Let us verify the model which has been trained.
Switch to Python interpreter or create a Jupyter notebook with Python kernel and load the data.
from evaics.client import HttpClient import evaics.ml from datetime import datetime, timedelta import pandas as pd client = HttpClient('http://host', user='username', password='secret') # do not set if no ML kit server installed # set to ML kit server host/port if HMI and ML kit server are on different # ports client.mlkit = True req = client.history_df(params_csv='params_alarm.csv') # it is recommended to perform tests on a time frame with differs from the # one the model has been trained with data = req.t_start(datetime.now() - timedelta(days=10)).t_end( datetime.now() - timedelta(days=5)).fill( '1T').fetch(output='pandas') # pop alarm and time column as they are required for a comparison chart # only alarms = data.pop('alarm') times = data.pop('time') print(data)
Here is the data frame which is used by the model to predict alarm events:
pwr temp 0 78.598641 18.676482 1 78.896076 18.825306 2 79.158506 18.974394 3 79.385697 19.123713 4 79.577443 19.273229 ... ... ... 7135 79.878043 10.700966 7136 79.826741 10.745771 7137 79.766478 10.791908 7138 79.697268 10.839371 7139 79.619127 10.888154 [7140 rows x 2 columns]
Let us load the model and predict alarm events:
from evaics.ml.learning import Regression reg = Regression().load('alarms').with_prediction_data( data).prepare_data().verify_prepared() result = reg.predict()
The result data frame contains a single column called “alarm_predict” (Y_NAME + “_predict”).
Let us return “time” and “alarm” columns and compare the predicted data with the real one:
result['alarm'] = alarms result['time'] = times
If the sensor data is similar to the data which has been used to train the model, the prediction will be pretty accurate. Otherwise the results may differ.
To increase the model accuracy, continue training it with various production data. The more plant state the model has experience with, the more accurate predictions it can output.