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app.py
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app.py
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import pandas as pd
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import dash_daq as daq
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
from datetime import datetime, date
from data_preprocessing import data_preprocessing
import pickle
app = dash.Dash(
__name__,
meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}],
external_stylesheets=[dbc.themes.CYBORG],
)
server = app.server
app.title = "Predictive Maintenance Dashboard"
def logo(app):
title = html.H5(
"PREDICTIVE MAINTENANCE DASHBOARD FOR WIND TURBINES",
style={"marginTop": 5, "marginLeft": "10px"},
)
info_about_app = html.H6(
"This Dashboard is focused on estimating the Remaining Useful Life (RUL) in wind turbines via Machine Learning."
" RUL is defined as the time until the next fault and estimated via XGBoost algorithm.",
style={"marginLeft": "10px"},
)
logo_image = html.Img(
src=app.get_asset_url("dash-logo.png"), style={"float": "right", "height": 50}
)
link = html.A(logo_image, href="https://plotly.com/dash/")
return dbc.Row(
[dbc.Col([dbc.Row([title]), dbc.Row([info_about_app])]), dbc.Col(link)]
)
df, df_button, x_test, y_test, x_train = data_preprocessing()
predict_button = dbc.Card(
className="mt-auto",
children=[
dbc.CardBody(
[
html.Div(
[
dbc.Button(
"Predict",
id="predict-button",
color="primary",
outline=True,
size="lg",
style={"color": "#fec036"},
),
]
)
],
style={
"text-align": "center",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
"border-left": "1px solid rgb(216, 216, 216)",
"border-right": "1px solid rgb(216, 216, 216)",
"border-bottom": "1px solid rgb(216, 216, 216)",
},
)
],
)
get_new_information_button = dbc.Card(
className="mt-auto",
children=[
dbc.CardBody(
[
html.Div(
[
dbc.Button(
"Get New Data",
id="get-new-info-button",
color="primary",
outline=True,
size="lg",
style={"color": "#fec036"},
),
]
)
],
style={
"text-align": "center",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
"border-left": "1px solid rgb(216, 216, 216)",
"border-right": "1px solid rgb(216, 216, 216)",
"border-bottom": "1px solid rgb(216, 216, 216)",
},
)
],
)
graphs = dbc.Card(
children=[
dbc.CardBody(
[
html.Div(
[
dcc.Graph(
id="Main-Graph",
figure={
"layout": {
"margin": {"t": 30, "r": 35, "b": 40, "l": 50},
"xaxis": {
"dtick": 5,
"gridcolor": "#636363",
"showline": False,
},
"yaxis": {"showgrid": False, "showline": False},
"plot_bgcolor": "black",
"paper_bgcolor": "black",
"font": {"color": "gray"},
},
},
config={"displayModeBar": False},
),
html.Pre(id="update-on-click-data"),
],
style={"width": "98%", "display": "inline-block"},
),
html.Div(
[
dcc.Dropdown(
id="feature-dropdown",
options=[
{"label": label, "value": label} for label in df.columns
],
value="",
multi=False,
searchable=False,
)
],
style={
"width": "33%",
"display": "inline-block",
"color": "black",
},
),
html.Div(
[
dcc.DatePickerRange(
id="date-picker",
min_date_allowed=x_train.index[0], # need to change this
max_date_allowed=x_train.index[-1],
initial_visible_month=x_train.index[0],
start_date_placeholder_text="Start Period",
end_date_placeholder_text="End Period",
calendar_orientation="vertical",
),
html.Div(id="output-container-date-picker-range"),
],
style={
"vertical-align": "top",
"position": "absolute",
"right": "3%",
"float": "right",
"display": "inline-block",
"color": "black",
},
),
],
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
)
]
)
rul_estimation_indicator = dbc.Card(
children=[
dbc.CardHeader(
"System RUL Estimation (days)",
style={
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
daq.LEDDisplay(
id="rul-estimation-indicator-led",
size=24,
color="#fec036",
style={"color": "#black"},
backgroundColor="#2b2b2b",
value="0.0",
)
],
style={
"text-align": "center",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
]
)
info_box = dbc.Card(
children=[
dbc.CardBody(
[
html.Div(
dcc.Textarea(
id="Info-Textbox",
placeholder="This field is used to display information about a feature displayed "
"on the graph and estimated RUL. In order to estimate the RUL, use "
"the button 'Get New Data' and then, 'Predict'. The estimated RUL will be "
"printed.",
rows=8,
style={
"width": "100%",
"height": "100%",
"background-color": "black",
"color": "#fec036",
"placeholder": "#fec036",
"fontFamily": "Arial",
"fontSize": "16",
"padding": "12px",
"display": "inline-block",
},
)
)
],
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
)
blade_angle_display = dbc.Card(
children=[
dbc.CardHeader(
"Blade Angle",
style={
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
html.Div(
daq.Gauge(
id="blade-angle-information-gauge",
min=min(df["WEC: ava. blade angle A"]),
max=max(
df["WEC: ava. blade angle A"]
),
value=0,
showCurrentValue=True,
color="#fec036",
style={
"align": "center",
"display": "flex",
"marginTop": "5%",
"marginBottom": "-10%",
},
),
className="m-auto",
style={
"display": "flex",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
},
)
],
className="d-flex",
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
style={"height": "95%"},
)
active_power_display = dbc.Card(
children=[
dbc.CardHeader(
"Active Power [kW]",
style={
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
html.Div(
daq.Gauge(
id="active-power-information-gauge",
min=min(df["WEC: ava. Power"]),
max=max(
df["WEC: ava. Power"]
), # This one should be the theoretical maximum
value=100,
showCurrentValue=True,
color="#fec036",
style={
"align": "center",
"display": "flex",
"marginTop": "5%",
"marginBottom": "-10%",
},
),
className="m-auto",
style={
"display": "flex",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
},
)
],
className="d-flex",
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
style={"height": "95%"},
)
active_power_from_wind_display = dbc.Card(
children=[
dbc.CardHeader(
"Active Power Available from Wind [kW]",
style={
"display": "inline-block",
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
html.Div(
daq.Gauge(
id="active-power-from-wind-information-gauge",
min=min(df["WEC: ava. available P from wind"]),
max=max(df["WEC: ava. available P from wind"]),
value=10,
showCurrentValue=True,
color="#fec036",
style={
"align": "center",
"display": "flex",
"marginTop": "5%",
"marginBottom": "-10%",
},
),
className="m-auto",
style={
"display": "flex",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
},
)
],
className="d-flex",
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
style={"height": "95%"},
)
wind_speed_information = dbc.Card(
className="mt-auto",
children=[
dbc.CardHeader(
"Wind Speed [m/s]",
style={
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
html.Div(
daq.Gauge(
id="wind-power-information-gauge",
min=min(df["WEC: ava. windspeed"]),
max=int(max(df["WEC: ava. windspeed"])),
value=0,
showCurrentValue=True,
color="#fec036",
style={
"align": "center",
"display": "flex",
"marginTop": "5%",
"marginBottom": "-10%",
},
),
className="m-auto",
style={
"display": "flex",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
},
)
],
className="d-flex",
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
style={"height": "95%"},
)
reactive_power_display = dbc.Card(
className="mt-auto",
children=[
dbc.CardHeader(
"Reactive Power [kVAR]",
style={
"text-align": "center",
"color": "white",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
dbc.CardBody(
[
html.Div(
daq.Gauge(
id="reactive-power-information-gauge",
min=min(df["WEC: ava. reactive Power"]),
max=max(df["WEC: ava. reactive Power"]),
value=0,
showCurrentValue=True,
color="#fec036",
style={
"align": "center",
"display": "flex",
"marginTop": "5%",
"marginBottom": "-10%",
},
),
className="m-auto",
style={
"display": "flex",
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
},
)
],
className="d-flex",
style={
"backgroundColor": "black",
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
),
],
style={"height": "95%"},
)
gauge_size = "auto"
sidebar_size = 12
graph_size = 10
app.layout = dbc.Container(
fluid=True,
children=[
logo(app),
dbc.Row(
[
dbc.Col(
[
dbc.Row(dbc.Col(rul_estimation_indicator,
xs=sidebar_size,
md=sidebar_size,
lg=sidebar_size,
width=sidebar_size)
),
dbc.Row(dbc.Col(info_box,
xs=sidebar_size,
md=sidebar_size,
lg=sidebar_size,
width=sidebar_size)
),
dbc.Row(dbc.Col(get_new_information_button,
xs=sidebar_size,
md=sidebar_size,
lg=sidebar_size,
width=sidebar_size)
),
dbc.Row(dbc.Col(predict_button,
xs=sidebar_size,
md=sidebar_size,
lg=sidebar_size,
width=sidebar_size)
),
]
),
dbc.Col(graphs,
xs=graph_size,
md=graph_size,
lg=graph_size,
width=graph_size)
],
# justify='start',
style={
"display": "flex",
"marginBottom": "-3%"
},
),
dbc.Row(
[
dbc.Col(active_power_display,
xs=gauge_size,
md=gauge_size,
lg=gauge_size,
width=gauge_size),
dbc.Col(active_power_from_wind_display,
xs=gauge_size,
md=gauge_size,
lg=gauge_size,
width=gauge_size),
dbc.Col(reactive_power_display,
xs=gauge_size,
md=gauge_size,
lg=gauge_size,
width=gauge_size),
dbc.Col(wind_speed_information,
xs=gauge_size,
md=gauge_size,
lg=gauge_size,
width=gauge_size),
dbc.Col(blade_angle_display,
xs=gauge_size,
md=gauge_size,
lg=gauge_size,
width=gauge_size),
],
justify='end',
style={
"marginTop": "3%"
},
),
],
)
def fig_update_layout(fig):
fig.update_layout(
xaxis=dict(
showline=False,
showgrid=False,
showticklabels=True,
zeroline=False,
gridcolor="#636363",
linecolor="rgb(204, 204, 204)",
linewidth=2,
tickfont=dict(family="Arial", size=12, color="white", ),
title=dict(font=dict(family="Arial", size=24, color="#fec036"), ),
),
yaxis=dict(
showline=False,
showgrid=False,
showticklabels=True,
zeroline=False,
gridcolor="#636363",
linecolor="rgb(204, 204, 204)",
linewidth=2,
tickfont=dict(family="Arial", size=12, color="white", ),
title=dict(font=dict(family="Arial", size=24, color="#fec036"), ),
),
autosize=True,
margin=dict(autoexpand=True, l=50, b=40, r=35, t=30),
showlegend=False,
paper_bgcolor="black",
plot_bgcolor="black",
title=dict(
font=dict(family="Arial", size=32, color="darkgray"),
xanchor="center",
yanchor="top",
y=1,
x=0.5,
),
)
return fig
@app.callback(
[
Output("Main-Graph", "figure"),
Output("rul-estimation-indicator-led", "value"),
Output("Info-Textbox", "value"),
],
[
Input("feature-dropdown", "value"),
Input("date-picker", "start_date"),
Input("date-picker", "end_date"),
Input("get-new-info-button", "n_clicks"),
Input("predict-button", "n_clicks"),
],
)
def update_graph(selected_column, start_date, end_date, n_get_new_info, n_pred):
if n_pred is None: # here is my work before prediction button is activated.
value_rul = 0.0
information_update = (
"This field is used to display information about a feature displayed "
"on the graph and estimated RUL. In order to estimate the RUL, use "
"the button 'Get New Data' and then, 'Predict'. The estimated RUL will be "
"printed."
)
if n_get_new_info is None:
if selected_column in list(df):
if start_date and end_date:
start_date_object = datetime.strptime(start_date, "%Y-%m-%d")
end_date_object = datetime.strptime(end_date, "%Y-%m-%d")
mask = (df.index > start_date_object) & (
df.index <= end_date_object
)
df_within_dates = df.loc[mask]
fig = go.Figure(
data=[
go.Scatter(
x=df_within_dates.index,
y=df_within_dates[selected_column],
)
]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
elif start_date:
start_date_object = datetime.strptime(start_date, "%Y-%m-%d")
mask = df.index > start_date_object
df_within_dates = df.loc[mask]
fig = go.Figure(
data=[
go.Scatter(
x=df_within_dates.index,
y=df_within_dates[selected_column],
)
]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
fig = go.Figure(
data=[go.Scatter(x=df.index, y=df[selected_column])]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
fig = go.Figure(
data=[go.Scatter(x=df.index, y=df["WEC: ava. windspeed"])]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
_information_update = (
"New information is received for the last week and covers periods from "
+ str(df_button.index[0])
+ " to "
+ str(df_button.index[-1])
+ ". To predict"
" RUL, use 'Predict' button. To view data for the aforementioned period, choose"
" appropriate dates on the calendar."
)
if selected_column in list(df_button):
fig = go.Figure(
data=[
go.Scatter(x=df_button.index, y=df_button[selected_column])
]
)
fig = fig_update_layout(fig)
return fig, value_rul, _information_update
else:
fig = go.Figure(
data=[
go.Scatter(x=df_button.index, y=df_button["WEC: ava. windspeed"])
]
)
fig = fig_update_layout(fig)
return fig, value_rul, _information_update
else: # Prediction button is pressed
if n_get_new_info is None:
value_rul = 0.0
information_update = " 'Predict' button will not produce a desired result until new information is received." \
" To predict RUL, please use 'Get New Data' button."
if selected_column in list(df):
if start_date and end_date:
start_date_object = datetime.strptime(start_date, "%Y-%m-%d")
end_date_object = datetime.strptime(end_date, "%Y-%m-%d")
mask = (df.index > start_date_object) & (
df.index <= end_date_object
)
df_within_dates = df.loc[mask]
fig = go.Figure(
data=[
go.Scatter(
x=df_within_dates.index,
y=df_within_dates[selected_column],
)
]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
elif start_date:
start_date_object = datetime.strptime(start_date, "%Y-%m-%d")
mask = df.index > start_date_object
df_within_dates = df.loc[mask]
fig = go.Figure(
data=[
go.Scatter(
x=df_within_dates.index,
y=df_within_dates[selected_column],
)
]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
fig = go.Figure(
data=[go.Scatter(x=df.index, y=df[selected_column])]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
fig = go.Figure(
data=[go.Scatter(x=df.index, y=df["WEC: ava. windspeed"])]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
model = pickle.load(open("assets/xgb_reg.pkl", "rb"))
y_pred = model.predict(x_test)
df_out = pd.DataFrame()
df_out["pred"] = y_pred
value_rul = round(max(df_out["pred"]))
information_update = "RUL is estimated based on the readings from the last week: " "from " + str(
x_test.index[0]
) + " to " + str(
x_test.index[-1]
)
start_date = df_button.index[0]
end_date = df_button.index[-1]
if selected_column in list(df_button):
fig = go.Figure(
data=[
go.Scatter(
x=df_button.index,
y=df_button[selected_column],
)
]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
else:
fig = go.Figure(
data=[go.Scatter(x=df.index, y=df_button["WEC: ava. windspeed"])]
)
fig = fig_update_layout(fig)
return fig, value_rul, information_update
@app.callback(
[
Output("active-power-information-gauge", "value"),
Output("active-power-from-wind-information-gauge", "value"),
Output("wind-power-information-gauge", "value"),
Output("reactive-power-information-gauge", "value"),
Output("blade-angle-information-gauge", "value"),
],
Input("Main-Graph", "clickData"),
)
def display_click_data(clickData):
if clickData:
data_time = clickData["points"][0]["x"]
value_active_power = df["WEC: ava. Power"].loc[df.index == data_time].values[0]
value_active_power_wind = (
df["WEC: ava. available P from wind"].loc[df.index == data_time].values[0]
)
value_reactive_power = (
df["WEC: ava. reactive Power"].loc[df.index == data_time].values[0]
)
value_wind_speed = (
df["WEC: ava. windspeed"].loc[df.index == data_time].values[0]
)
value_blade_angle = (
df["WEC: ava. blade angle A"].loc[df.index == data_time].values[0]
)
return (
value_active_power,
value_active_power_wind,
value_wind_speed,
value_reactive_power,
value_blade_angle,
)
else:
value_active_power = 0
value_active_power_wind = 0
value_reactive_power = 0
value_wind_speed = 0
value_blade_angle = 0
return (
value_active_power,
value_active_power_wind,
value_wind_speed,
value_reactive_power,
value_blade_angle,
)
if __name__ == "__main__":
app.run_server(debug=True, use_reloader=True)