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

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

Real-time Data Engineering for Industrial Systems Testing & Monitoring

Saviant

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
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. Read More
About 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. Read More
Customer Name	Not disclosed Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Not disclosed

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.

Impact #1	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.

Impact #1

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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Overview

Real-time Data Engineering for Industrial Systems Testing & Monitoring

Operational Impact