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Agile Mumbai 2022 - Shwetank Sharad | Maintenance 4.0: Leveraging AI for Optimization of Maintenance Function

  1. Maintenance 4.0: Leveraging AI for Optimization of Maintenance Function Digital Intelligence for Optimised Maintenance Commercial in Confidence Imagine AI Innovation
  2. Contents Industry 4.0 – Evolution & Global Adoption Maintenance 4.0 Industry Case Studies Augmenting Maintenance Technicians 01 03 05 02 04 4.0 Industry 4.0 – India Adoption 2
  3. 6 Industry 4.0 Evolution & GlobalAdoption 01 3
  4. Evolution of Technology in Manufacturing… Role Impact "Mass Production for Global Markets” “Humans usingmachines for mass production” “Cyber Physical System– Connected Machines” “Continuous learning / customized massproduction” “Assembly Line– Focusing on One Task/Person” “Move towardsjob specialization” Efficient Manufacturing – Reduce ManpowerDependency Faster Product toMarket profitable manufacturing 18th Century Industry 1.0 Mechanical production equipment powered by steam and water Industry 2.0 Mass prodcution assembly lines requiring labor and electrical energy Industry 3.0 Automated production using electronics andIT Industry 4.0 Intelligent production incorporated with IoT, cloud technology and big data 19th Century 20th Century Today Industry 4.0 originatedin2011fromaproject inthehigh-techstrategyoftheGermangovt….Transition from 5% of manufacturing IT spend to 20% by 2021, a 9.6X rise, driven by smart solutions and business sustainability needs during 2011-21… 4
  5. Key Emerging Technologies Enabling Industry 4.0 Deployment… US, China, India, Brazil, UK are planning $100+ Bn new investments in IoT, AI/ML, IT- OT integration, Robotics &Digital Twin MATURED TECHNOLOGIES $50 - $60 Bn @10% CAGR MATURING – NEXT 5 YEARS $4 - $5 Bn @30% CAGR EXPANDING TECHNOLOGIES NASCENT TECHNOLOGIES $30 - $40 Bn @15% CAGR $3 - $4 Bn @25% CAGR • Cloud Computing • Industrial Robots • Internet ofThings (IOT) • AI inManufacturing • 3D Printing • 4D Printing • Quantum Computing • Cyber-Physical Systems • Advanced Human-Machine Interface • Exoskeleton/Man-Machine • Cybersecurity Technology • AR/VR inManufacturing • Big Data& Analytics • Wearables &Sensors • Digital Twin • 5G inManufacturing • Edge Computing • Blockchain inManufacturing Sources: IIoT World,International Federationof Robotics,The Manufacturing Institute 5
  6. Visible Supply Chains Location Agnostic Command andControl Intuitive products and flexible service models • Traceability of suppliers /material • Predictability of potential disruptions • Multisite integration with central control towers • CPS-equipped connected products that enhance usability experience • Smart Contracts – Digital contracts/SLAs • Smart Procurement – AI-based supplier risk management • Smart Machines – Legacy retrofitting; self- organizing and correcting machines; digital twins for remote monitoring • Smart Process Line – Process automation to self-optimzing process lines; intelligent robotics: cobots and HMI; data integration across MES, SCM, CRM and procurement • Smart Services – AR/VR- based remote servicing; predicitve condition monitoring andmaintenance • Smart Resourcing – Self-adjusting HMI and robotic integration; AR/VR based operator assist • CPS Equipped – Products equipped with embedded IoT sensors, self-learning and self- optimizing capabilities using AI at the Edge, connectivity tech for M2M communication • Smart Logistics – Movement tracing and ML- based real-time route and mode optimization • Smart Warehousing – Autonomous warehouses with robotics and HMI; AI-based inventory, returns, reverse logistics management • New Data-Driven Business Models – M2M data led predictive analytics aimed at innovative employee & customer experience Smart Industry: Industry 4.0 is Transforming Operations, Supply Chain, Customer Solutions… 6 Smart Sourcing Smart Sourcing Smart Supply Chain Smart Services Smart Operations Smart Factory Smart Solutions Smart Products
  7. • Digital representation of product or machine, helps in design, testing, simulations Digital Twin • Connecting factory objects like machines, vehicles, products for control & optimization Connected Factory • 3D Printing can support massive customizations and can increase flexibility Flexible production • Automation, Visualization using AR/VR improves man- machine coordination • Remote monitoring with Sensors and Big Data helps in optimizing maintenance Visualization & Process Automation Predictive Maintenance • Helps detect patterns in production or quality data, providing insights for optimization Big Data • Factory operating independently on self-learning algorithms, reduces operations cost Autonomous Digital Factory • Sensors to track Products, Raw materials provides full transparency on production process Track and Trace Key Technology Features of Digital / Smart Factory… 7
  8. Big Data Decision- Making (Bosch Automotive China) • Before: Operational data from the shop floor, such as machine cycle times or part failure modes required a significant amount of manual collection and pre-processing. Continuous shop-floor improvement activities were impacted. • After: The Wuxi site, set up an industrial IoT framework, connecting newly installed machine condition sensors and individual cutting tool information. They were able to visualize the data, develop customizable reports with powerful analyses, including diagnostic, predictive and prescriptive functions, leading to 10% output increase. Democratized Technology At Shop Floor • A large manufacturer had deployed Autonomous Mobile Robots (AMRs) for a point-to- point material transfer workflow moving parts from kitting stations to an assembly cell. • The AMR system employed Cloud Robotics Technology, so it provided a simple interface that enabled the floor manager to set up & schedule additional workflows between the kitting area & the new cell with a few clicks, without any support from the IT staff. • As a result the workers and local staff were able to increase their productivity. Examples Of Global Companies Using Digital / Smart Factory Use Cases… Minimal Increment al Cost to Add Use Cases (Microsoft Manufacturi ng, China) • To ensure competitiveness of IT products & services (PC & other devices), Microsoft transformed the manufacturing process at its factory in 3 waves: connection of equipment, prediction using big data, application of machine learning to create cognitive manufacturing lines. • Using connected equipment & the capability to add new use-cases in a short time period, company added machine learning algorithms for predictive yield improvement based on production process data of individual components, yield improvement of 30% with the completion of one use-case. 8
  9. 13 Industry 4.0 India Adoption 02 9
  10. Discrete Manufacturing – $4.8 Bn Process Manufacturing – $1.6 Bn Discrete75% Process Manufacturing, 25% Share of Industry 4.0 Spending, 2021 65% 40% 25% 30% 8% 25% 2% 5% • Indian Automakers stepped up investments in Cloud and digital systems, shedding legacy IT infrastructure • Electronic component manufacturers in India have invested heavily in Connected Technologies like 5G & IIoT • From retrofitting legacy machines on process lines with IoT devices, to entirely autonomous process lines monitored remotely via digital thread – the discrete segment is capitalizing on M2M data to manage end-to-end operations • Indian pharmaceutical companies are prioritizing Cloud-based modernization with preference for“pay-per-use” models • 50% of the sector spends greater than 6% of its annual revenue on technology spend and is in early or intermediate stages of Industry 4.0 adoption • Other process industries, like Chemicals, are at early stages of Industry 4.0 deployment Data andAnalytics Connectivity Tech Intelligent Automation Advanced DigiTech Most India Industry 4.0 investments are currently in Cloud, IoT, Big Data Analytics, Connectivity Tech & RPA… Source: NASSCOMReportFeb2022 10
  11. Industry 4.0 Use Cases by Value Chain Stages, Key Technologies Involved… Real-TimeSupplier Management Real Time Order Management – IIoT and MES/SCADA integration Predicitve Supplier Performance– BDA, AI/ML Supplier Scenario Planning, Vulnerability Assessment – AI/ ML, BDA Sourcing Mix Modeling/ Dynamic orFlexi- Sourcing Strategy – AI/ML, AR/VR, Blockchain Supplier Financing - Blockchain Predictive Planning Predictive Demand Planning – Edge Devices, IoT, Big Data,AI/ML Real Time Replanning and Scheduling – ML, BDA Outcome-Based Decision Modeling – Blockchain, BDA, AI Traceability – IIoT Platform (Cloud, Edge Devices, Sensors), Robotics, AR/VR, Digital Thread Planning Production Operations Upstream –Supplier Warehouse/Logistics Downstream – Customers/Partners Smart or Dark Factories: Smart Machines Predictive Maintenance – Big Data, Cloud, AI/ML, Edge Devices Remote Controlled Supervisory or Maintenance Operations – Connectivity Tech, Robotics, Automation, Digital Twins Smart Lines Self-Optimizing Assembly Lines –IIoT Platform, Automation, AI/ML, Edge Devices, and integrated OT Platforms Flexi-Assembly Lines – Digital Twins,Additive Manufacturing Smart Operators/ Services Remote Floor Shop Monitoring – Robotics, Automation, Digital Twins, AR/VR,Drones Integrated Logistics: Smart warehouse/Logistics Predictive Warehouse Management – IIoT, Robotics, Automation, Connectivity Tech,Edge Devices Real-Time/ Predictive Inventory Management– IoT, Edge Devices, AI/ML, Drones, AR/VR, Robotics Freight-Sourcing Decision and IntegratedMulti-Modal Logistics – IoT, AI/ML, Connectivity Tech,BDA Digital Customer Experience: Smart Partners Predicitve Distribution Planning – Integrated CRM and SCM with MES, BDA, AI/ML based optimization Customers/Partner Decision Analytics – BDA, IIoT, Edge Devices, Connectivity Tech Hyperlocal or Last Mile Services Micro-Fulfillment – Big Data, Edge Devices,AI/ML, IoT Real Time Location Data – IoT, Connectivity Tech Direct-to-Customer (D2C) Services Omnichannel strategy – Cloud, BDA, AI/ML Traceability – IoT, AR/VR, Digital Thread 11
  12. Industry 4.0 Case Study 1: Bajaj Auto… Shopfloor Efficiency Improvement – Lowest running costs, Can operate without a cage in space constrained areas. Reduction in Ergonomic Risks- Usage of Co-Bots, thus reducing manual stress, providing Compact movement, extremely flexible (all axes + or – 360-degree rotation) and lightweight. Safety - Eases work for women workforce, with 30 patented force limiting features built in compliance with ISO TS 15066, Ceiling mount, Wall mount or Floor mount Co-Bots. Zero annual maintenance costs - Reduced power consumption and retention of IP within the company, organically driving growth of the organization. SOLUTION PROBLEM STATEMENT IMPACT – Smart Lines and Smart Operators/Services Two-wheeler assembly lines were highly labour intensive, spatially challenged. Around 50% of the workforce were women, who found it difficult to operate intensive assembly lines. Bajaj auto wanted to: • Reduce ergonomic risks to the employees. • Find a standardized automation solution. Tech Solution Deployed – Partnered with Universal Robots after 3 months of extensive testing of Universal Robots’ cobots: • Ceiling Mounted Cobots – Diminished the challenge of space constraint . • Reduction in Redundancy-Led Fatigue and Errors – Completing the repetitive movements that required precision. • Standardization & New Decal Applications – Catered to multi- modelling adaptability and tasks that required flexibility,productivity and reliability. 12
  13. Industry 4.0 Case Study 2: TVS Motors… Traceability – IoT-based product traceability through the flow cycle to assess quality of the material in real- time, for upstream and downstream information and associated decisions. Skill Matrix - Maintain a digital trace of operator performance. Enable the identification of a skill matrix and identify any exceptions that could impact product quality. OEE Improvement – Real-time insight into parameters that impact line productivity, such as line rates, loss, and quality analysis across multiple levels of operations. Predictive Maintenance – Statistical analysis of product quality parameters, coupled with real-time machine condition data enabled predictive maintenance and minimized costly stalls. TVS Motor’s assembly line machines were not connected, and data from machines was not flowing into the data lake, impacting traceability, visibility and predictability at the shopfloor TVS wanted to Build an integrated manufacturing data lake, Integrate machine data on shop floor, Move data from other IT systems on the shopfloor into the data lake Tech Solution Deployed – Partnered with Altizon and deployed the provider’s proprietary IoT platform and Digital Factory hybrid solution with an Edge solution inside the TVS network. The solution stack included: • Edge Computing: Distributed computing platform that allows IIoT data to be processed closer to the edge of thenetwork. • Connected Work: Integrated data lake for storing and processing all machine and manufacturing data for further analytics. • Digital Factory: Unified digital manufacturing platform powered by IoT and out-of-the box apps for monitoring, measuring, analyzing and predicting outcomes using AI. SOLUTION IMPACT – Digital Customer Experience 13 PROBLEM STATEMENT
  14. Industry 4.0 Case Study 3: Kia Motors… Real-Time Transaction Visibility Via Digitalized Showroom – Live Stream Showroom capability demonstrated continued commitment to tailor the car-buying journey to the demands of the customers with virtual viewings. Transparency – Customers could digitally make buying decisions along with their family members logged in from multiple geographies at the same time, recreating a physical showroom experience. Customer Connectedness – Digital consultation services by established dealers gave customers a sense of reliability and security while making purchase decisions during a pandemic. SOLUTION IMPACT – Digital Customer Experience During the pandemic, sales and services practically ceased overnight, affecting customer connect and demand forecasting. Challenge was to keep the potential customers engaged so that once the industry picks up, they turn buyers. Biggest challenge that KIA faced as a new player is that they were not able to demonstrate their product due to the restrictions set during the coronavirus lockdowns 3D Configurators – Kia Motors deployed an AR/VR based 3D configurator solution to create a digital catalogue of the showcased vehicle and a digital specifications board for every vehicle category in their product portfolio at the Mumbai showroom. 3D Configurator Customer Zone – Enabled customers to customize and design their favorite Kia cars and witness their intricate details. The content displayed in the showroom was remotely controlled centrally. ‘Kia Digi-Connect’: Anindustry-first video-based live sales consultation solution website integrated with the company’s CRM system, provided customers options of 360- degree virtual experience through video calls and screen sharing, along with sharing of digital brochures and dynamic pricing. 6000+ pre-bookings made on Day 1 of opening from pandemic lockdown 14 PROBLEM STATEMENT
  15. Industry 4.0 Case Study 4: Nokia… Real-Time Visibility for Central Control - Screens display real- time information from the various sensors that monitor every process across the factory floor. The data from these sensors runs through Microsoft’s Azure platform, and the system allows managers to track parts by serial number as they move through the factory, physically or via a digital twin platform Automation of Quality Testing Processes – Maintains a digital trace of operator performance. The system allows the company to pinpoint exactly where something went wrong and fix the problem quickly. Low Latency and Real- Time Data Capture - Deploying a private wireless network helped in greater agility on the shop floor to accommodate the rising need for line configuration changes. Fully Remote- Controlled Operations - Digital twin of the factory enabled automation of the production flow and remote operation and maintenance. SOLUTION IMPACT – Across Value Chain 31% labor time reduction through robotic automation. 31,000-man hours saved through RPA. 16% OEE improvement Nokia’s factory in Chennai, yielding 16 billion chip mounts per year, faced severe external supply chain shocks due to competition from China. Needed to cut costs and drive efficiency in the supply chain. Pressure to be agile and responsive in a volatile market was high. Nokia battled a monolithic IT system as a result of merging legacies of Siemens, Alcatel- Lucent, Nortel, Motorola and Panasonic. Tech Solution Deployed – Nokia has built a private wireless network based on 4G LTE. • Autonomous Guided Vehicles/Autonomous Intelligent Vehicles: Material flows warehouses driven by intelligent, autonomous vehicles. To enable the seamless movement of the AGVs, AIVs and also to track the assets moving around the shop floor, High Accuracy Indoor Positioning (HAIP) system using sensors, IoT gateways and private LTE platform. • ‘’Pick to Light System” for Inventory Control– All parts stored in racks across the store, and when the part if requested at a production station or testing area, an operator enters the data into the asset management system and a light goes on at the specific rack in the warehouse to make it easy to locate the part in the specific storage rack, and further transport it to the required place on the shop floor. 15 PROBLEM STATEMENT
  16. Major Technology Investments by Global and Large Manufacturers… Ola Electric with Siemens - $300 Mn for building India’s most advanced electric vehicle manufacturing facility Bosch Home Appliances - €100 Mn spend by 2025 on IoT- based solutions + smart refrigerator factory in India Henkel Adhesives - €50 Mn into a smart factory in Pune, equipped with end-to-end quality and track-and-trace capabilities using digitalized workflows M&M and Bosch – Partnership to develop Mahindra’s connected vehicle platform “AdrenoX Connect”with integrated platforms enabling flexible swichovers 4 Vedanta and GE – Partnership to digitalize India’s first Aluminium smelting plant deploying Digital Twin technology built on GE’s Predix Platform 1 2 3 5 2 5 16
  17. Indian Industry 4.0 Provider Landscape: Illustrative, Not-Exhaustive.. • Connected Building Blocks • Hosting Industrial IoT Platforms Analytics • Microchips Sensors Connected Hardware • System Integrators Cybersecurity Aiding I.40 Technologies Collaborative Robots/ Robotics Universal Robots AR/VR Drones/ UAV’s Additive Manufacturing/Connected Machine Vision AR/VR Drones/ UAV’s Microchips Sensors Connected H/W System Integrators Cybersecurity Additive Manufacturing/Connected Machine Vision Connected Building Blocks Aiding I 4.0 Technologies Hosting Industrial IoT Platforms Analytics Collaborative Robots 17
  18. 39 Maintenance 4.0 03 18
  19. Maintenance 4.0: SMART MAINTENANCE … Preventive and Proactive Maintenance Condition Monitoring Leaner Maintenance Automation of Clerical Maintenance Tasks Maintenance 4.0 describes a specific stream of innovation within Industry 4.0 focusing on Maintenance. Cornerstone of Maintenance 4.0: 19
  20. Maintenance Strategies: A Continuum… * Original equipment effectiveness Poor maintenance strategies can reduce a plant’s overall productive capacity between 5 and 20 percent. Unplanned downtime costs industrial manufacturers an estimated $50 billion each year. Predictive Maintenance (PdM) is the most efficient maintenance strategy available – a Gold Standard. PdM can increase equipment uptime by 10–20 percent and reduce maintenance costs by 5–10 percent… 20
  21. Predictive Maintenance: The Physical-Digital-Physical Loop… Real-time access to data and intelligence is driven by continuous, cyclical flow of information between physical and digital world through iterative series of three steps, physical-to-digital-to-physical loop Source: Deloitte analysis. 21
  22. The Predictive Maintenance Process… Jim a factory floor supervisor in a manufacturing plant in charge of maintaining numerous machines. Source: Deloitte analysis. 22
  23. Understanding Technologies That Enable PdM Process Deployment… Data Integration + Augmented Intelligence + Edge Computing + Augmented Behavior Using Wearables and Mixed Reality 23
  24. (PredictionWithPrecision) Vibration3D Acoustic Emission MagneticFlux Humidity TrueRPM Temperature World's First 6-in-1 Sensor . MachineDoctor is easy to configure, It feeds directly into Analysis Software RotationLF Insights Diagnosis Action Anomaly Detection Fault Diagnosis Time toFault Prediction Action MachineDoctor™ RotationLF™ Analysis Software = AUTOMATED End 2 End SOLUTION FOR REMAINING USEFUL LIFE PREDICTION WITH 99% ACCURACY 24
  25. THE SOLUTION&RESULT BACKGROUND L&T Nabha Power Plant is 700 MW thermal power plant in Punjab. Unplanned shutdown maintenance impacts profitability. THE CHALLENGE The Condensate Cooling Water (CCW) pump is a horizontal vane pump operating at up to 1650 m3/hr. Each day this pump is offline, it costs the plant $250,000 in lost revenue. L&T needed a predictive maintenance solution to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL). Nanoprecise proposed rotation LF system, installed 24 wireless sensors as a part of a pilot project on air compressors, CCW Pumps, Fans. Sensors sending data to SaaS-based platform using Edge and Cloud computing. RotationLF platform analysed data using algorithms. Six weeks later AI alerted that a vane fault had been detected on the pump, causing cavitation. The fault frequency depicted in the below plot indicates an early stage failure. As cavitation damage to vanes and housing progressed, the amplitude increased and the RUL decreased. Anomaly in the pattern alerted plant staff about this unusual trend automatically through mobile text & email alerts. The maintenance team used a hand- held vibration meter to verify the fault detected by RotationLF and then partially disassembled the pump to visually confirm that the vanes were damaged. Atemporary repair was made to the damaged vanes before putting the pump back in service. The RUL prediction of 25 days to failure provided sufficient time to schedule the part replacement and prevented shut down. PdMCaseStudy1: 25 Days Of Remaining Useful Life (RUL) Prediction… 25
  26. Trenitalia was able to maximize the brake pads’ useful life while reducing the number of needed spares. Decrease downtime by 5–8 percent, reduce annual maintenance spend of $1.3 billion by an estimated 8–10 percent, saving $100 million per year. More trains have run on time, so more passengers are happier. SOLUTION PROBLEM STATEMENT IMPACT Italian train operator, Trenitalia, had to remove each one of its 1,600 trains from service not just for scheduled maintenance and when a train failed unexpectedly.This created delays, performance penalties, annoyed passengers. Trenitalia added hundreds of onboard sensors on 1,500 locomotives as part of a three-year maintenance improvement initiative. Data were transmitted to private cloud storage in near-real time, where diagnostic analytics provided advance warning of the failure of parts such as brake pads. PdM Case Study2: TransportationIndustry… The Impact Of PdM: Not Just Operational Efficiency Improvement, But Also Business Growth Through Better Product Quality Resulting In Differentiation & Higher Customer Satisfaction… 26
  27. 21 04 Augmenting Maintenance Technician 27
  28. The Challenge For Utility Operators… To optimise the availability / uptime of generating and distribution assets Equipment downtime results in: Revenue loss Brand damage 7 Additional costs Diminishes customer confidence 28
  29. Business Impact For The Energy Industry… At a medium-size power plant, 1% change in plant availability could have a $3.5 million revenue impact Fewer than 24% of operators describe their maintenance approach as being a predictive one According to Aberdeen, unplanned Utility downtime can cost companies as much $260,000 per hour Capital expenditures continue to rise, with an increase in 2018 of 14 percent, to reach an all- time high of $133.8 billion for the 50 electric and gas utilities S&P Global tracks annually. 72% of Organizations Target Zero Unplanned Downtime… 29
  30. Root Cause Analysis for High O & M Cost… • Inaccurate diagnosis & prognosis • Skills, knowledge, experience and support limitations • Insufficient service procedures • Correct information not available at point of failure • Premature component/subsystem failures Causes: • Prolonged System Downtime • Large no. of Unplanned Maintenance Events 30
  31. ERP Maintenance Management Document Management Handwritten Forms Electronic Forms Manual Reports Spares Management Tool Requests Senior technicians retire: Knowledge Retention Sensors SCADA Inspection Management Emails Multimedia (e.g. Photos) Phone calls, Memory recall The Challenge In Perspective… Critical knowledge is inaccessible due to Fragmented Systems Man-hours are wasted on Manual and Disconnected Processes Inefficient use of technicians’ time with Lack of Automation Young digital natives require rapid technician Upskilling and Guidance Expectations of rich digital experience 31
  32. Transforming Maintenance With AI & Machine Learning… AI works like a human brain, but with advanced analytic and processing power Artificial Intelligence (including Machine Learning and Neural Networks) enables a system to learn, self-improve and interpret as it performs a task, refining over time through strategic trial and error. Natural Language Processing fills the gap between human communication and computer understanding. Our unique utilisation of these technologies allows us to perform tasks such as skilled analysis, pattern recognition, image and speech recognition, analysis of massive amounts of data, and sophisticated decision making. Big Data allows systematic extraction of information from large and complex datasets. 32
  33. Requires a system that: Provides the right technical information at the right time and place to reduce risk of maintenance error Provides all technical information at one place, Instructs and guides the technician Advises if the right tools are available and their location Enables real-time collaboration between technicians Achieving Optimized Maintenance… Ensures optimisation of the use of spares through data driven insights Continuously learns and optimises the maintenance process 33
  34. Provides a natural language interface to ERP, Maintenance and Document Management Systems, so as to extend and enhance their capabilities Utilises any device (phones, tablets, etc.) to provide solutions at the right time and place Is a digital intelligent system that learns from: • Asset behaviour • Technician behaviour • Organisational Data Guides the technician at all stages of the maintenance process, providing advanced troubleshooting capabilities The LexX Platform: Solution for Optimized Maintenance… LexX is an intelligent digital colleague, empowering technicians by bringing knowledge, information experience to their fingertips 34
  35. How This Technology Works… LexX utilises AI and Machine Learning to learn over time from your organisational data, technician interaction, and equipment behaviour, so that troubleshooting is continually refined. LexX Ingests all technical information (digital and handwritten) and structures the data in a unique manner to facilitate the AI and NLP capabilities of LexX. This information is used to provide solutions and guide the technician through Natural Language interaction (in Conversational style, like having an instant messaging discussion with a human) 35
  36. Intelligent Maintenance Assistant LexX Core (Eg. User-friendly interface, troubleshooting, repairs, parts lookup ) LexX Platform Modules… Data Pipeline Automated ingestion Handwritten (OCR) Structured Storage Representational State Transfer Protocol Standardized Database Learning Systems Search Algorithms Intelligent Knowledge Base Specialised Business Functions Eg., Classification Reporting Data Analytics Eg. Alerts Algorithmic Operations Support API Layer How Is It Powered? Ops Analytics Eg., predictors and alerts Inputs Fault Logs Service Manuals Schematics Events & Alarms Tools, Spares Work Orders Synergy (Eg. Workgroups and notifications) Performance and Reliability Dashboards (Eg. Visualisation and reporting) Digital Tools (Eg. Estimation image recognition, drone images) Outputs Secure Cloud Hosted 36
  37. LexX Platform Features… What Do You Get? Intelligent Maintenance Assistant Configurable for Client need or specific maintenance use-cases (i.e. fault management, parts lookup) Synergy Suite Bring the team to context for help, advice, or approval Performance and Reliability Dashboards Configurable dashboards for performance management and reliability engineering user groups Digital Tools Tools that empower the technician, such as automation for repetitive tasks (i.e., estimation, image recognition, drone images) 37
  38. Ultimate Success Indicator: Reduces O & M Costs / Improves Machine Availability The Measurable Value LexX Brings to Operations & Maintenance… Time to Troubleshoot (MTTR) improves: Technicians get it right the first time as best practices get consolidated; maintenance procedures get improved. Spare Parts Utilization improves by minimizing unnecessary use of parts. Time on Tool improves: With less technician time spent on the phone, searching for, retrieving and classifying work orders, LexX improves and the capacity of the existing workforce to improve machine availability / Uptime. Empowers the modern-day technician and reduces labor/contractor costs. Time to Productivity improves: Less time is needed to train and job-shadow technicians to high competency levels, increasing the scalability and flexibility of the workforce. Knowledge Retention Improves. Enhanced forecasting accuracy. Improved data and configuration management ensures the integrity and currency of the assets, components, spares and resources related to maintenance. This allows the organization to optimize its inventory through reliable long term-planning and forecasting. 38
  39. The Way Technicians Work Today How Technicians Should Work Today Digital Transformation With LexX Enabled Maintenance How Technicians Will Work Tomorrow The Future of Maintenance with LexX 39
  40. LexX Deployment Case Study 1: Energy Australia Background The Energy Australia site was Mt Piper Power Station, which comprises of two 700MW coal-fired steam turbine generators and can meet the energy needs of 1.18 million homes in NSW every year. Pump Repairs could take days to weeks depending on need to isolate, transfer pump to workshop for repairs, and parts availability. In the meantime, operators are always applying pressure to have the equipment brought back in to service. In some cases, a spare could be brought in while the repairs are happening, but if there aren't any spares then the fitters work overtime. Objective of POV Prove that the Lexx platform can help: o Improve availability/up-time of generating power plants o Demonstrate ease of use, without being intrusive o Demonstrate availability of right information at right time and place 40
  41. Pump Repair Process Work order comes in •If there's an issue related e.g. pump leaking or not starting, technician will go in- field. •Based on experience, and some ad hoc preliminary investigation, cause may be known. •Some information might be on the work order, or might be known to the operators e.g. temperature; •If not found in the WO, technician needs to check in with operators. If the problem can't be fixed immediately (which it generally can't), •Coordinate with operators to have the equipment (including components like valves) 'isolated' for some period •Technician estimates how long he thinks the equipment needs to be isolated (generally based on intuition) •Depending on urgency the isolation could be scheduled for later in the day/week or immediately If the pump needs to be removed from its location (which it generally does) then it must be taken to the workshop. •Arrange for a crane and rigger to be hired, and permits granted. •If pump is in a pit, then a confined space permit needs to be obtained. •The administrative side of things can take quite a while. Once in the workshop, the equipment gets pulled down. •Risks here that dismantling it improperly could cause more problems. Some root causes don't require dismantling. •If it's discovered that something is broken, then replacement parts must be retrieved from the store, •If there's no spares in store they must be ordered. Also, critical to know exactly which part number it is.. Once these repairs are done, •A crane and rigger must again be hired, permits granted, •Window of time arranged with operators, to have the equipment re-installed •The pump is brought back in to service. The role of LexX here is to: • Help the technician follow the correct procedure for identifying the root causes • Utilising historical or OEM provided troubleshooting guides • Identify the correct parts • Identify the nature of issue by contextually providing to the technician information from the work order, history, logs, manuals and any tribal knowledge existing in the organisation Scenarios tested with LexX: 1. Reactive maintenance: pump not starting (due to electrical issue but thought to be mechanical) 2. Planned maintenance: check oil level and change oil as required 3. Main belt repair 1 2 3 Understand Isolate/Fix Shift to Workshop Workshop Restore to Ops 41
  42. Issue Description: This event has occurred in the past, troubleshooting for a sulfuric acid pump led to a no fault was found (NFF). Since the issue was not mechanical but electrical, engineer and the team weren't able to diagnose the problem through isolation and disassembly. The issue turned out to be due to an associated solenoid, for which repairs could have been made without removing or tearing-down the pump. Scenarios 1: Reactive maintenance: pump not starting (due to electrical issue but was thought to be mechanical) Without LexX With LexX • Turnaround time: 2 working days, 70% reduction in turnaround time • Fewer NFF events • Less time spent on admin, more time- on-tool • Early intervention • Turnaround time: 7 working days • Technician went too far into mechanical troubleshooting. 42
  43. Issue Description Work order comes in requesting a check of the oil level for a pump. In some work orders, all the instructions are already broken down step-by-step, but level of detail is inconsistent (depends on who wrote the work order). When changing oil, you need the exact oil type for each pump. If the wrong oil is used then the damage can be catastrophic, requiring the pump to be trashed. Test Scenarios 2: Planned maintenance: check oil level and change oil as required Without LexX: Cycle time: 1 hour • There is a list with pump/oil information on the computer; technician would need to look up the pump and then the oil. • However, the software system they use is very difficult to use and technicians tend to avoid it as much as possible. • Technician has stuck a paper version of the list to his locker, • If that doesn't cover it, he asks a colleague with a better memory for which pump takes which oil. Results with LexX: Cycle time: 10 minutes, 80 % Reduction • Gets to pump and oil in an instant • "Takes the guesswork out of it." • No paper on his locker • No consultation needed • With LexX technician can entrust the job to an apprentice, focus elsewhere. 43
  44. Test Scenarios 3: Main Belt Repair Issue Description Apprentice provides support to experienced technician for a repair to the main belt and had a six-hour window for the job. As it was his first time doing the job, there was a bit of anxiety and uncertainty about what needed to be done. Experienced technician knew certain things ahead of time based on experience, like the dimensions of the bolts, what tools to use, what type of oil/grease etc. Apprentice wouldn't have known any of this stuff, so there was no chance he could have made the repair on his own. Anxiety, uncertainty, lack of independence Results: Using the LexX platform, apprentice says he has been able to prep for the repair ahead of time. He is able to look at photos, diagrams, identify which tools he would need, and be able to be more on-the-ball and proactive. He has been able to take the initiative rather than waiting for instruction from the experienced technician. The benefits highlight by the apprentice were, that having LexX platform will make him: · Confident, proactive, "backing himself"; · Less reliant on senior staff; · Spend less time on training; and writing weekly report. Quote: “Great training tool, to review procedures, schematics etc. in own time, and in preparation for jobs. Liked the 'Google' style of searching because it would save time trying to find the right information”. 44
  45. Background Lexx Technologies was the winner of a US Energy Provider’s Global Innovation Award in 2019. From this, a 12-week Pilot commenced to validate a set of field troubleshooting and performance management objectives, ahead of wider deployment. This Pilot took place at the Energy Provider’s Wind Farm located in Illinois. Objectives of the Pilot The Energy Provider’s key initiatives: Provide a user-friendly troubleshooter for desktop and mobile for technicians in the field Provide a troubleshooter configured for support, targeting fault resolution time Enable Algorithmic Work Order Classification by resolution type and auto-correction functionality Enable reports addressing performance analysis questions and spare and tools consumption trends Results: Estimated Efficiency Savings From the use of Lexx during the Pilot: 40% Troubleshooting Time Reduction 40% Time-To-Productivity Savings 10% Part Replacement Prevention LexX Deployment Case Study 2: US Wind Farm 45
  46. Lexx was able to ingest a large amount and array of data (including OEM manuals, work instructions schematics, SAP data, work orders, notifications, fault codes, OPs data, ROCC fault logs, events & alarms, inventory/spares, and SCADA) Data Ingestion Lexx improved the quality of data, andtherefore performance analysis Lexx was able to link various datasets accuratelyand efficiently to support performanceanalysis Lexx automated mundane taskssuchascorrecting, improving quality and classifying rawdata Lexx captured accurate data from troubleshooting to assist performance analysis Lexx empowered and reduced the workload of lead technicians Showed that Lexx can link troubleshooti ng and performance analysis Auto correct and auto classification returned high accuracy, with some results better than human interpretations Provided a single searchable interface to disconnected datasets linked to faults and turbines The learning feature enabled better capture of turbine behaviour and technician behaviour The Value | Key Findings Overall, Lexx enabled the availability and efficiency of critical equipment The Technician’s Response 84% agreed Lexx improved reliance on data, takingthe guesswork out of troubleshooting 86% agreed Lexx increased the reliance on digital tools while on the tower 90% agreed Lexx improved quality of repairs and allowed better utilisation of individual skills of technicians 46
  47. LexX can be used in the following maintenance & operational areas: Troubleshooting; Planned, preventive and breakdown maintenance; Performance diagnostics and analysis; Compliance & reporting; Issue and problem management; Asset life cycle management; Training and simulation; And publication management …and even more How Technology Can Be Used… The user interface is customised around individual Client requirements and processes Demo link: 47
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