Power-Gen International, December 13-15, 2016, Orlando, FL USA. The Use Cases of Prognostic Data Analytics Moritz von Plate, Cassantec AG

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1 Power-Gen International, December 13-15, 2016, Orlando, FL USA The Use Cases of Prognostic Data Analytics Moritz von Plate, Cassantec AG

2 Moritz von Plate, CEO Cassantec Agricultural Engineer, University of Bonn MBA, Georgetown University 7 years with The Boston Consulting Group in Berlin and Warsaw CFO of Solarlite GmbH, an award-winning pioneer in solarthermal power generation, Europe s fastest growing cleantech company in 2010 Since 03/2013 CEO of Cassantec AG The Prognostics Company Prognostics Today Failure risk Time t +1 t +2 t +3 t +4 t +5 t +6 Know the explicit time window until failure E: W: Copyright 2016 by Cassantec Ltd. 2

3 In retrospect, many malfunctions and failures could have been foreseen based on available data Copyright 2016 by Cassantec Ltd. 3

4 On top, the power industry faces significant challenges Power Industry Copyright 2016 by Cassantec Ltd. 4

5 Different approaches to maintenance have different data analytics requirements Reactive Maintenance ( Run to Failure ) Time-based / usage-based Maintenance Condition-based Maintenance (CBM) Required Data No systematic or databased monitoring Meantime between failure (MTBF) estimation or OEM recommendations Experience Process and condition data of the equipment Approach to data analysis No or very limited data analysis Data analysis is helpful but not necessary Extensive data analysis to identify necessary scope ( what ) and timing ( when ) Copyright 2016 by Cassantec Ltd. 6

6 Prognostic vs. Diagnostic solutions: addressing very different questions Prognostics When will this condition become critical? When will we get a warning, alert or alarm? When will be the best time to fix problems? Will we make it until the next scheduled outage? Will other plants in the fleet have the same issue? When / Will Diagnostics What is the condition of our power plant? Why is this condition critical, or why not? Which parameters are most indicative? Where do we find problem root causes? How do we best resolve challenges? What / Why / Which Where / How Copyright 2016 by Cassantec Ltd. 7

7 Prognostics the differentiation Versus Condition monitoring software Versus Diagnostics and Predictive Analytics Versus ERP and asset management software Versus work order management software Prognostics relates the condition data to the current and future malfunction risks whereas condition monitoring software is based on data projections, illustrations, and comparisons to generic alarm levels. Prognostics answers the questions related to timing (when) whereas Diagnostics and Predictive Analytics focus on answering questions on the current equipment condition (what / why / where). Prognostics provides decision support based on the current and future asset conditions and malfunction risks whereas ERP and asset management software offer limited top-level statistics. Prognostics helps prioritize and select work orders based on malfunction risks for critical assets. This, in turn, may be important input for work order management. Copyright 2016 by Cassantec Ltd. 8

8 But data analytics alone won t do the trick the combination with engineering know-how is key Know How + Data Analytics Malfunction modes Data features to identify malfunctions Approaches to mitigation of malfunctions Stochastic process model Library of condition and process data Utilization of all available data histories (i.e. vibration, temperature, pressure, flow, and speed) Copyright 2016 by Cassantec Ltd. 9

9 The implementation needs to intelligently and efficiently combine know-how and data analytics 1. Malfunction analysis Parameter identification Correlation assessment 3. Condition diagnostics & prognostics Condition-based maintenance planning 2. Data selection Data transformation Data analysis Copyright 2016 by Cassantec Ltd. 10

10 At first sight, data analytics applicability and benefits seem obvious yet three concerns often arise in practice Power plant unit A pump is running Do we meet data prerequisites? Can we prove our economic benefits? Power plant unit B pump is down Couldn t our experts take care of the analytics? Copyright 2016 by Cassantec Ltd. 11

11 Isn t data inadequate, given noise, varying load, etc.? Unlikely! The available data is typically sufficient 6 years Copyright 2016 by Cassantec Ltd. 12

12 Isn t the benefit low, given time-based maintenance? Unlikely! Benefits are achieved on multiple levels Benefit lever Reduce unscheduled maintenance and/or repair Shift maintenance into low-cost periods Shift maintenance into low-revenue periods Reduced preventive scope and/or frequency Better maintenance work order preparation Comment Use foresight to bundle maintenance work that is visible on the time horizon Use prognostics to move maintenance from unscheduled to scheduled Plan the maintenance work in such a way that, for example, overtime and hiring outside contractors can be avoided Use foresight to schedule maintenance work during low revenue times, e.g. when load is expected to be low Execute work that is required technically and minimize extra efforts, while maintaining the same risk profile Use longer advance notice to make work order preparation more efficient Preempt damages Identify malfunctions long before they develop into damages Reduce redundancies Knowledge management and Benchmarking Tie up less capital in redundant assets, while maintaining the same risk profile Increase employees efficiency through improved knowledge management Provide a basis to allow learning and best practices sharing Copyright 2016 by Cassantec Ltd. 13

13 Don t we have experts for analytics, especially prognoses? Unlikely! Analytical basis hard to provide via gut feel Predictive Analytics Prognostics Today????????? Warning Time t +1 t +2 t +3 t +4 t +5 t +6 Today Failure risk Time t +1 t +2 t +3 t +4 t +5 t +6 Know that something will happen at some point in the future Know the explicit time window until failure Copyright 2016 by Cassantec Ltd. 14

14 Hence, there are many use cases revolving around asset management (1 of 2) Category Maintenance and Repair Use Cases Long-term scheduling of maintenance Short-term preparation of reactive maintenance Maintenance staff planning and allocation Operations Production planning according to the future availability profile Increased assets availability and minimized downtime risk for field projects, e.g. in upstream oil & gas Finance Saved profit opportunities from downtime reduction Decreased annual maintenance costs Increased total expected benefits petroleum assets operators Optimized insurance policy and costs Planning budget(s) and total cost of ownership (TCO) Life Cycle Management Replacement and retrofit planning RUL-optimal exploitation Active management of the remaining useful life by adjusting operating capacity Copyright 2016 by Cassantec Ltd. 15

15 Hence, there are many use cases revolving around asset management (2 of 2) Category Risk Management Procurement Use Cases Operationalization of risk valuation standards Full transparency over future downtime risk for all critical assets components and active risk management Optimized parts and service procurement General Management Operationalized management reporting standards Training of maintenance workforce and reliability through prognostic solution Benchmarking: recognizable risk-impact of different operations strategies trough displayed risk profiles Optimized health, safety & environment (HSE) reporting Product Development Original asset design and development of industrial assets Timely retrofit and/or replacement of sensors Copyright 2016 by Cassantec Ltd. 16

16 Case Study 1 Wind power Reduction of O&M costs through prognostics Prognostic Report for a wind turbine Minimized operational and maintenance costs; Optimized spare parts and service procurement; Improved turbine availability and performance; Optimized insurance policy and costs. Copyright 2016 by Cassantec Ltd. 17

17 Case Study 2 Fossil power - full risk transparency with prognostics Copyright 2016 by Cassantec Ltd. 18

18 Fossil power improving critical assets reliability Case Study 3 Reliability Report combustion turbine Forecast of related malfunctions for most critical assets Condition-based, quantitative information on remaining useful life (RUL) Incorporating the complete set of monitoring and diagnostic data available Managing fleet-wide malfunction risks Copyright 2016 by Cassantec Ltd. 19

19 Case Study 4 Fossil power risk of unplanned outages managed with prognostics Unplanned outages occurred despite frequently scheduled maintenance, during which issues could have been dealt with had they been known. Validation results: Avoid unplanned outages in future; Ensure operational availability and reliability of the most critical assets; Evaluate risks of malfunction; Revisit maintenance strategy. Copyright 2016 by Cassantec Ltd. 20

20 Retention of Critical Knowledge through Prognostics At 50% of the utilities, more than 50% of professionals are going to retire in the next five years Structured documentation of asset operators tacit knowledge Implicit experience and know-how as basis for the forecasting process Well-documented catalogue of malfunctions Malfunctions possible identification through data parameters and possible mitigation action Utilization for HSE reporting, management reporting, benchmarking and standardization across assets and plants, knowledge management and training Copyright 2016 by Cassantec Ltd. 21

21 Thanks a lot for your attention! Q & A

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