decisionLabsmart models by creative thinkers

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dLOutline

1. Introduction to decisionLab

2. ATOM Overview

3. ATOM Use Case

4. ATOM Future Development

5. Artificial Intelligence Approach to Predictive Maintenance

6. Summary

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dLSome of the clients we help

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dLHow we do it?

SectorsAerospace

DefenceWaterPower

Transport

Services

Develop bespoke decision support

systems to optimisebusiness operations.

Agile Approach

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dLAgent-based

Turbine

Operations &

Maintenance

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Siemens Business Requirement• The introduction of the Siemens SGT-A65 aero-derivative gas turbine into Siemens Power Generation

product line has highlighted issues with the in-service performance and the supporting Repair & Overhaul.

• Existing excel-based methods for forecasting engine removals and the demand on R&O capacity and spare modules are inadequate given the complexity of the problem.

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• How do you predict business performance and forecast KPIs to inform decision-making?

• How to evaluate investments options – investment in improving engine performance, Repair & Overhaul capacity, Increase in spares inventory?

Exam Question:

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Digital Representation: “The digital representation that provides both the elements and the dynamics of how a system operates and lives throughout its life cycle”.

Digital Integration: “Integrates artificial intelligence, machine learning and data analytics to create a digital simulation models that update and change as their physical counterparts change. A digital twin is able to continuously learns and updates itself from multiple sources…..”

What is a Digital Twin?

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dLDigital Twin• Provides the capability to use sophisticated simulation and data-analytics methodologies to model

fleet operations of Siemens gas turbines.

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dLKey Benefits• The ability to model the entire fleet operations of Siemens aero-derivative gas turbines will enable:

• Forecasting of system KPIs• Visualization of fleet and maintenance facility operations• Identification of bottlenecks in the system and its impact of maintenance cost.• Run ‘what-if’ scenarios to aid investment decision-making.

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DIGITAL TWIN ADOPTION:A DEEP DIVE INTO THEATOM MODEL

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Model Complexity

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dLMODEL ARCHITECTURE

• Decoupling model functionality

• Simultaneous development

• Version control (Git)• Cloud deployment

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dLATOM Future Development

Phase 1 – Standalone Application Phase 2 – Enterprise Solution Phase 3 – Introduction of AI to Simulation Modelling

Current capability Future Capability

Applying AI to optimization

Moving agents from rule-based decision making to agents that agents learn their own knowledge directly from historic data and by trial and error – Reinforcement Learning

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• Not a Closed-Form Solution:• Agent-based modelling offers a simulation framework that is flexibility to solve a variety of business

process across varied levels of complexity.

• An enabler to the digital twin across various industry sectors

Extrapolation to Other Business Units

• Babcock International have taken on management of the tank power-pack repair system for the MOD

• £100s Million per annum

• Use Cases:

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dLAI Approach to Predictive Maintenance

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AI Capability

User Interface

Programme Nelson

Type 45 Destroyer: Use Case

Deployment

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dLSummary

• ATOM captures complexities of the lifecycle operations of industrial gas turbines – Digital representation of the assets operations.

• ATOM enables prediction of business performance and forecast KPIs to inform decision-making.

• ATOM is an enabler to the digital twin and is the first step towards the digital twin status.

• ATOM embodies agent-based modelling that provides a simulation framework that can be extrapolated to other business units.

decisionLabsmart models by creative thinkers

Thank you!