Saviant > Case Studies > Real-time Data Engineering for Testing and Monitoring

Real-time Data Engineering for Testing and Monitoring

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 Real-time Data Engineering for Testing and Monitoring - IoT ONE Case Study
Technology Category
  • Application Infrastructure & Middleware - Data Visualization
  • Sensors - Environmental Sensors
  • Sensors - Vibration Sensors
Applicable Industries
  • Renewable Energy
Applicable Functions
  • Facility Management
Use Cases
  • Machine Condition Monitoring
  • System Integration
The Challenge

Sensors capture physical data from equipment at a high frequency which is then managed and analyzed locally on desktop-based systems. Their end users then process the data locally on their desktops and manually create visibility into equipment conditions & their failure modes.

They decided to automate data collection and enable cloud-based data processing as well as AI-based fault detection while making it more data-driven instead of age-old rules-based. There were a few challenges to this:

  1. Traditional predictive tools are hard to scale and deploy
  2. Need predictive analytics to be embedded within their application
  3. Data preparation, cleansing, choosing the right algorithm, training it, and validating needs expertise with modern data platforms
  4. The platform and application need to easily integrate with all hardware products
The Customer
About The Customer

The instruments engineering company empowers leaders in the industrial equipment & renewable energy industry. They provide smart sensors & hardware, to help industrial Enterprises reduce their equipment maintenance costs, improve machine yield, increase machine uptime and ensure process quality. 

The Solution

A minimum viable product approach was adopted to quickly engineer and build a data platform that holds the power to connect millions of sensors globally and enable industrial systems and equipment to be more intelligent about their failures, availability & operating efficiency.

Saviant partnered with the instrument engineering company in developing the desired intelligent data platform for their next decade's vision. With a team of technology consultants that included Data Science / Machine Learning consultants, Technology Architect, IoT consultants, a platform was designed which enables

  1. High-performance data engineering and automates capturing data, orchestration, and analysis
  2. Machine Learning models to replace the age-old “if-then rules” method of fault detection
  3. Accurate & timely alerts & notifications about the failure conditions & alarms
Operational Impact
  • [Efficiency Improvement - Asset Monitoring]

    The solution enabled more efficient and automated fault diagnosis.Timely preventive actions could be taken to avoid downtimes and improve productivity.

  • [Data Management - Big Data Analysis]

    Real-time insights were easily made available to the end customers on a single platform that helped the Instrument Engineering company to offer Intelligence-as-a-service.

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