P R O G N O S T I C S

Transcription

1 P R O G N O S T I C S THE KEY TO PREDICTIVE

2 Me BEng Digital Systems Engineer Background in aerospace & defence and large scale wireless sensing Software Verification & Validation Wireless sensor networks (EMMON) Health & Usage Monitoring Systems / Condition Monitoring Systems

3 problem: downtime 300% The amount by which Total Downtime Cost is usually underestimated $2.5m The cost per hour of downtime in automotive manufacturing 24% Percent of total manufacturing cost attributed to downtime 90% Amount of maintenance work categorised as crisis work to fix breakdowns D o w n t i m e i s e x p e n s i v e

4 I DEAL PREVENTATIVE M AINTENANCE: OPERATING OPERATING OPERATING OPERATING MTBF MTBF MTBF MTBF PLANNED MAINTENANCE PLANNED MAINTENANCE PLANNED MAINTENANCE REAL- WORLD PREVENTATIVE M AINTENANCE: OPERATING OPERATING OPERATING OPERATING MTBF MTBF MTBF MTBF PLANNED MAINTENANCE PLANNED MAINTENANCE PLANNED MAINTENANCE UNPLANNED BREAKDOWN (panic) UNPLANNED BREAKDOWNS (double panic) UNPLANNED BREAKDOWN (sigh)

5 old: new PREVENTATIVE PREDICTIVE Known costs + surprise items Replacement of good parts Potential for unintended damage Inefficient No idea of actual machine condition REACTIVE Exact knowledge of machine condition Parts only replaced when deeded Fewer surprises Reduced operational costs Reduced downtime SCALABLE

6 I DEAL PREDICTIVE MAINTENANCE: OPERATING!!! CONDITION WARNING OPPORTUNISTIC MAINTENANCE REAL- WORLD PREDICTIVE MAINTENANCE: OPERATING OPERATING!! OPPORTUNISTIC MAINTENANCE HOW LONG? CONDITION WARNING PLANNED MAINTENANCE

7 Is not a dirty word P R O G N O S T I C S Understanding when a machine will stop being able to work

8 prognostics: evolution Health and Usage Monitoring Systems (HUMS) introduced Prognostics research for industrial applications grows N O W British international helicopters crash The birth of modern industrial automation systems Prognostic Health Management Society is formed Senseye releases automated condition monitoring & prognostics

9 the birth: condition monitoring Does it sound funny?

10 C O N D I T I O N M O N I T O R I N G The good: I t c a n b e v e r y a c c u r a t e I m p r o v e s s a f e t y I d e n t i f i e s f a i l u r e s S h o w s c u r r e n t h e a l t h The bad: I t s e x p e n s i v e I t s s l o w Tr a i n i n g r e q u i r e d N e e d s s p e c i a l i s t i n t e r p r e t a t i o n C a n b e s l o w t o s h o w b e n e f i t s C a n n o t s c a l e

11 T h e U G LY

12 D i a g n o s t i c s p r o v i d e s C u r r e n t S t a t e

13 what is: prognostics Forecast failure Forecast of when functional failure will occur Predict future state Models of failures Predict complex failure conditions Use of machine context Based on data characteristics of failures Beyond Diagnostics Condition Monitoring is only part of the task For action, foresight is required P r o g n o s t i c s p r o v i d e s R e m a i n i n g U s e f u l L i f e

14 P E R F O R M A N C E / C O N D I T I O N prognostics: remaining useful life Normal operation band Anomaly detection threshold Diagnosis threshold Variation in RUL ` Functional failure threshold Predictive Diagnostics Prognosis O P E R A T I N G T I M E Senseye covers the full timeline

15 Prognostics: Trajectory Modelling (instance based) Example RUL trajectories for many failures over time of similar machine type Note: not all failures are equal!

16 Scaling the Diagnostics Engineering Role Human resource vs Connected devices Human resources don t scale Automate the Diagnostics Engineering Role

17 it s: automated & CONDITION MONITORING PROGNOSTICS ANOMALY DETECTION AUTO OPTIMISATION PROBABILITY OF RISK FORECASING DEGRADATION FORECASTING AUTO CONDITION EXTRACTION AUTO TRAINING REMAINING USEFUL LIFE STATE AND USAGE DETECTION AUTOMATED THRESHOLDING How is my machinery behaving right now? For how long will my machinery continue to work?

18 key: benefits 10 40% Lower maintenance costs 30-50% Reduction in downtime 45 55% Increased maintainer productivity 85% Increase in maintenance accuracy A v o i d d o w n t i m e and s a v e m o n e y

19 Predict the Unpredictable R e d u c e u n p l a n n e d d o w n t i m e and m a x i m i z e O E E w i t h S e n s e y e, t h e u l t i m a t e c l o u d - b a s e d p r e d i c t i v e m a i n t e n a n c e a p p l i c a t i o n

20 technology: convergence Condition monitoring Industry 4.0 / Industrial IoT Prognostic s Machine learning / AI

21 Any & many Any connected machine is supported, big or small, from 10 to 10,000 Scale effortlessly Remaining Useful Life Understand for how long your machinery is likely to perform Improved predictive maintenance planning Motor has a 70% risk of failing within two weeks Condition Monitoring Automated condition monitoring insights, only relevant information is highlighted. Takes the pain out of asset management Web based Nothing to install or maintain, powered by the cloud and accessible on all devices. No infrastructure to support

22 removing: downtime Panicked repairs Amount of uptime On Time and In Full (OTIF) deliveries Overall Equipment Effectiveness (OEE) Costs

23 The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. - W a r r e n B e n n i s

24 @senseyeio T H A N K Y O U +44 (0) (UK) +44 (0) (Alex)