Predictive Maintenance of Hull and Propeller for Marine Vessels

Transcription

1 Predictive Maintenance of Hull and Propeller for Marine Vessels Dr. Lilach Goren Huber Zurich University of Applied Sciences (ZHAW) SWISSED16 Conference, September 2016 With: Dr. Marcel Dettling (ZHAW) Simon Kunz (Mespas AG)

2 Outline Introduction: goals Statistical model as basis for predictive maintenance 2 B l P H l 1 Maintenance optimization scheme and results

3 Introduction: ship resistance A ship has to overcome friction resistance when moving forwards through water. Resistance factors: speed, geometry, loading (draft), trim, wind, currents, Waves Currents Draft / Load Fouling Speed Power Squatting Salinity & Temperature Fouling: rougher hull higher friction resistance. Accumulates over time Consequence: higher fuel consumption and emissions over time. Goal: plan maintenance in order to reduce fuel consumption.

4 Introduction: Maintenance options Sandblast & Paint: dry dock D Propeller Polishing P and / or Hull Cleaning H

5 Introduction: project goal Reduce the fuel bill and emissions: Provide an estimate of the current fouling state Fouling not observed directly statistical model Estimate the expected effect of fouling and of maintenance in the future Optimize maintenance scheduling and simulate different scenarios optimizing the life cycle management of marine vessels MESPAS enhances competitiveness among software providers

6 Estimating the hull condition speed Observed data Observed directly Literature models On average, time invariant draft wind Ship resistance Fuel consumption Not observed directly Depends on vessel, route, lay time, maintenance No literature models fouling Estimate from model

7 Model Model the fuel consumption (fc) as function of Vessel speed through water (v) Draft (d) Wind speed and direction (w) Time (t) Maintenance actions B t 1 t 2 T P H f v d w t log = C + C log + C log + C + C f v d w t c 0 v d w t Deduce the time dependence of the fc due to fouling

8 Results: added fuel consumption Additional fuel consumption due to fouling depends on time and maintenance Additional fc rate [T Propeller Cleaning Confidence Interval Date Fuel consumption on a particular cruising day, corrected for differences in speed and wind.

9 Verification of Estimates against Sea Trials fuel consumption rate [T/Hr] Raw fuel consumption Raw fuel vs. consumption speed v speed [Kn] Vessel cruises at lower speeds in operation than it was designed for. Fuel consumption was lower during sea trials. fuel consumption rate [T/Hr] Real world fuel consumption matches sea trial data after estimated fouling and wind influence was removed. Corrected fuel consumption Fuel consumption vs. speed w/o w speed [Kn]

10 Maintenance optimization For a given time horizon T determine: How many intervention measures (IM)? Which IM? when? Every IM has An effect on the fuel consumption Intervention costs Opportunity costs Objective function: total cost saving compared to the costs without maintenance. w 1 w 2 t 1 t2 S T maximize: Under the condition: ( ) N 1 C = C S t, t..., t ; w, w..., w C ( w) tot F 1 2 N N 1 M i i= 1 N i= 1 t = T i cost of action i

11 Optimization results: predictive maintenance oil price: 600 $/mt Payback Time: 12 months max. frequency: 5 weeks Maint. Propeller polishing Hull cleaning Dry dock cost 5000 $ 100,000 $ 1,000,000 $ The optimal maintenance schedule: Potential saving: 364,221 $ This amounts to 2.3% of the annual bunker price 23% of the expected additional fuel costs due to fouling are saved

12 Combining prior knowledge and data Very little (sometimes no) data available Noisy data Fouling is investigated in the literature evidence for expected fouling effect and effects of hull maintenance Use / combine observed data with prior knowledge by means of Bayesian inference: Pr ϕ Y ~ Pr Y ϕ Pr ϕ { m } { m} { m} Posterior distribution: probability of model given data Likelihood of observing the data given different model parameters Update the posterior with upcoming data Prior disribution: belief in possible model parameters Optimization is possible also in the absence of data

13 Uncertainty analysis of model parameters posteriors parameter distributions (priors): Fouling: 20 ± 3 % annual fouling contribution Effect of P: reducing 30 ± 3% of the fouling level Effect of H: reducing 50 ± 5% of the fouling level Effect Fouling of D: B reducing slope 97 ± P 3% of the fouling slope H level 2 B l l 1 P H Expected saving: 450,090$ Distribution of expected saving due to uncertainty in the fouling : The saving ranges between 250,000 $ und 660,000 $ with 90% certainty.

14 Sensitivity analysis: fouling prediction With probability of 30% the recommended schedule is the optimal one: With probability of 13% the recommended schedule changes to:

15 Summary Simple model Features selection based on physics Linear model allows for intuitive understanding Linearity allows for simplifications in optimization scheme Optimization is possible also without data Flexibility of optimization inputs/ constraints

16 Thank you!