Saviant > Case Studies > Data Engineering for enabling Condition Monitoring

Data Engineering for enabling Condition Monitoring

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 Data Engineering for enabling Condition Monitoring - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Computer Vision Software
  • Sensors
Applicable Industries
  • Automotive
Applicable Functions
  • Business Operation
  • Hardware Design & Engineering Services
  • Software Design & Engineering Services
The Challenge

The client wanted to deliver a complete sensor-to-software system, that is easy to use with a simple plug-and-measure concept.

Each device/sensor has its unique technology, serves a different purpose, and therefore has a separate data acquisition platform that works best for that product. However, their end customers, who are large industrial Enterprises, may need one or multiple devices/products for their applications and therefore need to work with different platforms. There were different data acquisition platforms in use which meant:

  1. The client has dozens of hardware & software systems for providing services to their enterprise clients
  2. Large effort and resources are required to maintain these platforms
  3. Time-to-market is also slow & lengthy
  4. The scattered product portfolio leads to a disconnected sales process & low customer perception
  5. Lack of standard Functionality across platforms
The Customer
About The Customer

The instrument engineering company manufactures data acquisition systems (DAQ) which enable critical testing, measurement, and monitoring applications for aerospace, industrial, and automotive industries. 

The Solution

Platform implementation as an outcome

Saviant partnered with the Instrument Engineering company in developing the complete robust condition monitoring platform with smart analytics & AI capabilities. With a team of 20 technology consultants, and developers working on .Net core, GraphQL - New API technology, OPC UA to represent Device objects in client-server mechanism, and to communicate with devices, with device discovery protocols like MQTT.

This common platform captures data from various sources & platforms in real-time, orchestrates it, and delivers it to a variety of applications via APIs. This achieved:

  1. A complete sensor to the software platform
  2. Opening interfaces to customers across various levels – device, measurement file, or application
  3. Standardization across platforms
  4. Platform independence – Windows, Linux, MacOS etc
  5. Scalability
  6. Security
Operational Impact
  • [Efficiency Improvement - IT]

    This modern platform therefore enabled a truly plug and measure capability for the customer across different products and their platforms.

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