Il caso della Prescriptive Maintenance

Transcription

1 Reimagine 2018 HPE Pointnext AI at Work Il caso della Prescriptive Maintenance nel mondo industriale Edmondo De Salvo WW AI, Data & Emerging Technology Center of Excellence 24 maggio 2018

2 We live in a world where everything computes and now also acts Technology, security and intelligence will be embedded everywhere Everyone and everything will be connected and learning Everything will be understood

3 Powerful AI Experiences Already An Integral Part Of Our Daily Lives

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5 Artificial Intelligence Use cases and target markets

6 Il caso della Prescriptive Maintenance nel mondo industriale

7 Traditional Maintenance Approaches Fall Short Prescriptive Maintenance Use Case Limited efficiency On asset breakdown At pre-defined time intervals On shut-down machines Anticipate asset breakdown Machine-learning and IOT driven Optimize the outcome based on risk and cost Model transparency Action automation Reactive Preventive Predictive Prescriptive Condition-based monitoring

8 Need for maintenance optimization Speed Delayed response to maintenance issues and lack of automation, optimum solution based on asset and business data Economics High inventory, maintenance costs and unplanned operations downtime. Productivity loss due to quality issues Risk Risk to employee/ public safety and environmental impact due to root cause visibility

9 Prescriptive Maintenance Benefits MFG, Energy & Utilities, Aerospace & Defense, Transportation, Healthcare Limit Scheduled Downtime Reduce Costs Through Automation Avoid Defective Production and Improve Quality

10 HPE Digital Prescriptive Maintenance Service Key functionalities Handles real-time and batch data streaming Integrate analytics and AI (machine learning, deep learning ) Provides data exploration like anomaly detection Provides data prediction like equipment or product failure Guides maintenance through interpreted prediction Prescribes best decisions based on company specifics Executes actions through customized and automated workflow Operates at the Edge and/or the datacenter 10

11 Our approach in 3 simple steps Explore Experiment Evolve HPE Artificial Intelligence Transformation Workshop (1-day) AI/ ML Proof of Value Services (8-12 weeks) Custom, subscription, consumptionbased professional or operational services Understand outcomes and challenges Align teams around common vision and plan Explore the best use cases and technologies Gather and validate your use cases and data Conduct a Proof-of- Value for select use cases Integrate AI to the existing applications Modernize data, compute, storage platforms Manage change, train your teams and AI systems Protect, manage and consume data and analytics

12 Getting started with HPE Artificial Intelligence Workshop One-day visual and interactive workshop covering artificial intelligence, machine learning, big data and analytics topics Benefits Align business, data and IT operations teams Explore opportunities, priorities and select relevant use cases Identify dependencies, data sources, your level of readiness Define a high-level roadmap for your intelligent data strategy 12

13 HPE Digital Prescriptive Maintenance POV Service Deliverables Environment setup (at customer site or at HPE AI Lab) Customer Data load and cleaning Design of Analytics scope (Descriptive, Diagnostic, Interactive, Predictive, Prescriptive) Development and reviews with customer Business Insights report building and presentation Next steps proposal

14 HPE Digital Prescriptive Maintenance POV examples Improved product quality management at Aluminum maker Streamlined process through correlation analysis Product defect management at steel maker Working with HPE Pointnext, the client implemented a PoV with predictive model to identify the anode blocks that are likely to fail in production. Benefits Increased energy efficiency Increased quality of the final product HPE Pointnext implemented classification model to identify key process variables correlated to temperature deviations and helped the client analyze the process. Benefits Prevent defect products Understand the factors causing defects HPE Pointnext replaced the instrumental control with a predictive model based on technological data from the current and previous operations to predict parts failures and end product quality. Estimated benefits Predict maintenance 1 month before the failure Eliminate unneeded expensive post-production control Prevent production defects and reduce downtime 14

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