Towards a Usage Driven Maintenance Concept: Improving Maintenance Value Tom Stuivenberg1, Adel A. Ghobbar, Tiedo Tinga, Richard Curran
Abstract For critical airline components, design and failure data is not released by the manufacturer, and the maintenance execution of these components is mandated, planned and executed by the manufacturer. This yields maintenance practices identical for all systems within the fleet, independent of the historical usage of the operator. And since the design spectrum is not validated towards the actual usage spectrum, this will result in too early replacements of components which in turn increase life-cycle maintenance costs and reduce overall availability. In this research, a usage severity driven maintenance framework is proposed, which zooms in at the actual failure mechanisms applied on the component and then calculates its service live based on the loads acting on the component under several circumstances. For the Chinook T-55 engine, this maintenance strategy reduces life-cycle maintenance costs with 20% while availability has increased.
Keywords Usage-driven maintenance, Failure Modelling, Maintenance, Reliability, Failure Prediction, Maintenance Value
1. Introduction Maintenance intervals for critical engine components are based on a design spectrum, which is a composite worst case spectrum to cover all possible missions by all anticipated operators. The actual usage spectrum of an operator will however deviate from this conservative design spectrum – especially for military operators
1 T. Stuivenberg ( ) Delft University of Technology, Faculty of Aerospace Engineering, Air Transport and Operations Department, Kluyverweg 1, 2629 HS Delft, The Netherlands e-mail:
[email protected]
J. Stjepandic´ et al. (eds.), Concurrent Engineering Approaches for Sustainable Product Development in a Multi-Disciplinary Environment, DOI: 10.1007/978-1-4471-4426-7_31, Ó Springer-Verlag London 2013
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–, probably causing over-maintenance. Although the usage of individual aircraft within a fleet is different, maintenance intervals are equal for the complete fleet. Maintenance optimization should therefore include the assessment of the remaining useful life of the components, and the related replacement intervals based on actual degradation, rather than assumed degradation. This analysis should go further than the traditional statistical analysis of the failure moments based on historical data, since this is not applicable if the failure rate is not constant and loaddependent. This paper proposes an alternative approach which shows how maintenance performance can be measured using a dynamic system on the base of usage profiles, given a maintenance organizational infrastructure. The approach is to zoom into the material level for obtaining the remaining useful life. It then covers the transition from usage (global) to local loads. Then, the damage accumulation at each load can be described by some failure model. When this approach can be linked to maintenance activities, the ‘value’ of the maintenance process can be improved, especially for military systems where the load spectrum is large and data is limited. The methodology is applied to the Chinook T-55 engine and applications are shown for three components which are identified as cost and maintenance critical, and who mainly determine the intervals of maintenance.
2. Literature review The definition of reliability is, according to [1], “The ability of the item to maintain the required function for a specified period of time under given operating conditions”. This assumes that the relation between the functional capability of the system and the operating condition under which the system is used is known, and that it will be used for the prediction of the reliability in time. In reality, reliability predictions however are mostly based on usage duration, combined with an assumed deterioration of that component in time. This allows the user to predict the reliability of a single component under a specific load case, but in realistic scenario’s, this evaluation becomes too complex for systems with more than one component in a dynamic environment with varying loads. In current maintenance procedures, the actual moments of failures are thus not assessed, and many parts are replaced far before the end of their service life. Figure 1 shows the value of a load case assessment [2]. It shows possible values of an arbitrary damage parameter D at three moments in time (t1,t2 and t3) using three normal distributions. The uncertainty in the damage parameter that is observed is caused by: • Unknown actual usage; • Uncertainty in the effect of usage on (internal) loads; • Variations of the life consumption for a given internal load, caused by variations in material properties or dimensions.
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Since the component/structure is continuously degrading in time, the damage gradually increases as represented by the movement from the distribution of the parameter D to the right. Since the relative uncertainties as described above increase in time, the width of the distribution is enlarged resulting in conservative and too early replacements. This conservatism in the interval determination can be reduced by decreasing the uncertainty in the predicted condition by applying information about the actual usage for the determination of the intervals. This will optimize the maintenance schedule, reduce maintenance costs and improve system’s availability. Knowledge on the relation between usage profiles and the deterioration of the system/structure/component thus helps improving the reliability assessment, which drives the maintenance program.
Fig. 1 Evolution of the distribution of the damage parameter (D) in time.
Existing models to incorporate usage behaviour of the operator in the maintenance interval assessment can be categorized into probabilistic and prognostic models. Probabilistic models are mathematical, and are considered ‘static’ as they assess the condition of the component at a specific point in its life-cycle. Examples are the limit-state approach [3,4], the use of failure distribution functions as the Weibull distribution [1], Fault Tree analysis [5], Cut Set approaches [6] and stochastic evaluations as a Markov analysis [7]. The disadvantage of all these methods is that they do not fully relate the usage of the system in the past with deterioration of the system in time. Prognostics models on the other hand try to include user profiles into the reliability distributions, and then estimate the future state of the system. Static and dynamic variations exist. The static methods are based on estimated load and usage scenario’s which are used for the failure rate assessment.
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The dynamic models on the other hand measure the loads on the system continuously (Condition Monitoring), and use them to predict the future state [8,9]. Of all existing methods, both probabilistic and prognostic, only a few are capable of incorporating usage data. The majority of those models assume that the system’s degradation level can only be known through periodic inspection. Another common assumption is that maintenance can be predicted on the base of historical maintenance data. However, this is not true when the load spectrum is significantly different for individual systems/components. A model that includes user profiles, and then assesses a time of replacement is thus not commonly used, but would be useful in maintenance optimization modeling.
3. Methodology The literature review shows that it is important to understand failure behavior of systems and components in order to optimize their maintenance intervals. Insight in the governing loads on the components then enables the ability to relate these loads with usage behavior. The service life of components can then be calculated for different failure mechanisms. A framework is developed, proposing a usage-based maintenance infrastructure. The inclusion of the operational profiles and related loads is an extension towards most existing models, and it changes the assessment of the effectiveness of the preventive maintenance schedules in the existing strategies. In this framework, shown in Figure 2, the mission type and mission duration of a specific flight will result in a specific load. Together with environmental conditions, the material properties and its geometric dimensions, this specific load result in a failure mechanism such as fatigue, creep, wear and erosion. These failure mechanisms ultimately result in the failure of the component, and are different for any load cycle. When the degradation of the component can be calculated for the failure mechanisms identified as most critical, a remaining useful life estimation can be made. Maintenance must then be executed at the moment the calculated and load dependent failure rate (D) reaches a pre-determined limit; the maintenance threshold. A usage-based stress evaluation of the components then result in a typical failure mechanism different for varying conditions (environment, duration, type). Maintenance intervals are now based on actual degradation and no longer on the base of assumed degradation. The value of this framework is shown in this paper, by simulating the engine maintenance performance for the Chinook T-55 engine. The analysis starts with the determination of the maintenance value using the Value Operations Methodology (VOM) approach [10]. Weighting factors for the dif-
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ferent objectives are derived from the Analytical Hierarchical Process (AHP) method [11].
Fig. 2 Evolution of the distribution of the damage parameter (D) in time.
Then, a usage spectrum is developed that relates loads with a service life calculation. This is a categorization of typical mission profiles flown by the operator, the relative amount of occurrences of that specific mission and the related engine settings. The third step is to relate the engine parameters that belong to a specific mission type with the degradation of the components during this mission. Therefore, a detailed calculation of the loads and their relation to the service life is performed. The prediction of the fatigue life is established using the Palmgren-Miner rule [14]. After applying the stress calculation, the time history of the stresses for a specific flight are translated into a stress range histogram, using a Rainflow Cycle Counting algorithm [15]. Rainflow cycle counting is used to decompose the irregular time history into equivalent stress of block loading. The resulting stress range histogram can then be used to predict the remaining useful life based on an S/N curve [16,17], which shows the magnitude of a cyclic stress (S) against the cycles to failure (N). For creep failure, the Robinson-Taira rule is used [18]. Using this equation, the creep rate is divided into p section of periods ti and life limit tr,i. Damage D is per period and dimensionless, while D=1 represents failure. A discrete event simulation is carried out to simulate the life-cycle performance of the maintenance concept, based on the value function defined earlier. In this simulation, the usage spectrum of a specific engine is simulated in accordance
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with the standard and usage driven maintenance approach. The original failure rates, other than the maintenance intervals, are established using censored data technique approaches to obtain a meaningful estimate of the fundamental reliability parameters: the cumulative failure distribution F(t), the reliability function R(t) and the hazard function λ(t). The Direct Method, a Product Limit Estimator, the Rank Adjustment Method [19] and the Kaplan Meier Approach [20] are used. Results are shown in Figure 3 for the 1st stage compressor blades. In the figure, also a Weibull curve is plotted that best fit the determined reliability function. The simulation model now uses this conservative Weibull function to simulate the rate of failure. Furthermore, the environment and exposure to erosion damage is modelled using erosion prediction models for different environments [21].
Fig. 3 Reliability plot 1st stage compressor blade based on censored data techniques
4. Results The Chinook Honeywell T-55-L-714-A engine is selected as it is an aviation system of which the maintenance planning and execution is completely in hands of the manufacturer. Maintenance is carried out every 1500 flight hours, independent of the past usage. Verification of the usage spectrum with a design spectrum is not possible, since the manufacturer does not release any design infor-
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mation. The hypothesis that the degradation of the engines experiencing different types of operations is different for any individual engine was tested by examining the margins of the Power Turbine Inlet Temperature. A degrading engine has decreasing margins of this temperatures at the same operational test conditions. Based on daily flight checks, deviations can be monitored and the slope of deterioration is tested. This analysis showed a decreasing trend, different for every engine. It was seen that every engine shows a different deterioration rate, except when they were on the same helicopter at the same period, and thus experienced the same loads. It was concluded that engine deterioration is mostly load-dependent, and different for every individual engine. By maintaining all engines in the same manner, overmaintenance is created. A usage severity framework is therefore applicable for the Chinook Honeywell T-55 engine. Since not all engine components can be modelled and calculated, the three components with the most significant contribution to engine maintenance performance in terms of availability and costs are determined using a Degrader Analysis [13]. Based on this analysis, the 1st stage turbine blade, 1st stage turbine disk and 1st stage compressor blades were identified as maintenance drivers and the usage driven maintenance concept is applied to those three components. Furthermore, creep and fatigue were identified as the life-limiting failure mechanisms. The analysis of the maintenance performance is accompanied by the determination of its value, using the VOM approach [10].
(1) Weighting factors for the different objectives are derived from the AHP approach. The resulting value function is then shown in equation 1, where ε is assumed to be 0. For every flight, the the fatigue and creep failures are then calculated in order to determine the average failure rate of a specific mission type. Figure 4 shows the stresses of this specific flight in time. A histogram of the stress cycles and amplitudes is then shown in figure 5. For this flight, the fatigue rate and creep rate are determined to be 0.00019160 and 0.00003020208 h-1 respectively. Failure, resulting from fatigue damage, is expected at 5219 flight hours. This analysis is conducted for all types of mission flights, resulting in a distribution of the damage parameter per flight type, dependent on the mission environment: deployed or standard operations. Table 1 shows the results for the turbine blade.
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Fig. 4 Stress accumulation during a flight for the turbine blade
Table 1 Average life time for the three failure mechanism at different mission types Average life limit (h) – Turbine blade Mission Type
Deployment
Standard
Fatigue
Creep
Fatigue
Creep
Training
3612.15
7258.39
4608.94
15555.89
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17877.91
1119.69
14870.22
Mission
1927.21
17332.43
2614.55
10214.41
Transport
2100.91
14448.48
2901.14
8316.39
Combat
2402.17
15453.52
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Based on this usage-damage relation, it is seen that the test flight has the most impact on the degradation. Training flights on the other hand have the lowest impact on the fatigue life, despite the remaining useful creep life is the lowest for transport flights. Furthermore, the difference between flying in deployed or standard situations is perfectly shown by the results of the model. The usage of a specific system is thus important in the determination of the maintenance intervals. A normal distribution of the degradation parameter is used as input in the simulation
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model and the value of the maintenance process is compared with respect to the maintenance approach where maintenance is executed at 1500 flight hours. When these tests are performed and the life cycle of the engine is simulated, the maintenance strategy as provoked by the manufacturer and based on the design spectrum, can be compared with the usage driven strategy. In this paper, effects are compared using the Value Operations Methodology, showing that the life cycle engine maintenance costs can be reduced by 14.7% when this usage driven strategy is adopted. Also the availability of the engine improves from 78.15 towards 91.16%. It is seen that the type of mission and the type of operation results in different degradation intervals. Deployed operation, especially for combat extraction and training flights, is more severe than flying in the standard environment.
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Fig. 5 Stress histogram for the stresses in figure 4.
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5. Conclusion The maintenance of components of which the design spectrum is unknown, and of which no failure data is available, is considered to be over-conservative. Insight in the actual accumulation of the damage parameter, which is a function of its usage spectrum, can improve the value of the maintenance process. In this paper, a usage driven maintenance strategy is proposed for a system – the Chinook T-55 gas turbine engine – that has a divers usage spectrum but where maintenance is executed equally for all available engines. The service life calculation, usually performed based on the design spectrum and using full scale fatigue and creep tests, is now conducted by zooming in at the material level and by calculating the service life of the most valuable components under different circumstances. A usage severity driven maintenance framework is then used for the execution of the maintenance activities, based on the actual damage parameter rather than calendar time or flight hours. This approach includes usage data and then calculates its effects upon the remaining service life of that particular component. It is concluded that a usage driven maintenance model is beneficial since it raises the maintenance value to a higher trajectory. It can be concluded that a maintenance strategy based on actual usage rather than assumed usage will reduce over-maintenance, and is applicable when failure mechanisms are known and the loads on the component can be calculated. Increasing maintenance intervals can however cause safety issues since they affect the airworthiness of the component. This framework is therefore only used to validate the usage spectrum and showing the benefits of a more usage severity driven maintenance strategy. Implementation of the concept should be accompanied by accurate validation of the calculated service lives.
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8. Jardine AKS, Lin D, Banjevic D (2006) A Review on Machinery Diagnostics and Prognostic Implementing Condition-Based Monitoring. Mechanical Systems and Signal Processing. 1483-1510. 9. Wang W (2010) A Model to Determine the Optimal Critical Level and the Monitoring Intervals in Condition-Based Maintenance. International Journal of Production Research. 14251436. 10. Curran R, Abu-Kian T, Repko MJF et al (2010) A Value Operations Methodology for Value Driven Design: Medium Range Passenger Airliner Validation. In: Proceedings of AIAA Annual Society Meeting. 11. Saaty TL (2003) How to Make a Decision: The Analytical Hierarchy Process. European Journal of Operations Research. 48:9-26. 12. Adamides ED, Stamboulis YA, Varelis AG (2004) Model-Based Assessment of Military Aircraft Engine Maintenance Systems. Journal of Operations Research Society. 957-967. 13. Banks JC, Reichard KM, Hines JA et al (2010) Platform Degrader Analysis for the Design and Development of Vehicle Health Management Systems. The Journal of the Reliability Information Analysis Center. 18-21. 14. Palmgren AG (1924) Die Lebensdauer von Kugellagern. Zeitschrift des Vereines Deutscher Ingenieure, 68: 339–341. 15. ASTM (1999) Standard Practices for Cycle Counting in Fatigue Analysis. Techncial Report ASTM E 1049-85. 16. Franke L, Dierkes G (1999) A Non-Linear Fatigue Damage Rule with an Exponent Based on a Crack Growth Boundary Condition. International Journal of Fatigue. 21:761-767. 17. Sonsino CM (2007) Course of SN-curves Especially in the High-Cycle Fatigue Regime with Regard to Component Design and Safety. International Journal of Fatigue. 29:2246-2258. 18. Robinson EL (1952) Effect of Temperature Variation on the Long-Time Rupture Strength of Steels. Trans ASME. 74:777-781. 19. Lewis EE (1994) Introduction to Reliability Engineering. John Wiley and Sons, New York. 20. Kaplan EL. Meier P (1958) Nonparametric estimation from incomplete observations. J. Amer. Statist. Assn. 53:457-481. 21. Montgomery JE, Clark JM (1962) Dust Erosion Parameters for a Gas Turbine. In: SAE Summer Meeting.
