Predictive Maintenance of Hull and Propeller for Marine ...em>Edit Basic page
16
Zürcher Fachhochschule Predictive Maintenance of Hull and Propeller for Marine Vessels With: Dr. Marcel Dettling (ZHAW) Simon Kunz (Mespas AG) Dr. Lilach Goren Huber Zurich University of Applied Sciences (ZHAW) SWISSED16 Conference , September 2016
Transcript of Predictive Maintenance of Hull and Propeller for Marine ...em>Edit Basic page
Zürcher Fachhochschule
Predictive Maintenance of Hull and Propeller for Marine Vessels
With: Dr. Marcel Dettling (ZHAW) Simon Kunz (Mespas AG)
Dr. Lilach Goren Huber Zurich University of Applied Sciences (ZHAW) SWISSED16 Conference , September 2016
Zürcher Fachhochschule
Outline
• Introduction: goals
• Statistical model as basis for predictive maintenance
• Maintenance optimization scheme and results
1l
B 2l
P H
Zürcher Fachhochschule
Introduction: ship resistance
A ship has to overcome friction resistance when moving forwards through water.
Resistance factors: • speed, geometry, loading (draft), trim, wind, currents,…
• 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.
Salinity & Temperature
Waves Speed
Squatting
Draft / Load Fouling
Power Currents
Zürcher Fachhochschule
Introduction: Maintenance options
Sandblast & Paint: dry dock D
Propeller Polishing P and / or Hull Cleaning H
Zürcher Fachhochschule
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
Zürcher Fachhochschule
• Not observed directly • Depends on vessel, route, lay time, maintenance • No literature models
• Observed directly • Literature models • On average, time invariant
Estimating the hull condition
Fuel consumption
Ship resistance
speed
draft
wind
fouling
Observed data
Estimate from model
Zürcher Fachhochschule
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
00 0 0 0 0
log log logcv d w t
f v d w tC C C C Cf v d w t
= + + + +
Deduce the time dependence of the fc due to fouling
B
T t1 t2
P H
Zürcher Fachhochschule
2014 2015
3.5
4.5
5.5
Date
Add
ition
al fc
rate
[T
Additional fuel consumption due to fouling depends on time and maintenance
Propeller Cleaning
Fuel consumption on a particular cruising day, corrected for differences in speed and wind.
Confidence Interval
Results: added fuel consumption
Zürcher Fachhochschule
10 15 20 25
05
1015
Raw fuel consumption v
speed [Kn]
fuel
con
sum
ptio
n ra
te [T
/Hr]
Fuel consumption was lower during sea trials.
Vessel cruises at lower speeds in operation than it was designed for.
10 15 20 25
05
1015
Fuel consumption w/o w
speed [Kn]fu
el c
onsu
mpt
ion
rate
[T/H
r] Corrected fuel consumption vs. speed
Real world fuel consumption matches sea trial data after estimated fouling and wind influence was removed.
Verification of Estimates against Sea Trials
Raw fuel consumption vs. speed
Zürcher Fachhochschule
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.
( )1
1 2 1 1 2 11
1
, ..., ; , ..., ( )N
tot F N N M ii
N
ii
C C S t t t w w w C w
t T
−
− −=
=
= ∆ −
∆ =
∑
∑Under the condition:
maximize:
cost of action i
T w1 w2
1t∆ 2t∆
S∆
Zürcher Fachhochschule
Optimization results: predictive maintenance
oil price: 600 $/mT Payback Time: 12 months max. frequency: 5 weeks
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
Maint. Propeller polishing Hull cleaning Dry dock
cost 5000 $ 100,000 $ 1,000,000 $
Zürcher Fachhochschule
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:
• Update the posterior with upcoming data
Optimization is possible also in the absence of data
Posterior distribution:
probability of model given data
Prior disribution: belief in possible
model parameters
Likelihood of observing the
data given different model
parameters
{ } { } { }PrP Pr~r mm mYY ϕϕ ϕ
Zürcher Fachhochschule
Uncertainty analysis of model parameters
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 of D: reducing 97 ± 3% of the fouling level
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.
Fouling B slope P slope H
posteriors
1l
B 2l
P H
Zürcher Fachhochschule
Sensitivity analysis: fouling prediction
With probability of 30% the recommended schedule is the optimal one: With probability of 13% the recommended schedule changes to:
Zürcher Fachhochschule
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
