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Data Engineering for Enabling Condition Monitoring of Industrial Equipment
Technology Category
- Networks & Connectivity - MQTT
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Aerospace
- Automotive
Use Cases
- Asset Health Management (AHM)
- Structural Health Monitoring
Services
- Data Science Services
- System Integration
The Challenge
The instrument engineering company faced a significant challenge in managing the data acquisition from various devices and sensors. Each device or sensor had its unique technology and served a different purpose, requiring a separate data acquisition platform. This led to a scattered product portfolio, disconnected sales process, and low customer perception. The company's end customers, large industrial enterprises, often needed to work with multiple devices or products, which meant dealing with different platforms. This situation resulted in a large effort and resources required to maintain these platforms, and a slow and lengthy time-to-market. The company also faced a lack of standard functionality across platforms.
The Customer
Not disclosed
About The Customer
The customer is a European instrument engineering company that manufactures data acquisition systems (DAQ). These systems enable critical testing, measurement, and monitoring applications for aerospace, industrial, and automotive industries. The DAQ systems acquire data in real-time for different physical quantities using sensor technologies, enable high measurement accuracy due to patented technology, and integrate with various enterprise software applications like LabView, Visual Studio .NET, and more. The company's end customers are large industrial enterprises that may need one or multiple devices or products for their applications.
The Solution
To address these challenges, the company partnered with Saviant to develop a robust condition monitoring platform with smart analytics and AI capabilities. The solution involved building an end-to-end data engineering capacity throughout their data acquisition, organization, analytics, and action procedures. The platform was designed to acquire data from a wide variety of sources in real-time and make it available to a variety of applications for further analysis. The team of 20 technology consultants and developers used .Net core, GraphQL, OPC UA, and MQTT for device discovery protocols. The platform captured data from various sources and platforms in real time, orchestrated it, and delivered it to a variety of applications via APIs. This resulted in a complete sensor to software platform, opening interfaces to customers across various levels, standardization across platforms, platform independence, scalability, and security.
Operational Impact
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