Welcome to the world of Infosys Engineering! It is a half a billion plus organization that takes pride in shaping our engineering aspirations and dreams and bringing them to fruition. We provide engineering services and solutions across the lifecycle of our clients’ offerings, ranging from product ideation to realization and sustenance, that caters to a cross-section of industries - aerospace, automotive, medical devices, retail, telecommunications, hi tech, financial services, energy and utilities just to name a few major ones.

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September 7, 2018

Address condition monitoring and predictive maintenance challenges in the Aerospace Industry with Krti 4.0

Let's begin this blog post with a story. A man wanted to learn how to swim. He began by speaking to a few people about it and reading a few books on swimming techniques before heading to the lake. But, there began his problem: While he practiced the motions and went through every movement and stroke he had read about and discussed, he was still unable to keep his body afloat. Frustrated, he looked around and saw a person enjoying a leisurely swim in the middle of the lake. He waited for the swimmer to come ashore and asked if he could teach him how to swim. Soon, with a few lessons, the man was swimming by himself in the lake.

 

The message of the story is: It is easier to learn from a practitioner than a theoretician. This approach applies to almost every scenario that requires individual knowledge coupled with the guidance of someone who has traversed the path to successfully accomplish something. Now, swimming may be a simple skill to learn and, in the story, the investments needed were time and money spent on a few interactions and purchasing the right books. But what if an enterprise wanted to enhance its operational efficiency at an organization level? Here, the investments would be much higher and the ROI would need to be well-defined.

 

Take the example of aerospace and defense, a technology and investment-intensive industry burdened by strict safety and regulatory requirements. This industry needs stringent quality and production measures for consistency, reliability and high performance. The success drivers for organizations in this industry are high product quality and operational efficiency. From a theoretical point of view, the following levers can help organizations achieve manufacturing and operational efficiencies:

 

  • Intelligent management of critical shop-floor assets such as time-sensitive raw-materials, kits, molds, tools, and the workforce
  • Sophisticated algorithms powered by AI and data analytics that suggest next steps by sending out context-aware alerts and recommendations

 

Much like the earlier story about swimming, these levers look great on paper. But, without alignment with a practitioner's perspective, these may be insufficient to achieve the desired goals. So, how does one find the practitioner's view? This is possible by accessing processes and frameworks developed by organizations that have already deployed reliable systems with assured performance along with data prediction and connectivity between assets to collect data. In fact, I think that could actually mean the difference between the success and failure of any transformation program.

 

Condition monitoring and predictive maintenance - A practitioner's view

 

KRTI 4.0 is an AI-based operational efficiency framework developed by Infosys and Pöyry by leveraging decades of industry experience working with global organizations. This framework enables aerospace OEMs and Tier 1 suppliers to deploy a system that significantly enhances the reliability of operations and maintenance on the manufacturing shop floor. The KRTI 4.0 framework is unique because it leverages the different expertise areas and key operational experience of Infosys and Pöyry to provide a practitioner's view:

 

  • Pöyry specializes in reliability, availability, maintainability, and safety (RAMS) modeling and engineering design for different industrial processes. Its dynamic RAMS models are based on effective use of Industry 4.0 technology and backed by intense field knowledge of asset behavior
  • Infosys leverages its rich engineering experience gained by working with different aerospace OEMs as well as its knowledge of manufacturing practices to create knowledge, operational and maintenance models coupled with data analytics and AI-based resolutions. These models form the backbone of the KRTI 4.0 framework, providing condition-based and predictive maintenance at the shop floor level rather than at an individual system level.

 

As a framework, KRTI 4.0 differentiates itself from other condition monitoring and predictive maintenance solutions by offering a comprehensive shop floor-centric view based on reliability rather focusing on asset availability. This unique capability enables OEMs to schedule maintenance, resources and inventory based on reliability assessments. Moreover, decision makers gain a 360-degree view of risks associated with anomalies in terms of business, human and environmental safety.

 

Connect with us to get real-world guidance on how to improve the operational efficiency of your organization. 

September 6, 2018

Krti 4.0: Reasserting the Relevance of Knowledge Modeling

Seamless knowledge management is a problem that many organizations grapple with. Consider how, in traditional engineering, knowledge about industrial systems and processes often reside within software code and documents, and with domain specialists. Many times, such knowledge is lost when domain specialists retire or are redeployed or when software applications are upgraded or replaced. Software migration itself poses significant technical challenges as it is often difficult to understand the logic of knowledge embedded in software applications. Further, documents describing business logic are sometimes not updated, resulting in incomplete capture and processing of business knowledge.

I strongly feel that problems related to the loss of critical business knowledge can be easily alleviated by capturing, representing and processing such knowledge in a manner where the lifecycle of the product and process is properly managed. This is where intelligent knowledge modeling comes in.


Breaking down knowledge modeling

Simply put, an ontology is a set of object types, objects and relationships as well as the attributes of objects and relationships. Knowledge models based on ontology structures can theoretically capture, represent and process infinite amounts of data associated with a system by using objects, relationships and attributes. An ontology representation can also deterministically connect the ontologies with external systems such as databases and downstream systems in order to take relevant actions.


Here is an illustration of a knowledge meta-model:

 

blog_metamodel_1.png

Here is another illustration of a knowledge model that is an instantiation of the above meta model:

blog_model_1.png

When knowledge is managed using such models, it greatly enhances product development by accelerating the design and development of new and similar products from existing designs.

Knowledge models can also be used to improve the problem-cause-resolution process in maintenance and operations. The illustration below demonstrates how this can be done:

 


fig22_ccn_blog_sep2018.png

Solve business problems with comprehensive knowledge modeling

Infosys and Pöyry have collaborated to create KRTI 4.0, an industrial solution to help companies capture and represent embedded knowledge around problems, symptoms, root causes, and resolutions. The KRTI 4.0 framework uses knowledge models to provide a host of tools such as dashboards, graphs and alerts for clear problem resolution lifecycles. These help users such as plant managers, supervisors and technicians to understand and interpret both static and real-time aspects of the system. These knowledge models are particularly useful for enterprises in the power, oil and gas, paper, and automotive industries that must capture knowledge across multiple plants and data across multiple systems to gain insights about problem identification and resolution.

Learn more about how KRTI 4.0 can help your organization capture knowledge to solve business problems faster.

 


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