Predictive Maintenance Solution over Azure Infrastructure (IoT)


If we consider the Internet of Things (IoT) data, it is a difficult task to handle and manage from source to destination because of the inherent complexity of operations involved. It requires many resources and complex services to process it and find the insight inside the data. Such operations need to be performed using various tools and services from different vendors. It would be a much more efficient way, if we can directly get the IoT data, process it & analyze it over a common Infrastructure. Also, one of the problems is how to apply big data processing framework, i.e., Apache Spark and other predictive analytics over a common infrastructure.


Microsoft Azure provides the complete cloud-native Infrastructure to manage the IoT data seamlessly. IoT applications can be described as things (devices) sending data that generate insights. These insights generate actions to improve a business or process. This architecture consists of the following components to form a complete Azure IaaS solution for IoT.

Fig: Azure Architecture

IoT devices can securely register with the cloud and can connect to the cloud to send and receive data. A cloud gateway provides a cloud hub for devices to connect securely to the cloud and send data. It also provides device management, capabilities, including command and control of devices.

Here IoT stream data is the big data in order to analyze this data first we need to Transform, Process & Clean the data. Azure provides Databricks and HDInsight services for big data management and spark processing. We can use either one of them. After Transform the data is ready for further analysis.

Azure Machine Learning, a cloud-based environment you can use to train, deploy, automate, manage, and track ML models. Here we can apply different machine learning algorithms and predictive analysis on the data. Azure ML service provides Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle, It has a separate storage account where we can store train models. After Training we can deploy the model on the endpoints called web service. Where we can feed the data to web-service either from manually or using Rest API.

Finally, Power BI is the best place to visualize the output, which is the business analytics service by Microsoft. Using Power BI we can analyze the output in a better way and compare the business needs with an existing solution. This architecture mostly useful in the areas like smart windmill data analysis, CCTV surveillance system (theft prediction), Progressive cavity pumps & CNC machine failure prediction, etc.

Thanks! You've already liked this
No comments