Saviant > Case Studies > Real-time Data Engineering for Industrial Systems Testing & Monitoring

Real-time Data Engineering for Industrial Systems Testing & Monitoring

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 Real-time Data Engineering for Industrial Systems Testing & Monitoring - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Equipment & Machinery
  • Renewable Energy
Applicable Functions
  • Maintenance
  • Quality Assurance
Use Cases
  • Machine Condition Monitoring
  • Real-Time Location System (RTLS)
Services
  • Data Science Services
  • Training
The Challenge
The UK-based instruments engineering company was facing challenges with their existing system of capturing, managing, and analyzing data from live machines and systems. The sensors installed on various components of industrial equipment like turbines and pumps captured physical data at a high frequency, which was then managed and analyzed locally on desktop-based systems. This resulted in manual, inaccurate, and time-consuming fault detection. The company decided to automate data collection, enable cloud-based data processing, and implement AI-based fault detection. However, they faced several challenges including the difficulty of scaling and deploying traditional predictive tools, the need for predictive analytics to be embedded within their application, the requirement of expertise for data preparation, cleansing, choosing the right algorithm, training, and validation, and the need for the platform and application to easily integrate with all hardware products.
The Customer
About The Customer
The customer is a UK-based instruments engineering company that empowers leaders in the industrial equipment and renewable energy industry. They provide smart sensors and hardware to help industrial enterprises reduce their equipment maintenance costs, improve machine yield, increase machine uptime, and ensure process quality. Their smart hardware solution captures vibration and other environmental data in real time through sensors installed on various components of industrial equipment like turbines and pumps. The data is then processed to monitor, analyze, and alert about machine condition, generating insights to help predict downtimes and maintain systems health and equipment efficiency.
The Solution
The company adopted a minimum viable product approach to quickly engineer and build a data platform capable of connecting millions of sensors globally and enabling industrial systems and equipment to be more intelligent about their failures, availability, and operating efficiency. They partnered with Saviant to develop the intelligent data platform. The team of technology consultants included Data Science/Machine Learning consultants, a Technology Architect, and IoT consultants. The platform designed enabled high-performance data engineering and automated capturing data, orchestration, and analysis. Machine Learning models were used to replace the age-old “if-then rules” method of fault detection. The platform also provided accurate and timely alerts and notifications about the failure conditions and alarms.
Operational Impact
  • The solution enabled more efficient and automated fault diagnosis. Timely preventive actions could be taken to avoid downtimes and improve productivity. Real-time insights were easily made available to the end customers on a single platform. This helped the Instrument Engineering company to offer Intelligence-as-a-service, thereby enhancing their service offering and increasing customer satisfaction.

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