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A framework for prognostic-based real-time life extension
Seyed A. Niknam Dr. Rapinder Sawhney
The University of Tennessee, Knoxville
Outline
Motivation
Introduction
Background
Elements of the life-extension program
Non-monotonic degradations
Decision Making
Future works
Prognostic Reports
Report 1
– The remaining life of system A is 55 days
Report 2
– System A will last 55 days with no change in the current operating conditions. However, by decreasing the load and speed around 10%, system A will be working for 95 days. The optimal time for the replacement of the degraded units would be after 70 days when the spare parts, equipment and personnel are available. Also, after 65 days the chance of adverse effect on unit B increase by 20%. Moreover, …..
Motivation
Efficiency over the nominal design life
Investment for new installations
Failure rate of 300kW & 1 MW wind turbines (failure per turbine per year) [1]
Unit 300kW 1MW
Generator 0.059 0.126
Brake 0.029 0.056
Hydraulics 0.039 0.096
Yaw system 0.079 0.152
Sensors 0.037 0.151
Pitch system 0.034 0.237
Blade 0.078 0.308
Gearbox 0.079 0.255
Shaft/bearings 0.002 0.046
Introduction
Operation and Maintenance (O&M) costs: 10-30%
O&M costs increase over the 20 years of wind turbine operating life
Minimizing the O&M costs:
– Reliability optimization
– Condition-based maintenance
– Maintenance strategies
Introduction
System durability vs. O&M costs
A multiple-criteria decision making process – Adequate information with respect to failure-
causing faults and the remaining operational life of the faulty components and sub-systems
Objective: to provide a frame for supporting a prognostic-based life-extending program – Lack of knowledge (e.g. about aging mechanism),
– Lack of tools (e.g. trust worthy prognosis algorithm),
– Lack of data and time
Background
ISO 13381: General guidelines for prognostic Prognosis is a case-dependent process, and therefore, it
is not reasonable to specify certain approaches or methodologies in prognosis standards.
Since prognosis is mainly based on data, determining the degree of certainty of prognosis process i.e. the confidence level is an essential task.
Four phases: – Pre-processing: identifying and modeling the failure modes – Existing failure mode prognosis: severity of existing and
future failure modes – Future failure-mode prognosis: estimation of time to failure – Post-action prognosis: how to prevent the initiation of
future failure modes.
Background
Applying prognosis for extending the operable life
– Damage-mitigating control system: connecting the dynamics of material degradation with the current active control technologies
– Major challenge: to characterize the fatigue damage model and make it compatible with the control system
Background
Emphasis on real-time and onboard prognostic
Business issues and the level of risk in appropriate selection of prognostic model
Background
Staged prognostics approach
Monitoring and managing passive systems, such as the nacelle in wind turbine
Prognostic-based Life Extension
Priority: to mobilize the potential residual life to guarantee the return on investment
The first and foremost step: effective and robust diagnosis – To detect and identify the impending faults
and relevant collateral damage. – Competing failures – Early fault detection (time) – Nature of the incipient faults (e.g. pitting of
spur gears) – Real-time diagnosis
Prognostic-based Life Extension
Real-time estimations of remaining useful life (RUL)
– RUL: time that a component or system is able to operate without the need for major repair and maintenance or without significant rate of minor faults
– Predict the chance that a system operates without a major failure up to a specific time
Prognostic-based Life Extension
Prognostic-based Life Extension
Effects-based or individual-based
– prognostic methods based on sensed or inferred degradation measures.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8degradation paths of failed units (noisy data)
time
degrd
ation m
easure
s
( , , )i iy t
Prognostic-based Life Extension
Self‐healing or human interventions are desirable.
Repair activities:
– Major repairs have direct influence of the age of system. Normally major repairs require special equipment and have longer process.
– Minor repairs reduce the rate of aging and require short period of downtime e.g. routine services, oil change and alignments.
Prognostic-based Life Extension
Concerns about the end-of-life threshold:
– Adjustment: the failure threshold should be adjusted after life-extending events. This will add complexity to the prognostic algorithms.
– Using failure zone
– Ignore a predefined critical threshold (path classification and estimation model)
Parameter Selection
Three features were introduced by Coble and Hines to characterize suitable prognostic parameters [4, 5]:
Monotonicity: Underlying positive or negative trend of the parameter. It is given by the average difference of the fraction of positive and negative derivatives for each path."
Prognosability:
Trendability: Indicates the degree to which the parameters of a population of systems have the same underlying shape and can be described by the same functional form."
( )exp( )
( )
std failurevaluesprognosability
mean failurevalues startingvalues
Question
Is there any non-monotonic degradation trend?
Examples of non-monotonic degradation
Vacuum fluorescent displays
Degradation in light displays such as plasma display panels [6]
Critical characteristic of light display quality is luminosity (or brightness)
Examples of non-monotonic degradation
Fatigue Crack Closure
Excellent example of self-healing [7]
Examples of non-monotonic degradation
Fouling in Heat Exchangers
0 0.5 1 1.5 2 2.5 3 3.5 4
x 104
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6x 10
-3
Overa
ll T
herm
al R
esis
tance
Time (minute)
Overall Thermal Resistance 1/UA
Examples of non-monotonic degradation
Rotor Unbalance
0 0.5 1 1.5 2 2.5 3 3.5 4
x 104
0
200
400
600
800
1000
1200AE SIGNAL - Bearing 1
Time
RM
S
Trend Analysis
Trend: general pattern of the mean level.
Trend analysis: the process of identifying significant changes in the magnitude of a reference variable
Decision Making
Considerable research on prognostic algorithms
Attention to post-prognostic issues
Return on Investment (ROI) associated with the opportunities created by prognostic
Optimizing availability
Take actions in the period of the remaining operational life
Optimal set of actions (i.e. alternatives)
Decision Making
Challenges:
Presence of various decision alternatives, and therefore, the need for compromising – Fault accommodation (i.e. modifying control
rules or using redundancy) - Derating
– Altering operation conditions or tactical control
– Maintenance practices
Multiple criteria which usually have conflict and make the judgment of alternatives a complex endeavor.
Uncertainty associated with the available data
Decision Making
Modifying the operating conditions creates the need for an optimization procedure
Power curve for a 5 MW wind turbine
Decision Making
Multiple Criteria Decision Making/Analysis (MCDM/MCDA)
– Multiple Attribute Decision Making (MADM): focuses on selecting the best alternative among a finite set of predetermined alternatives
– Multiple Objective Decision Making (MODM): deals with creating alternatives from a large number of alternatives
Future Works
Prognostic: – Adaptive prognostic models – Real-time and onboard prognostic – Ability to handle non-monotonic trend
How well degradations measures indicate the repair activities – Degradation measures can be either a directly measured
parameter or a function of several measurable parameters
Noise smoothing and automatic trend estimation methods
Detecting and modeling the turning points Fuzzy logic-based approaches for trend detection:
– Able to identify the underlying trends (particularly during high fluctuations of a signal)
– Able to handle noisy data
General model for MODM
References
[1] Arabian-Hoseynabadi, H., H. Oraee, and P.J. Tavner, Failure Modes and Effects Analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems, 2010. 32(7): p. 817-824
[2] Lee, J., et al., Intelligent prognostics tools and e-maintenance. Computers in Industry, 2006. 57(6): p. 476-489
[3] Reinertsen, R., Residual life of technical systems; diagnosis, prediction and life extension. Reliability Engineering and System Saftey, 1996. 54: p. 23-34.
[4] Coble, J.B., Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters, in Nuclear Engineering. 2010, University of Tennessee.
[5] Coble , J. and J.W. Hines, Fusing Data Sources for Optimal Prognostic Parameter Selection, in Sixth American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human_Machine Interface Technologies NPIC&HMIT. 2009: Knoxville, Tennessee.
[6] Bae, S.J. and P.H. Kvam, A Nonlinear Random-Coefficients Model for Degradation Testing. Technometrics, 2004. 46(4): p. 460-469.
[7] Lee, S.Y., et al., A study on fatigue crack growth behavior subjected to a single tensile overload - Part 1. Acta Materialia, 2011. 59: p. 485-494.
[8] Nielsen, J.J. and J.D. Sørensen, On risk-based operation and maintenance of offshore wind turbine components. Reliability Engineering & System Safety, 2011. 96(1): p. 218-229.
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