Yokogawa-BrigitteZacharia for Yokogawa Technologies Solutions India

Posted on Oct 22, 2021

BYOML on e-RT3 Plus

#ert3plus #controller #machinelearning #byoml

Introduction

e-RT3 Plus is a sophisticated controller that provides high usability. As we continue to explore the multiple ways in which e-RT3 Plus can be utilized, the benefits it offers in various scenarios emerge. In today's world, machine learning (ML) is increasingly being used to model autonomous industrial systems. In this article, we explore how to run custom ML models on e-RT3 Plus.

The aim of this article is to demonstrate how to run a machine learning model on e-RT3 Plus for prediction. Here, we demonstrate how to predict the downtime of a pH sensor. To achieve this, two Node-RED flows are created in e-RT3 Plus for data gathering and prediction. The prediction is based on creating a machine learning model that studies the impedance and the oxidation-reduction potential of the sensor. In this article, we have shown how to create a sample model. However, you can use any method for creating your own model. You can also "Bring your own Machine Learning" (BYOML) model and run it on e-RT3 Plus. While we have used a pH sensor and an LC31 module to read the process values, you can modify the use case and implementation according to your requirements.

The LC31 module is connected to a pH sensor that sends process data to e-RT3 Plus. Node-RED is installed on e-RT3 Plus and two flows are created. The first flow gathers data from the LC31 module and stores it in InfluxDB. InfluxDB is installed on a server. The second flow uses a machine learning model to predict the downtime of the pH sensor. The results of prediction can be viewed on the Node-RED dashboard.

The following image shows the hardware setup used for this tutorial.

Workflow

The following figure shows the workflow for predicting the downtime of the pH sensor.

Prerequisites

This tutorial focuses on deploying machine learning models on the e-RT3 Plus device. Therefore, you must have a working knowledge of the e-RT3 Plus device and its supported modules along with machine learning principles, model creation, and Node-RED programming.

Before you start this tutorial, the following requirements must be met:

Hardware setup must be complete.
InfluxDB must be installed on the server and a database must be created to store the process data.
Node-RED must be installed on e-RT3 Plus with the following node modules:
- m3io nodes
- InfluxDB nodes
- Machine learning nodes
- Node-RED dashboard nodes
Docker must be installed and running on the server.

Follow these links for more information on:

Install Node-RED machine learning and dashboard nodes

To predict the downtime of the pH sensor we use a machine learning model in the Node-RED flow. Therefore, we must install the node-red-contrib-machine-learning module which contains a set of nodes that offer machine learning functionalities.

Note: Node-RED must be installed on e-RT3 Plus before you can install the machine learning nodes and Node-RED dashboard.

Follow these steps to install machine learning and dashboard nodes in Node-RED:

Open the Node-RED editor.
In the upper-right corner, select Menu > Manage palette. The User Settings page appears.
On the left pane, click the Palette tab and then click the Install tab.
In the search modules box, type node-red-contrib-machine-learning.
From the search results that appear, click Install next to the node-red-contrib-machine-learning module name.
In the dialog box that appears, click Install.

The machine learning nodes are installed.
In the search modules box, type node-red-dashboard.
From the search results that appear, click Install next to the node-red-dashboard module name.
In the dialog box that appears, click Install.

The dashboard nodes are installed.

Getting Started

After getting the hardware setup ready and installing the required software, we are ready to start programming the e-RT3 Plus device to predict the downtime of the pH sensor.

This tutorial can be broadly divided into three parts:

Gathering data for creating a machine learning model
Using the collected data to generate a model
Using the generated model in e-RT3 Plus for prediction

To predict the downtime of the pH sensor, the following steps must be completed:

Configuring the LC31 module
Reading process data and writing it to InfluxDB
Exporting the InfluxDB data to a .csv file
Bring your own machine learning model
Using the generated model for prediction
Visualizing the prediction and process data on the Node-RED dashboard

Configuring LC31 module

First, we must configure the LC31 module in order to read data from it. The LC31 module enables e-RT3 Plus to be easily connected to external devices such as temperature controllers or pH sensors.

Configuring the LC31 module involves three steps:

Writing the configuration code in Python
Transferring the code to the e-RT3 Plus device
Executing the code on e-RT3 Plus

The LC31 module registers are read by using the Modbus protocol. The LC31 module enables us to read the calculated pH, and process values such as impedance by accessing specific registers. By using these process values, we generate the machine learning model.

Writing the configuration code in Python

To start reading data from the LC31 module, we must write a Python program that performs the following configurations:

Set the power supply of the pH sensor to 4.5V. This is done by writing the value "9000" to channel 1 of the DA module.
Provide a delay of 30 seconds to ensure that the startup is complete before configuring the sensor.
Configure the Modbus master and communication setting parameter to enable communication.
Configure the registers of LC31 module to enable data to be read from the communication table.

To write the values to the necessary registers, we use the m3io library functions.

For more information on the Input/Output relays and registers, refer to the LC31 User's Manual.

The sample code to complete these configurations is as follows.

import ctypes
import time


#Constant declaration
COM_SETTING = 1344      #Communication setting parameters
FUNC_CODE = 4           #Function code
TOP_REG_NUM = 0         #Read first register number
READ_NUM = 25           #Number of read points

#Library load so file
libc = ctypes.cdll.LoadLibrary("/usr/local/lib/libm3.so.1")

#Specify unit 0
c_unit = ctypes.c_int(0)

#Specify slot 2 (for LC31 module)
c_slot = ctypes.c_int(2)

#Creating a short type array with 1 element
short_arr = ctypes.c_short * 1

#Creating a short type array with the number of elements READ_NUM
short_read_num_arr = ctypes.c_short * READ_NUM

#DA- set voltage
c_pos_da = ctypes.c_int(1)      #Writing to channel 1 in DA module
c_num_da = ctypes.c_int(1)      #Number of writing points
c_slot_da = ctypes.c_int(4)     #DA is in slot number 4
data_da = [9000]                #9000 to supply 4.5V
short_arr_da = ctypes.c_short * 1  
c_data_da = short_arr_da(*data_da)
libc.writeM3IoRegister(c_unit, c_slot_da, c_pos_da, c_num_da, c_data_da) 

#Delay for the sensor to start before settings
time.sleep(30)

#Modbus setting- setting LC31 as 'modbus master'
c_pos = ctypes.c_int(9)         #Write register number
c_num = ctypes.c_int(1)         #Number of writing points
data = [0]                      #Write value
short_arr = ctypes.c_short * 1  #Creating a short type array with 1 element
c_data = short_arr(*data)
libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)    

#Modbus settings- communication setting parameters
c_pos = ctypes.c_int(10)         #Write register number
c_num = ctypes.c_int(1)          #Number of writing points
data = [COM_SETTING]             #Write value
c_data = short_arr(*data)        #Store data in short type array
libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)

#Set requirements- turn on Setting Request
c_pos = ctypes.c_int(41)    #Write relay number
c_data = ctypes.c_uint16(0) #Write data (reset)
libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)
c_data = ctypes.c_uint16(1) #Write data
libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)

setting_fin_flg = False
while setting_fin_flg == False:

    #Setting completion status check
    c_pos = ctypes.c_int(9)   #Read relay number
    c_data = short_arr()
    libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

    if c_data[0] == 1:
        setting_fin_flg = True

#Setting error status check
c_pos = ctypes.c_int(11)   #Read relay number
c_data = short_arr()
libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

if c_data == 1:
    print("SETTING ERROR")

else:
    #Destination slave address code setting
    c_pos = ctypes.c_int(1281)         #Write register number
    c_num = ctypes.c_int(1)            #Number of writing points
    data = [1]                         #Write value
    c_data = short_arr(*data)          #Store data in short type array
    libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)

    #Function code setting
    c_pos = ctypes.c_int(1289)         #Write register number
    c_num = ctypes.c_int(1)            #Number of writing points
    data = [FUNC_CODE]                 #Write value
    c_data = short_arr(*data)          #Store data in short type array
    libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)

    #First register number setting
    c_pos = ctypes.c_int(1290)         #Write register number
    c_num = ctypes.c_int(1)            #Number of writing points
    data = [TOP_REG_NUM]               #Write value
    c_data = short_arr(*data)          #Store data in short type array
    libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)

    #Read point setting
    c_pos = ctypes.c_int(1291)         #Write register number
    c_num = ctypes.c_int(1)            #Number of writing points
    data = [READ_NUM]                  #Write value
    c_data = short_arr(*data)          #Store data in short type array
    libc.writeM3IoRegister(c_unit, c_slot, c_pos, c_num, c_data)

    #Send request
    c_pos = ctypes.c_int(49)    #Write relay number
    c_data = ctypes.c_uint16(0) #Write data (reset)
    libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)
    c_data = ctypes.c_uint16(1) #Write data
    libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)

    resp_fin_flg = False
    resp_error_flg = False
    while resp_fin_flg == False:

        #Response request status confirmation
        c_pos = ctypes.c_int(17)   #Read relay number
        c_data = short_arr()
        libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

        if c_data[0] == 1:
            resp_fin_flg = True

        else:
            #Request error status check
            c_pos = ctypes.c_int(18)   #Read relay number
            c_data = short_arr()
            libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

            if c_data[0] == 1:
                resp_fin_flg = True
                resp_error_flg = True

    if resp_error_flg == True:
        print("REQUEST ERROR")

    else:
        print("Success!")

Transferring the code to the e-RT3 Plus device

Once you have completed writing the Python code, save the file and transfer it to the e-RT3 Plus device by using WinSCP.

For more information on transferring files by using WinSCP, refer to Using WinSCP to transfer files to e-RT3 Plus.

Executing the code on e-RT3 Plus

Now, we must execute the Python code to enable data to be read from the LC31 module.

Follow these steps to execute the Python code:

Open an SSH terminal to the e-RT3 Plus device.

For more information about connecting to e-RT3 Plus using SSH, refer to Remote Communication with e-RT3 Plus.
Use the cd command to navigate to the directory in which you copied the Python code.
Run the following command to execute the Python code.
```
python3 {FILENAME_OF_PYTHON_CODE}.py
```
Here, FILENAME_OF_PYTHON_CODE refers to the filename of the Python code which you have just copied to the device.

If the message "Success!" is displayed, we can start reading data from the LC31 module. If the message "REQUEST ERROR" is displayed, the configuration has failed, or if the message "SETTING ERROR" is displayed, it indicates an error in the settings.

Reading process data and writing it to InfluxDB

In this section, we create a Node-RED flow that reads data from the LC31 module and writes it to InfluxDB in the appropriate format. We will read two pH measurements, an oxidation-reduction potential measurement, and two impedance measurements.

Follow these steps to create a Node-RED flow that reads data from the LC31 module registers and writes it to InfluxDB:

Open the Node-RED editor.
To read data from the LC31 module every ten seconds, select the inject node and configure its properties.

To refresh the data in the LC31 module on every read attempt, perform these steps:

Open a text file on your computer and write the following Python code.

import ctypes

libc = ctypes.cdll.LoadLibrary("/usr/local/lib/libm3.so.1")

#Specify unit 0
c_unit = ctypes.c_int(0)

#Specify slot 2 (for LC31 module)
c_slot = ctypes.c_int(2)

#Creating a short type array with 1 element
short_arr = ctypes.c_short * 1

c_pos = ctypes.c_int(49)    #Write relay number
c_data = ctypes.c_uint16(0) #Write data (reset)
libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)
c_data = ctypes.c_uint16(1) #Write data
libc.writeM3OutRelayP(c_unit, c_slot, c_pos, c_data)

resp_fin_flg = False
resp_error_flg = False
while resp_fin_flg == False:

    #Response request status confirmation
    c_pos = ctypes.c_int(17)   #Read relay number
    c_data = short_arr()
    libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

    if c_data[0] == 1:
        resp_fin_flg = True

    else:
        #Request error status check
        c_pos = ctypes.c_int(18)   #Read relay number
        c_data = short_arr()
        libc.readM3InRelayP(c_unit, c_slot, c_pos, c_data)

        if c_data[0] == 1:
            resp_fin_flg = True
            resp_error_flg = True

if resp_error_flg == True:
    print("REQUEST ERROR")

else:
    print("REQUEST SUCCESSFUL")

Save the file as <YOUR_READREQ_FILENAME>.py.

Here, YOUR_READREQ_FILENAME refers to the name of the Python file.
Copy the file to the e-RT3 Plus device by using WinSCP.

Note: For more information on transferring files by using WinSCP, refer to Using WinSCP to transfer files to e-RT3 Plus.
On the left pane, expand function, select the exec node, and drag it to the work area.
Double-click the created node.

The Edit exec node pane appears.
In the Command box, specify the following command.
```
python3 <PYTHONCODE_FILEPATH>/<YOUR_READREQ_FILENAME>.py
```
Here,

PYTHONCODE_FILEPATH refers to the location in the e-RT3 Plus device which you have copied the Python code.

YOUR_READREQ_FILENAME refers to the name of the Python file.

This command runs the Python code to refresh the data in the LC31 module on every read attempt.
From the Output drop-down list, select when the command is complete-exec mode.
In the Name box, specify the name of the node.
In the upper-right corner of the Edit exec node pane, click Done.

To read the pH value from the LC31 module register, perform these steps:
1. On the left pane, expand m3io, select the readM3IoRegister node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit readM3ioRegister node pane appears.
3. In the Name box, specify the name measurement parameter.
4. In the Unit box, specify the e-RT3 Plus base module number to which the module is connected. Since we have only one unit in the setup, the value is set to 0.
5. In the Slot box, specify the slot number in which the module is connected.
6. In the Position box, specify the position of the register from which you want to read data. The first pH value must be read from register 1544.
7. In the upper-right corner of the Edit readM3IoRegister node pane click Done.

Repeat step 4 to add nodes to read each of the following measurement values from the registers of the LC31 module:

Measurement	Type	Register address
pH_measure2	Second pH measurement	1555
imp_pHORP	Oxidation-reduction potential	1558
imp_ref1	First impedance measurement	1560
imp_ref2	Second impedance measurement	1561

For more information on the registers, refer to the LC31 User's Manual.

To assign names to each register, perform these steps:

On the left pane, expand function, select the function node, and drag it to the work area.
Double-click the created node.

The Edit function node pane appears.

Click the Setup tab, and type the following code in the editor.

const m3io = global.get('m3io');
const posToNameMapper = {
    "1544":"pH_measure1",
    "1555":"pH_measure2",
    "1558":"imp_pHORP",
    "1560":"imp_ref1",
    "1561":"imp_ref2"
}
const get_topic = (topic) => {
    const io_cofig = topic.split(",")
    const ioName = m3io.getm3ioname(parseInt(io_cofig[0]),parseInt(io_cofig[1]));
    const pos = io_cofig[2]
    return [ioName,posToNameMapper[pos].toUpperCase()];

}
context.set("get_topic", get_topic)

Click the Function tab and type the following code in the editor.

const new_topic_data = context.get("get_topic")(msg.topic)
msg[new_topic_data[0]] = new_topic_data[1]
return msg;

In the upper-right corner of the Edit function node pane, click Done.

To create a key-value pair of the register name and its value, and send the object with the set of five register values, perform these steps:
1. On the left pane, expand sequence, select the join node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit join node pane appears.
3. From the Mode drop-down list, select manual.
4. From the Combine each drop-down list, select msg, and then type payload in the adjacent box.
5. From the to create drop-down list, select a key/Value Object.
6. In the using the value of box, type LC31.
7. In the After a number of message parts box, type 5.
8. In the upper-right corner of the Edit join node pane, click Done.
To write the data to InfluxDB, perform these steps:
1. On the left pane, expand storage, select the influxdb out node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit influxdb out node pane appears.
3. In the Name box, specify the name of the node.
4. Next to the Server drop-down list, click the Edit icon, and specify the following on the page that appears:
  1. In the Name box, specify a name for the server.
  2. In the URL box, specify the URL in the following format:
    
    http://<SERVER_IP_ADDRESS>:<PORT_NUMBER>
    
    Here,
    
    <SERVER_IP_ADDRESS> refers to the IP address of the server on which the database is hosted.
    
    <PORT_NUMBER> refers to the port number to access the database.
  3. In the upper-right corner of the Edit influxdb out node pane, click Update.
5. In the Database box, specify the name of the InfluxDB database you created.
6. In the Measurement box, specify the name of the measurement that is written to the database.
7. In the upper-right corner of the Edit influxdb out node pane, click Done.
To display the first pH measurement as a trend on the Node-RED dashboard, perform these steps:
1. On the left pane, expand dashboard, select the chart node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit chart node pane appears.
3. Click the Edit icon next to the Group drop-down list and configure the properties as follows:
  1. In the Name box, specify the name of the chart.
  2. From the Tab drop-down list, select the name of the dashboard on which you want to display the chart.
    
    Note: If you are configuring the first element of the dashboard, click the Edit icon next to the Tab drop-down list and configure the dashboard settings.
  3. In the Width box, specify the width of the line chart.
  4. Select the Display group name check box.
  5. In the upper-left corner of the Edit chart node pane, click Update.
Repeat step 9 to add charts to monitor the trend of the remaining impedance and pH measurements.
To display the process data being written to InfluxDB on the debug pane, perform these steps:
1. On the left pane, select the debug node, and drag it onto the work area.
2. Double-click the created node.
  
  The Edit debug node pane appears.
3. From the Output drop-down list, select msg, and then type payload in the adjacent box.
4. In the To section, select the debug window check box.
5. In the Name box, specify a name for the node.
6. In the upper-right corner of the Edit debug node pane, click Done.
Now that all the necessary nodes are available on the work area, connect the nodes to create the final flow. The final flow should look something like this.
On the menu bar, click Deploy to activate the flow.

The data from the registers of the LC31 module are read and stored in InfluxDB.
In the upper-right corner of the Node-RED editor, click the Debug tab.

The debug pane appears, displaying the values written to InfluxDB.

Exporting the InfluxDB data to a .csv file

Now we must export InfluxDB data to a .csv file so that it can be used to train and test the machine learning model.

To learn how to export data from InfluxDB to a .csv file, refer to Exporting data from InfluxDB to a .csv file.

Bring your own machine learning model

With the data exported to the .csv file, you can now build, train, and create a model. You can build your own machine learning model using any of the model creation methods, or you can use the sample model created for predicting the downtime of the pH sensor. BYOML is a useful feature that helps you to use your own machine learning model on e-RT3 Plus.

Note: The model provided is for sample usage and does not include support.

Since we will be executing the ML model on e-RT3 Plus, it is necessary to verify if the model is compatible with the python3 packages installed on e-RT3 Plus.

To view the versions of the python3 packages installed on e-RT3 Plus, establish an SSH connection and run the following command.

pip3 list

Follow these steps to create a model:

Use the data exported into the .csv file to prepare the training and testing data (train.csv and test.csv).

Note: To prepare the training and testing data, we use only two columns, "imp_pHORP" and "imp_ref1", in the .csv files. Additionally, a third column LABEL is added which indicates the operational status of the pH sensor. The value 1 in the LABEL column indicates that the sensor is in good operating condition and 0 indicates the downtime of the sensor.
Prepare a requirements.txt file to specify the environment information in the following format:
```
numpy==1.13.3
pandas==0.22.0
scikit-learn==0.19.1
scipy==0.19.1
```
Note: It is important to note that, the environment in which the model is created must match the execution environment.

Create a file with the following Python code to train and create the machine learning model.

# import required libraries
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
import pickle
import sys
import warnings
import os

# turn off warnings
if not sys.warnoptions:
    warnings.simplefilter("ignore")
    os.environ["PYTHONWARNINGS"] = "ignore"

# read training and testing data
train_df=pd.read_csv('train.csv')
test_df=pd.read_csv('test.csv')

# create training, testing- features(X) and labels(y)
X_train=train_df.drop('LABEL',axis=1)
y_train=train_df['LABEL']

X_test=test_df.drop('LABEL',axis=1)
y_test=test_df['LABEL']

# train model on training data
model=LogisticRegression(random_state=42,C= 100, penalty='l2', solver= 'newton-cg')
model.fit(X_train,y_train)

# get predictions on test data
y_pred = model.predict(X_test)

# print classification report
print("Classification report:")
print(classification_report(y_test, y_pred))

# print confusion matrix
print("Confusion matrix:")
print(confusion_matrix(y_test, y_pred))

# print accuracy
print("Accuracy = {} %".format(np.round(accuracy_score(y_test, y_pred)*100,2)))

# save the model
filename = 'lr_model.b'
pickle.dump(model, open(filename, 'wb'))

Save the file as model.py.
Create a file with the following Docker commands and save it as Dockerfile.
```
FROM python:3.6

RUN mkdir <DOCKER_WORKING_DIRECTORY>
VOLUME <DOCKER_WORKING_DIRECTORY>

WORKDIR <DOCKER_WORKING_DIRECTORY>
COPY . .
RUN pip install -r requirements.txt
CMD ["python3", "model.py"]
```
Here, <DOCKER_WORKING_DIRECTORY> refers to the specified working directory in the Docker environment.

Note: Ensure that requirements.txt, train.csv, test.csv, Dockerfile, and model.py are saved in a common folder.
Start Command Prompt.
Navigate to the directory in which the files are saved.
Run the following command to build the model image.
```
docker build -t <IMAGE_NAME> .
```
Here, <IMAGE_NAME> refers to the name of the image.
Run the following command to create a model with the specified environment settings:
```
docker run --rm -v <ML_MODEL_FILEPATH>:<DOCKER_WORKING_DIRECTORY> --name <IMAGE_NAME> <IMAGE_NAME>
```
Here,

<ML_MODEL_FILEPATH> refers to the folder where you want to save the generated model.

<DOCKER_WORKING_DIRECTORY> refers to the working directory in the Docker environment.

<IMAGE_NAME> refers to the name of the image.

The model lr_model.b is created in the specified location.
Copy the model to the e-RT3 Plus device by using WinSCP.

For more information on transferring files by using WinSCP, refer to Using WinSCP to transfer files to e-RT3 Plus.

Using the generated model for prediction

Now that we have the machine learning model ready, we can use it in a Node-RED flow to predict the downtime of the pH sensor. In this flow, we read the oxidation-reduction potential measurement and the impedance measurement (register 1558 and 1560), which were used while generating the model, and perform prediction accordingly.

Follow these steps to create a Node-RED flow that uses a machine learning model to predict the downtime of the pH sensor:

Open the Node-RED editor.
To read data from the LC31 module every ten seconds, select the inject node and configure its properties.
To read the impedance process values in registers 1558 and 1560, follow step 4 of the data gathering flow.
To create an array of the process values read in the previous step, perform these steps:
1. On the left pane, expand sequence, select the join node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit join node pane appears.
3. From the Mode drop-down list, select manual.
4. From the Combine each drop-down list, select msg, and then type payload in the adjacent box.
5. From the to create drop-down list, select an Array.
6. In the After a number of message parts box, type 2.
7. In the upper-right corner of the Edit join node pane, click Done.
To add the array created in the previous step as the payload of the returned message, perform these steps:
1. On the left pane, expand function, select the function node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit function node pane appears.
3. Click the Function tab, and type the following code in the editor.
```
const features=msg.payload
const toArray=[]
toArray.push(features)
msg={
    payload:toArray
}
return msg;
```
4. In the upper-right corner of the Edit function node pane, click Done.
To use the model generated in the previous section, perform these steps:
1. On the left pane, expand machine learning, select the predictor node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit predictor node pane appears.
3. In the Name box, specify a name for the node.
4. In the Model path box, specify the location of the folder that contains the model.
5. In the Model name box, specify the name of the model to be used.
6. In the upper-right corner of the Edit predictor node pane, click Done.
To interpret the results of the prediction, use the function node again, and specify the following code in the Function tab:
```
status = ""
if (msg.payload ==0) {
status = "NG";
} else {
status = "OK";
}
msg= {
    payload:status
}
return msg;
```
Here, a message payload value of zero indicates that the sensor is not performing as expected and a payload value of one indicates that the sensor is in good operating condition.
To display the result in a user-friendly format, perform these steps:
1. On the left pane, expand function, select the template node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit template node pane appears.
3. From the Property drop-down list, select msg. and then type payload in the adjacent box..
4. From the Syntax Highlight drop-down list, select mustache.
5. In the Template editor, type the following:
```
Predicted class= {{payload}} ! 
```
6. From the Format drop-down list, select Mustache template.
7. From the Output as drop-down list, select Plain text.
8. In the upper-right corner of the Edit template node pane, click Done.
To display the status of prediction as a gauge on the dashboard, perform these steps:
1. On the left pane, expand dashboard, select the gauge node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit gauge node pane appears.
3. Click the Edit icon next to the Group drop-down list, and configure the properties as shown in step 9.3 of data gathering flow.
4. From the Type drop-down list, select Gauge.
5. In the Label box, specify the name of the chart.
6. In the Value format box, type {{value}} to specify the value shown on the gauge.
7. In the Units box, specify the unit of measurement. For example, ohm, volt.
8. In the Range box, specify the range as 0 to 1.
9. In the upper-right corner of the Edit gauge node pane, click Done.
To display the result of the prediction on the dashboard, perform these steps:
1. On the left pane, expand dashboard, select the text node, and drag it to the work area.
2. Double-click the created node.
  
  The Edit text node pane appears.
3. From the Group drop-down list, select [Sensor data] Sensor status.
4. In the Label box, specify the name of the chart.
5. In the Value format box, type {{msg.payload}} to display the prediction result on the chart.
6. In the Layout section, select a layout of your choice.
7. In the upper-right corner of the Edit text node pane, click Done.
To display the predicted class on the debug pane, perform these steps:
1. On the left pane, select the debug node, and drag it onto the work area.
2. Double-click the created node.
  
  The Edit debug node pane appears.
3. From the Output select msg, and then type payload in the adjacent box.
4. In the To section, select the debug window check box.
5. In the Name box, specify a name for the node.
6. In the upper-right corner of the Edit debug node pane, click Done.
Repeat step 11 to add a debug node for an error scenario.
Now that all the necessary nodes are available on the work area, connect the nodes to create the final flow. The final flow should look something like this.
On the menu bar, click Deploy to activate the flow.

The process values are read and the model predicts the downtime of the pH sensor.
In the upper-right corner of the Node-RED editor, click the Debug tab.

The debug pane appears, displaying the predicted class. A predicted class of 1 indicates that the sensor is in good operating condition and 0 indicates the downtime of the sensor.

Visualizing the prediction and process data on the Node-RED dashboard

The results of the prediction flow can be visualized on the Node-RED dashboard.

Follow these steps to view the prediction results:

Open a web browser.
In the address bar, type the URL of the Node-RED dashboard in the following format:

http://<ERT3PLUS_IP_ADDRESS>:<PORT_NUMBER>/ui/

Here,

<ERT3PLUS_IP_ADDRESS> refers to the IP address of the e-RT3 Plus device.

<PORT_NUMBER> refers to the port number to access Node-RED. The default port number is 1880.

The Node-RED dashboard page appears, displaying the results of the prediction and the trend of the process values. If the status is displayed as "OK", it indicates that the pH sensor is in good operating condition. On the contrary, a status "NG" indicates the downtime of the pH sensor.

Conclusion

The e-RT3 Plus device effectively uses the machine learning models to correctly predict the downtime of the pH sensor, thus proving to be a versatile controller that can be easily used to create ML-based solutions.

DEV Community

BYOML on e-RT3 Plus

Introduction

Workflow

Prerequisites

Install Node-RED machine learning and dashboard nodes

Getting Started

Configuring LC31 module

Writing the configuration code in Python

Transferring the code to the e-RT3 Plus device

Executing the code on e-RT3 Plus

Reading process data and writing it to InfluxDB

Exporting the InfluxDB data to a .csv file

Bring your own machine learning model

Using the generated model for prediction

Visualizing the prediction and process data on the Node-RED dashboard

Conclusion

References

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