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.

The post KAISPE Air Compressor Predictive Maintenance (IoT Central) is now Published on Microsoft Azure Marketplace appeared first on KAISPE.

To demonstrate the predictive maintenance solution, we will walk through the Predictive Maintenance of Air Compressors using Azure Machine Learning service step by step. We will train the machine learning model on remote computing resources, and the Azure Machine Learning workflow in the Python Jupyter notebook, as a template to train our own machine learning model with our own data.

Let’s set up development environment and create a workspace

1- Set up a development environment:

2- Create a workspace

3-Create Experiment

4- Create or attach an existing compute target

Note: You can also create the compute resources in the Azure Portal

In the next step we will verify the dataset and upload it into the cloud, so that the cloud training environment can access it. We save the model training data to a csv file.

So, let’s convert our label column values from [‘NORMAL’, ‘ABNORMAL’, ‘BROKEN’] to [0,1,2]. This is an essential step as the scikit-learn’s Random Forest can’t predict text.

Next, we will be training our model on a remote cluster where we have to submit the job to the remote training cluster you set up earlier. To submit a job you will perform these tasks:

• Create a directory
• Create a training script
• Create an estimator object
• Submit the job

So let’s create the directory to deliver the necessary code from your computer to the remote resource and created a training script

Now, we create an estimator and submit the job to the cluster

In last we will register a model in the workspace so that you or other employees can later query, examine, and deploy it.

Finally we can see the run experiment and register model on Azure Portal.

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

The post Predictive Maintenance of Air Compressors using Azure ML Services appeared first on KAISPE.

We provide Predictive Maintenance through Microsoft Azure Machine Learning in just a few easy steps.

• Trained Models according to the need of the different types of data using Azure Machine Learning.
• Real-Time Data from Machines are going to the Azure IoT Hub.
• Filter the Real-Time Data according to the user requirement using Stream Analytics.
• We Consumed Azure Machine Learning Trained Model according to the user environment.
• Finally, we have a comparison analysis between Real-Time Data and Our Trained Model Data and gives you the future operational Performance using KAISPE IoT Web Portal.

Now, we are going to demonstrate how KAISPE helps Air Compressor Industry to achieve Predictive Maintenance Feature.

We trained our model using Different Air Compressors Datasets.

Now we consumed Trained Model Web Service API for the Real-Time Prediction.

Now there are different types of Air Compressors that send different types of data to the Azure IoT Hub.

We have a target feature which is Vibration Analysis so we will extract the label features that’s are:

Pressure, Angular Speed and Piston Speed in our Azure Machine Learning Model.

We filtered Real-Time data according to the above features using Azure Stream Analytics.

We have a comparison analysis between Real-Time Data and our Trained Model API to make the Prediction for the Air Compressor Vibration Analysis and stored the relevant device information and Prediction Values in the Azure Storage and shows in the KAISPE IoT Web Portal and gives advance notice of the possibility of the failure in Real-Time.

Azure Table Storage that stores Comparison Analyses between Real-Time and Trained Model.

KAISPE IoT Web Portal that helps you to Visualize Real-Time Prediction for the Possibility of the Future Failure.

I hope you liked this blog post. Keep in touch with us at info@kaispe.com

The post Predictive Maintenance Feature in KAISPE IoT Web Portal appeared first on KAISPE.