Case study
“I am blown away but the quality of the team and the offerings from Azure! Our backend is being put together at an amazing pace and we are looking good for meeting our deadlines moving forward.” -- Rob Sutherland | Director of Application Development, Athena Industrial Services
According to <The mobile economy 2020> report released by GSMA, IoT connections will reach 24.6 billion globally by 2025, up from 12 billion in 2019, with a CAGR of up to 13%; while in China, according to the <IoT Whitepaper (2020)> by CAICT, IoT connections reached 3.63 billion in 2019, with an expectation of reaching 8.01 billion by 2025, a CAGR of 14.1%. Many consulting firms predict that one of the fastest expanding subsets of Industrial Internet of Things (IIoT) connectivity will be the smart industry.
Among a number of IIoT applications, edge computing, automated quality assurance, predictive maintenance, and analytics are examples of IoT analytics technologies that can assist organizations in successfully leveraging complex IoT data sets to help lower maintenance costs and equipment failures, enhance customer experiences, and optimize products to meet customer needs.
Organizations can save up to millions of dollars in high maintenance costs thanks to AI's predictive maintenance. But without high-quality data from the devices being tested, enterprises cannot make industrial machine learning algorithms function. Through a network of equipment, IIoT sensors can gather data, which can then be used to identify which machines require planned maintenance and when.
About Athena
Athena Industrial Services ("Athena") is a Canadian-based research and development company that develops and manufactures innovative non-destructive testing equipment, providing high-quality technical systems to asset owners and inspection companies. Electromagnetic Field Imaging (EMFI) technology gives customers critical data and reporting capabilities to meet the growing demand for higher levels of direct assessment and fault characterization.
Project Pain Points
In November 2021, Athena came to Microsoft AI Co-Innovation Lab (Redmond, USA). At that time, Athena used nodes and gateways for railroad track detection to help railroad customers access crucial railroad data. Using data telemetry from these nodes and gateways, Athena then gave its customers insights into railroad operations and the need for predictive maintenance.
However, managers are often inundated with a wealth of information, making it challenging to view everything. But some information could significantly affect crucial operations. In order to connect its BRD gateway to the IoT Hub, communicate notes of telemetry data, and produce insights to solve problems for its clients, Athena sought to employ Azure services to connect gateway devices, capture and store telemetry data, and use Azure Machine Learning for advanced analytics.
Empowered by Microsoft Labs technology
To meet Athena's requirements, the main obstacle was to design a modern, continuous, and comprehensive rail data gathering approach that permits remote rail track inspections.
Following a thorough examination of Athena's requirements, the Lab designed a special solution in light of its circumstances, and assisted Athena's engineering staff in bringing the solution to fruition:
Technologies deployed
1. Azure IoT Hub
Establishing two-way communication with billions of IoT devices
Enabling enhanced security through per-device authentication
Delivering devices at scale through IoT Hub device provisioning services
Enabling device management at scale
Azure IoT Hub is a Platform-as-a-Service (PaaS) managed service hosted in the cloud that serves as a central messaging hub for bi-directional communication between an IoT application and the devices it manages. Azure IoT Hub to build IoT solutions allows reliable and secure communication between millions of IoT devices and the cloud-hosted backend of your solution.
Simple architecture of Azure IoT Hub
2. Azure Machine Learning
Building and train models quickly
Delivering responsible solutions
Large-scale implementation
Innovating on a more secure hybrid platform
Azure Machine Learning provide developers and data scientists with extensive experience to build, train, and deploy machine learning models faster. With our industry-leading MLOps (DevOps for Machine Learning), you can accelerate product launches and facilitate team collaboration. Designed for responsible machine learning, the platform enables innovation on a secure, trusted platform. Enterprise-class machine learning services for faster model building and deployment.
Project Results
After a month of collaborative work, the Lab successfully helped Athena achieve their expected results:
As the Industry 4.0 is trending, factories and enterprises are facing huge business challenges, in terms of both the need to automate production processes and the high maintenance costs of equipment, which is driving the transformation of traditional factories to smart factories. Reduced costs, increased revenue, and more effective data use are the hallmarks of a successful transformation. The key to success is using the right technology and the right platform.
Each company is different and special, and technology fit is particularly important. Microsoft AI Co-Innovation Lab has a world-class team of engineers to provide customized services to speed up your AIoT projects.
