This shows our continuous commitment with industrial customers to help them adopting IoT solutions like this to maintain their key assets and achieve high operating efficiency with improved bottom line.

At KAISPE, we pride ourselves on our innovation, expertise, and our continued commitment to ensuring delivery of the highest quality products to our customers. As a Microsoft Certified Partner, we provide solutions backed by industry best practices and standards. We are Microsoft Co-Sell partner and have strong presence on Microsoft marketplaces with our remarkable apps and services.

To learn more, visit https://testing.kaispe.com or call (315) 791-4472. Connect with us on Twitter, LinkedIn, and Facebook.

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Azure IoT Central – Devices

This is how the summary Predictive Maintenance results are shown in KAISPE portal:

KAISPE Portal – Predictive Maintenance

Users can further view the details of Predictive Maintenance experiment outcome:

KAISPE Portal – Predictive Maintenance Details

Here is the high level architecture of how Azure IoT Central is connected with KAISPE Predictive Maintenance solution:

If you would like to know more about this integration, please feel free to contact us info@kaispe.com

The post Azure IoT Central is now Connected with KAISPE Predictive Maintenance Solution appeared first on KAISPE.

We will use Anomaly Detection algorithm for our solution that comes with Azure Machine Learning and is useful for detecting different types of anomalous patterns in time series data.

So, in the first step we will upload the dataset on Azure Machine Learning Studio in the CSV file format. In order to do that, open Azure Machine Learning home page and Click +NEW at the bottom of the window -> Select DATASET -> Select FROM LOCAL FILE.

In the Upload a new dataset dialog, click Browse, and find the Agricultural_Data updated.csv file you created.

Now in the next step we will create an experiment in Machine Learning Studio that uses the dataset you uploaded. So, click +NEW at the bottom of the window and Select EXPERIMENT, and then select “Blank Experiment”.

Select the default experiment name at the top and rename it to IoT Measurements and in the module palette to the left of the experiment page, expand Saved Datasets. Find the dataset you created under My Datasets and drag it onto the main page.

Now let’s prepare the data by using Apply SQL Transformation which was used to separate out the timestamps by date and time using SQLite.

After Apply SQL Transformation, we will search and drag Select Columns in Dataset, and in the Properties pane to the right page, click Launch column selector and select the following columns:

Next, In the module palette, Search  and drag Edit Metadata onto the main page and connect the Select Columns in dataset to the Edit Metadata. Select Edit Metadata, and in the Properties pane to the right page, click Launch column selector and select the following column:

Now, back in the Properties pane, we will look for the New column names parameter. In this field, enter processDate in new column names and select DateTime as our data type.

So, now in the next step we will apply separate Time Series Anomaly Detection for Temperature, Humidity, Soil Moisture and pH Level, which we mean to identify the increase or decrease of each of these variables and to evaluate against various parameters of the anomaly detection module.

After we applied separate Time Series Anomaly Detection for Temperature, Humidity, Soil Moisture and pH Level, you can get the result using R code which you can find and drag the Execute R Script module onto the experiment page and connect the output port of the time series anomaly detection to the first input port of the Execute R Script module and at the same time connect the output port of the Apply SQL Transformation to the second input port of the Execute R Script module.

Note: The purpose of creating the RScripts is for visualization of the influence and behavior of the variables.

Now we can see the visualization and the behavior of all variables:

For Temperature:

For Humidity:

For Soil Moisture:

For pH Level:

I hope you found this blog post helpful. If you have any questions, please feel free to contact me muhammad.ahmad@kaispe.com

The post Anomaly Detection for IoT Measurements using Azure Machine Learning appeared first on KAISPE.

So today I am going to reverse geocode using address points. In my case I am using Bing Maps API to get readable address information.

First Sign up Bing Maps developer account and when you have your Bing Maps developer account, you need to create a new key. This key will allow you to use the Bing Maps API, select My account and select My keys:

Next fill out the form and you can leave the Application URL section empty and click the Create button.

When you have created the key, copy the key to your clipboard so you can use it in your Power BI query when you access the Bing Maps

So, for this particular problem, we have a list of address points for the devices.

Now, we need to convert the address points (latitude and longitude) into readable address. To do this, we will need to create a new query in Power BI that we will use as a function to reverse geocode.

For this, I’m using the following URL:

http://dev.virtualearth.net/REST/v1/Locations/47.64054,-122.12934?o=xml&key=BingMapsKey

Note: I have replaced the API key which I have created in the last step.

Now we need to create a function using Bing map API key. To do this we will be following these steps:

First, select Get Data in the Home ribbon and Web as source

Find address column and click the Table link and drilling through.

Now we need to use this query to create a dynamic function that we can use to convert the address point information. Click the Advanced Editor button in the Home ribbon. You should see the M query

Now we’re ready to use our new function in a custom column in our query that has the address points.

Now, go to the Add Column ribbon and click Add Custom Column.

Note:  convert Latitude and Longitude values to text if you run into a data type error.

Now, expand the new column and select the fields and click OK.

You should see results here.

You can also seen visuals on Power BI.

I hope you found this blog post helpful. If you have any questions, please feel free to contact me muhammad.ahmad@kaispe.com.

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