class_weight is None for the balanced training data)
# parameter_space = {
# 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],
# 'C': [0.01, .1, 1, 10, 100, 1000],
# 'class_weight': [
# {0: 0.01}, {1: 1}, {1: 2}, {1: 10}, {1: 50}, 'balanced']}
# # train and test svm
# clf, bgg = winfault.svm_class_and_score(
# xtrain, ytrain, xtest, ytest, labels,
# parameter_space=parameter_space, bagged=True, score='recall_weighted',
# search_type=RandomizedSearchCV)