Differential Degradation Assessment of Helicopter Engines Operated in Marine Environment

The helicopters used for marine operations encounter harsh environment laden with salt mist, sand and dust which could accelerate the deterioration of components. Assessment of the effect of operational environment on component degradation of such helicopter engines is crucial in scheduling their maintenance and ensuring flight safety. The objective of this study is to understand and assess the differential degradation pattern of aeroengines operated in marine environment in comparison to their counterparts operated in non-marine environment. In this study, a sample of 257 ex-service aeroengines of same type and make, operated in marine and non-marine environment were randomly selected and their degradation pattern observed. After obtaining the data on component degradation, further statistical analysis was carried out and the statistical significance of the observations were computed. Out of the ten major components considered in this study, five of them were found to have statistically significant differential degradation due to operation in marine environment. For the remaining components adequate evidence was not available to substantiate differential degradation due to operation in marine environment. These findings serve as valuable input for maintenance inventory planning as well as component improvement programme.

Helicopters and small aircraft are extensively employed for commuting between sea bound installations, coastal patrolling and for Naval aviation. A significant portion of the operational cost in aviation activity is attributed to engine maintenance expenditure1,2. And hence assessment of the effect of marine environment on these aero engines is crucial in deciding their longevity and planning operational support. In marine environment, the sea winds carry several kilograms of salt per cubic kilometer of air which promotes corrosion in the engine components. Although the engine components are designed to withstand given stresses and to provide a specified factor of safety, erosion and corrosion changes the mechanical characteristics of the materials thus reducing the margin of safety and endangering the flight leading to aircraft accidents3,4. Unless the corrosion is detected and treated, it becomes a serious problem that hampers the flight safety of the aircraft5-7.Persistence of such issues despite extensive precautions taken by the designers and users indicate the need for further research. The menace of marine corrosion on aeroengines is far wider than the generally described aqueous corrosion observed in marine applications. It includes atmospheric corrosion of metals exposed in the marine environment and hot salt corrosion in engines operating at sea or taking in salt-laden air8-11. Further, the mechanism of corrosion that occurs on control equipment may be similar to that which occurs on the basic structure but a small amount of corrosion on avionics equipment can cause serious degradation or complete system failure. Thus the amount of corrosion that is detrimental varies from component to component and the corrosion tolerance of each component is an important factor while considering the overall system safety.

As mentioned above, this study has been motivated by the fleet maintenance issues encountered during the operation of aeroengines in marine environment. The information on the differential degradation of engines exposed to marine environment serves as an important input for maintenance inventory planning. Hence a field study has been carried out on a large fleet of helicopter engines operated in temporal climatic region with a portion of the population exposed to marine environment. Based on the physical observations of these engines, a few components were identified for the detailed study regarding their differential degradation due to operation in marine environment. Data on the degradation level of these components were recorded for the entire sample of engines studied and the components were classified on the basis of their tolerance to marine environment. The field data collected during this exercise corresponds to approximately 3,00,000 h of cumulative flying spread over several calendar years. This prolonged duration of study involving a large sample of engines help to gather the aggregate average system response and to control the effect of any inadvertent heterogeneities in degradation arising out of seasonal variations, operational mission profiles, isolated maintenance issues, local climatic variations, etc.

Though the accelerated degradation of the engines operated in marine environment is intuitively understandable, no systematic study has been reported on how the degradations in aeroengines operated in marine environment differs from their non-marine counterparts. One can observe a few general studies10,12-17 reported in the literature on the performance degradation of aero gas turbine engines due to various environmental effects. The most prominent among them is AGARD-CP-55817 which discusses the sand erosion issues encountered during the military operation in the deserts of Gulf region. Similar studies have been reported by Narayanamurthy18, et al. regarding the component deterioration of helicopter engines due to environmental effect mainly due to the sand erosion during desert operation by the Indian Armed Forces. These papers on the environmental effect on engine deterioration generally focuses on the material degradation mechanisms or atmost limit their scope of study to sub-system level such as compressor fouling and its effects due to sand ingestion during desert operation or flying in the vicinity of volcanic eruption16,18. Majority of them with the exception of a few19,20 do not distinguish between marine and non-marine environment while discussing the engine deterioration. Airworthiness authorities often intuitively assume the severity of marine environment and limit the operational life of aeroengines operated in marine environment at a level much lower than their non-marine counterparts. The published literature is sparse on this aspect and to the best of the author’s knowledge no article has been published on the comparative evaluation of the aeroengine degradation due to the operation in the marine and non-marine environments. This article fulfills the above literature gap and provides a methodology for comparative assessment of the degradation of aeroengines operated in marine environment vis-a-vis non-marine environment.

An empirical field study has been chosen for analysing the differential degradation of aeroengines operated in marine environment. In addition to the operational environment, the extent of damage observed on the engine components depends on many factors such as material used, coatings applied, presence of degradation accelerating factors such as stress concentration, high temperature prevailing inside the aeroengine, presence of combustion products etc. In order to assess the marginal effect of marine environment, one needs to control all other influencing variables through appropriate research methodology. Therefore in the current study, instead of focusing on the ‘physics of failure’, a comparative evaluation of the engines operated in marine environment has been carried out with reference to those engines of same make and model having identical performance characteristics but operated in non-marine environment. The overall plan of research includes selection of a random sample of engines and classifying them into two mutually exclusive groups based on their operating environment for subsequent assessment of the degradation in its constituent components as shown in Fig. 1.

Figure 1. The scheme of the proposed research.

During this research, a large sample of aeroengines were selected and their degradation patterns observed after a predetermined duration of operation, i.e., each engine included in the sample needs to complete a minimum period of operation in the given environment so that the environmental effect if any could be visible on its components. This minimum operational duration specified as 1200 h in this study corresponds to the authorized time between overhauls of the given engine.

In summary, the formal research approach identifies the degradation noticed on critical components of the aeroengine measured on a categorical scale as the variable and the operational environment as a factor. This leads to classification of the sample of engines into two mutually exclusive sets on the basis of their operational environment thus formulating the investigation as a typical two sample problem for differential comparison. Thus the research problem boils down to comparing the proportions of degradations in the marine sample with those of the non-marine sample as described in the following subsections.

2.1 Sampling Scheme and the Data

The sample consists of helicopter engines drawn from various operating bases that includes marine as well as non-marine locations. Since it is extremely difficult to implement a probabilistic random sampling design in the actual military operational scenario, a non-probabilistic sampling design is adopted on an adhoc basis and subsequently the sample randomness has been verified using probabilistic methods as follows. To provide the statistical justification of the randomness of the sample selected, a runs test based on the theory of runs21 has been used. The identification number of the engine has been taken as the base reference and a run is defined based on the succession of the unit serial number in the order in which it has been included in the sample. The central idea is to ensure that there are no definite groupings or alternating patterns in the selection of engines as evidenced by too few runs or too many runs. Let n1 be the occurrences of type 1 sequences and n2 be the number of occurrences of type 2 sequences and r indicate the number of runs. Based on the sampling distributions of r, the mean µr and the standard deviation σr are given by (vide Eqns. 15.8 and 15.9 of Canavos22)

${\mu }_{r}=\frac{2{n}_{1}{n}_{2}}{\left({n}_{1}+{n}_{2}\right)}+1$         (1)

${\sigma }_{r}=\sqrt{2{n}_{1}{n}_{2}\frac{2{n}_{1}{n}_{2}-{n}_{1}-{n}_{2}}{{\left({n}_{1}+{n}_{2}\right)}^{2}\left({n}_{1}+{n}_{2}-1\right)}}$          (2)

Now for testing the null hypothesis concerning the randomness of the field data, a two tailed test would be appropriate as too many or too few runs would indicate that the process of sample selection is not random. Also it is well known that for a sufficiently large number (say > 10) of n1 and n2 , the sampling distribution of r is closely approximated by Normal distribution. Therefore, based on the z- value for a significance level of 0.1, one can test the hypothesis on the randomness of the given sample. Once a random sample of engines is available, one needs to formulate the theoretical frame work for the differential degradation analysis. Even before that, one has to have clarity on the data needed and how to extract the data. The data extraction is proposed as per the scheme shown in Figure 2 for the entire sample of engines studied. Let each engine included in the sample fall into the Group {M} or {N} where {M} includes the engines operated in marine environment and {N} includes the engines operated in non-marine environment. Based on the operational practice, there is no exchange of engines between marine and non-marine operations and hence {M} and {N} are mutually exclusive.

Figure 2.The data extraction scheme.

2.2 The Theoretical Framework

Suppose the data on the degradation observed on the major components of a large number of engines is available, the next step is to evaluate the proportion of degradations in each sample. ie., for a given component, one is interested in comparing the proportions of degradations evident in the marine engines with that of non-marine engines. Let ${P}_{{M}_{J}}$ be the proportion of marine operated engines that had undergone degradation in j-th component and ${P}_{{N}_{J}}$ be the proportions of non-marine engines that had degradation in j-th component. Let NM and NN denote the sample sizes from marine and non-marine category respectively and let Xj and Yj be the observed number of degradations corresponding to marine and non-marine engines for a given part j. The sample proportions $\stackrel{̂}{{P}_{{M}_{j}}}={X}_{j}/{N}_{M}$ and $\stackrel{̂}{{P}_{{N}_{j}}}={Y}_{j}/{N}_{N}$ are the maximum likelihood estimators of ${P}_{{M}_{J}}$ and ${P}_{{N}_{J}}$ respectively. Once the sample proportions are known, the differential degradation of j-th component ${\Delta }_{j}$ which is defined as the difference in proportion of degradation between the marine and non-marine engines with respect to j-th component can be obtained as

${\Delta }_{j}={P}_{{M}_{J}}-{P}_{{N}_{J}}$                          (3)

Further to check whether the marine operations cause a differential degradation to part j, the following hypothesis testing is proposed.${H}_{o}:{P}_{{M}_{J}}={P}_{{N}_{J}}$ or ${P}_{{M}_{J}}-{P}_{{N}_{J}}=0$ against the alternative
${H}_{a}:{P}_{{M}_{J}}\ne {P}_{{N}_{J}}$
In order to test the above hypothesis, let us consider the statistic $\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}$ . Since by the assumption, Xj and Yj are binominal random variables, the variance estimators are given by

$Var\left(\stackrel{̂}{{P}_{{M}_{j}}}\right)=Var\left(\frac{{X}_{j}}{{N}_{M}}\right)=\frac{\stackrel{̂}{{P}_{{M}_{j}}}\left(1-\stackrel{̂}{{P}_{{M}_{j}}}\right)}{{N}_{M}}$         (4)

$\text{Var}\left(\stackrel{̂}{{P}_{{N}_{j}}}\right)=\text{Var}\left(\frac{{Y}_{j}}{{N}_{N}}\right)=\frac{\stackrel{̂}{{P}_{{N}_{j}}}\left(1-\stackrel{̂}{{P}_{{N}_{j}}}\right)}{{N}_{N}}$          (5)

Under Ho, the two proportions are assumed to be equal. ie., let the common proportions be ${P}_{{M}_{j}}={P}_{{N}_{j}}={P}_{{O}_{j}}$ where ${P}_{{O}_{j}}$ is the pooled estimate of the proportion of degradations. Then, if null hypothesis is true, for sufficiently large number of samples, the statistic $\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}$ is approximately normally distributed with mean

$E\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)=0$      (6)

and Variance,

$Var\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)=\frac{{P}_{{O}_{j}}{\left(1-{P}_{{O}_{j}}\right)}_{}}{{N}_{M}}+\frac{{P}_{{O}_{j}}{\left(1-{P}_{{O}_{j}}\right)}_{}}{{N}_{N}}$       (7)

Therefore the standard deviation (SD) follows as,
$SD\left(\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}\right)=\sqrt{{P}_{{O}_{j}}{\left(1-{P}_{{O}_{j}}\right)}_{}\left(\frac{1}{{N}_{M}}+\frac{1}{{N}_{N}}\right)}$         (8)

Since ${P}_{{O}_{j}}$ is unknown, let us try to get the best estimator of ${P}_{{O}_{j}}$ as $\stackrel{̂}{{P}_{{O}_{j}}}$ by pooling the information from both samples from the marine and non-marine operating environment to determine the pooled estimates, i.e.,

$\stackrel{̂}{{P}_{{O}_{j}}}=\frac{{X}_{j}+{Y}_{j}}{{N}_{M}+{N}_{N}}$         (9)

where Xj and Yj are the number of degradations in sample of marine and non-marine engines respectively for the j-th component. Therefore standard deviation

$SD\left(\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}\right)=\sqrt{\stackrel{̂}{{P}_{{O}_{j}}}{\left(1-\stackrel{̂}{{P}_{{O}_{j}}}\right)}_{}\left(\frac{1}{{N}_{M}}+\frac{1}{{N}_{N}}\right)}$         (10)

Under Ho, for large NM and NN, the distribution of the static $\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}$ is approximately normal, i.e.,

$\frac{\left(\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}\right)}{\text{SD}\left(\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}\right)}\text{\hspace{0.17em}}\sim N\left(0,1\right)$ therefore,

$z=\frac{\stackrel{̂}{{P}_{{M}_{j}}}–\stackrel{̂}{{P}_{{N}_{j}}}}{\sqrt{\stackrel{̂}{{P}_{{O}_{j}}}{\left(1-\stackrel{̂}{{P}_{{O}_{j}}}\right)}_{}\left(\frac{1}{{N}_{M}}+\frac{1}{{N}_{N}}\right)}}$       (11)

and the p-value is given by

$P\left(z>{z}_{obs}\right)=1-P\left(z\le {z}_{obs}\right)$       (12)

When the p-value is high, we have no adequate evidence to reject the null, i.e., As far as Ho is concerned, the analyst does not have adequate evidence to say that the degradation of this component is affected solely by the marine operations. On the corollary; we can infer that marine environment operations has no significant effect on the life of the component in comparison with operations in non-marine environment.

One might further be interested in examining the confidence interval (CI) for the differential degradation. The statistic of interest is the difference between the two sample proportions $\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)$ . Since $E\left(\stackrel{̂}{{P}_{{M}_{j}}}\right)={P}_{{M}_{j}}$ and $E\left(\stackrel{̂}{{P}_{{N}_{j}}}\right)={P}_{{N}_{j}}$ , it could be shown that $E\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)={P}_{{M}_{j}}-{P}_{{N}_{j}}$ and $Var\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)=Var\left({P}_{{M}_{j}}\right)+Var\left({P}_{{N}_{j}}\right)$ . It implies that

$Var\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)=\frac{{P}_{{M}_{j}}{\left(1-{P}_{{M}_{j}}\right)}_{}}{{N}_{M}}+\frac{{P}_{{N}_{j}}{\left(1-{P}_{{N}_{j}}\right)}_{}}{{N}_{N}}$       (13)

For sufficiently large number of samples, the distribution of the statistic $\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)$ is approximately normal with mean and variance as shown above. Then the distribution of z is obtained as follows.

$z=\frac{\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)-\left({P}_{{M}_{j}}-{P}_{{N}_{j}}\right)}{\sqrt{\frac{{P}_{{M}_{j}}{\left(1-{P}_{{M}_{j}}\right)}_{}}{{N}_{M}}+\frac{{P}_{{N}_{j}}{\left(1-{P}_{{N}_{j}}\right)}_{}}{{N}_{N}}}}$       (14)

Therefore the probability of the random interval $\stackrel{̂}{\left({P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)±{\Phi }_{1-\alpha /2}^{-1}SD\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)=\left(1-\alpha \right)$ where $\Phi \left(.\right)$ is the standard Normal cumulative density function and hence the $100\left(1-\alpha \right)%$ confidence interval of the differential degradation of the j-th component due to operation in marine environment is given by

$\left(\stackrel{̂}{{P}_{{M}_{j}}}-\stackrel{̂}{{P}_{{N}_{j}}}\right)±{\Phi }_{1-\alpha /2}^{-1}×\sqrt{\frac{{P}_{{M}_{j}}\left(1-{P}_{{M}_{j}}\right)}{{N}_{M}}+\frac{{P}_{{N}_{j}}\left(1-{P}_{{N}_{j}}\right)}{{N}_{N}}}$          (15)

As described above, one can obtain the sample proportions and other statistical properties of the differential degradation of the engines operated in the marine environment. These quantities provide the theoretical basis for the differential degradation assessment of engines presented in this article.

Now let us move on to the assessment of differential degradations of a fleet of helicopter engines based on the field observations. It covers a large fleet of engines out of which a portion is exclusively used for marine aviation and hence provides an opportunity to study the effect of marine environment on the degradation of engine components. For large samples of data collected over a period of time, one need not be very pessimistic on the randomness of the sample due to the non-probabilistic sampling technique adopted. The total number of engines and the environment wise break-up are given in Table 1. Here the sample sizes of both marine operated and non-marine operated engines are large enough to approximate the sampling distribution of the ‘number of runs’ (r) by Normal distribution. The number of runs is counted based on the order in which the engines are withdrawn from service and the run test result with p-value = 0.36249 indicate that there is no evidence against the randomness of the selected sample.

Table 1. Number of engines sampled.

While the randomness of the sample selection has been ensured using statistical test, the identicalness of the units considered in the sample is ensured by means of engineering analysis. Due to their similarity in both construction as well as functional characteristics, the assumption of identical systems forming a homogeneous sample is implicitly tenable. The engineering details such as the standard of preparation, constructional aspects, embodiment of modifications etc. of each engine included in the sample have been verified from the records available with the engine manufacturer.

On observing the physical condition of the ex-service components, the criteria for assessing the degradation has been adopted from the engine manufacturer as indicated in the overhaul manual. Domain expertise has been extensively involved in the above decision process. Based on the physical observations made during the pilot study, ten components have been identified for further investigation. Let us denote these components as C1, C2 , …., C10 without getting into the engineering details. Let the degradation level of each ex-service component be indicated in a nominal scale as follows. If the component is physically degraded (component rejected in industrial terms prescribed by the engine manufacturer) denote the observation as 1’ or else denote it as 0’. A snapshot of the data base generated on this basis is shown in Table 2.

Table 2. A snapshot of the database.

The physical condition of components C1, C2 , …., C10 have been observed for all the ex-service engines included in the study. Different components exhibited varying levels of degradation after prolonged exposure to the operating environment. A summary of the number of degradations observed in each component is presented in Table 3. One important feature observed is the high number of degradations for some engine components (eg. component C4) irrespective of the operating environment. If one looks at the marine operated engines alone, it may lead to wrong conclusion. For instance, in 42 cases out of 48 marine engines, component C4 has been degraded giving a prima facie impression that degradation of component C4 is due to its exposure to marine environment. However if one closely examines the case of engines operated in non-marine environment, component C4 has the maximum number of degradations (163 cases out of 209 engines) and one may not find any statistical support for the argument for differential degradation on component C4. Similarly for component C5, degradation during non-marine operation seems to be more severe than the marine operation.

Table 3. Number of degradations observed in each component C1, C2 , …., C10.

Having obtained the data on the differential degradation due to the operational environment, further statistical analysis has been carried out. The point estimates of the proportion of degradation in different environments, the point and interval estimates of the differential degradation and the Statistical significance of the observations were computed and tabulated as shown in Table 4. Out of the ten major components considered in this study, five of them were found to have statistically significant differential degradation due to operation in marine environment. For the remaining components adequate evidence is not available to substantiate differential degradation due to operation in marine environment.

Table 4. Significance (p-value) of the differential degradation ($\Delta$ j) noticed on different components .

Motivated by the maintenance issues of helicopter engines that are used in marine as well as non-marine environments, a systematic study on the differential degradation assessment of aeroengine has been carried out as illustrated in this article. The severity of marine environment is often taken as a basic premise and many airworthiness engineers believe that the aeroengines operated in marine environment have very limited life potential compared to engines operated in non-marine environment. This intuitive assumption needs a formal verification and hence a hypothesis test has been proposed. Under the null hypothesis it is assumed that $\Delta$ j =0. It implies that under null, the marine operating environment has no marginal effect on the component deterioration. The above hypothesis has been tested based on the data collected from a random sample of engines to confirm the differential degradation if any. Statistically significant differential degradation due to operation in marine environment has been observed in five components C2, C6, C8,C9 and C10 with p-values 2.58141 × 10-06 , 7.17953 × 10-36, 1.09585 × 10-04, 2.70403 × 10-14 and 3.46773 × 10-04 respectively as shown in Table 4. However, for components C1, C3, C4 and C7 the p- values obtained are 0.38492, 0.34456, 0.32181 and 0.39008 respectively indicating that there is no adequate evidence available to prove the differential degradation due to marine environment. However for component C5 non-marine degradation seems to be more predominant than degradation induced by marine environment. Figure 3 depicts the 95% confidence intervals of the differential degradation for the components C1, C2, …., C10 in a lucid manner and provides a bird’s eye view of the results obtained.

Figure 3. 95% Confidence interval for differential degradation of components C1, C2 , …., C10.

This study reveals varying response of different components to the differential degradation while operating in marine environment. Marine environment surely affects the degradation of certain components, but this statement needs to be interpreted carefully. As revealed in this study, operation in marine environment cannot be solely responsible for the component degradation as many components degraded during non-marine operation as well. This probably negates the conventional wisdom of imposing arbitrary life restrictions on marine engines under the pretext of harsh marine environment.

This result opens up a large area of research in life management of critical components of complex systems operated in marine environment. As mentioned above, this study emphasizes the need for detailed material analysis and institution of degradation mitigation plans leading to judicious life management of aeroengines. Some components are susceptible for higher rate of degradation in marine environment such as C2, C6, C8, C9 and C10 and these may be identified for further study under component improvement programme. Similarly component C5 needs some re-look from the engineering perspective related to the non-marine operation, probably issues related to operation at high altitudes or hot desert conditions and so on. The remaining components C1, C3, C4 and C7 have degradation as a result of operational exploitation but one cannot attribute it on the harshness of marine environment. These components are affected by some degradation mechanisms prevailing both at marine and non-marine environments. These aspects offer new venues of research and can be explored further as an extension to this work.

Further, the component rejections during engine overhaul is directly linked to the extent of degradation and thus the outcome of this study provides an essential input for maintenance planning and inventory management. The demand rate of these high value inventory and their differential consumption pattern for marine vis-a-vis non marine application is an indispensable information for the maintenance-repair-overhaul (MRO) supply chain management.

An assessment of the differential degradation of a typical helicopter engine operated in marine environment has been carried out. While discussing the practical issues, methodological issues were also addressed. Data analysis revealed that five out of the ten selected components of the given aeroengine do exhibit statistically significant differential degradation due to operation in marine environment. This study brings out the need for systematic assessment of the environmental degradation on critical components of complex systems. Through the analysis of operational feedback collected from a large fleet of engines, this article highlights the methodology to assess equipment degradation due to operation in marine environment through a field survey.

The author wishes to thank Dr K. Tamilmani, DS & Chief Executive, CEMILAC for granting permission to publish this article. The technical association with Dr. Suresh Srivastava is acknowledged with thanks. The constructive criticism and the contribution by the referees are also greatly appreciated.

1.     Orupp. Maintenance cost forecast for civil aircraft gas turbine engines. In Proceedings of the XIV International Symposium on Air breathing Engines (ISABE), Florence, Italy, 1999.

2.     Singnori, B. Engine fleet management Alitalia experience on engine maintenance. In Proceedings of the XIV International Symposium on Air breathing Engines (ISABE), Florence, Italy, 1999.

3.     Campbell, G.S. & Lahey, R. A survey of serious aircraft accidents involving fatigue fracture. Int. J. Fatigue, 1984,6(1), 25-30.

4.     Esaklul, K.A.  Handbook of case histories in failure analysis. ASM International, USA, 1993, Vol. 2.

5.     Cole, I.S. & Paterson, D.A. Modeling aerosol deposition rates on aircraft and implications for pollutant accumulation and corrosion. Corros. Eng. Sci. Technol., 2009,44(5), 332-39.

6.     Steensma, D.K. Audit report on U.S. navy aircraft corrosion prevention and control program. US Navy Report No.97-181, 1997.

7.     James, M.N. Crashing aircraft, sinking ships - fractographic and SEM support for unusual failure hypothesis. Eng. Fail. Anal., 2002, 9(3), 313-28.

8.     Knight, S.P.; Salagaras, M. & Trueman, A.R. The study of intergranular corrosion in aircraft aluminum alloys using X-ray tomography. Corrosion Science, 2011, 53(2), 727-34.

9.     Richardson, Tony J.A. Ed., Shreir’s corrosion. Vol. 4. Elsevier, 2010, 3175-97.

10.   Carter, T.J. Common failures in gas turbine blades. Eng. Fail. Anal., 2005, 12(2), 237-47.

11.   Ren, X.; Wang, F. & Wang, X. High temperature oxidation and hot corrosion behavior of the NiCr-CrAl coating on a nickel based super alloy. Surf. Coat. Technol., 2005, 198(1-3), 425-31.

12.   Turan, D. & Karci, A. Failure analysis of aircraft piston engine components. Eng. Fail. Anal., 2009, 16(4), 1339-45.

13.   Scala, S.M.; Konrad, M. & Mason, R.B. Predicting the performance of a gas turbine engine undergoing compressor blade erosion. In Proceedings of 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA 2003-5259, Huntsville, Alabama, 2003.

14.   Sirs, R.C. The operation of gas turbine engines in hot and sandy condition. In Proceedings of the Conference of the Advisory Group for Aerospace Research and Development, AGARD-CP-558, Rotterdam, Netherland, 25-28April, 1994.

15.   Edmard, V.R. & Rouse, P.L. U.S. Army rotorcraft turboshaft engines with environmental ingestion effects. In Proceedings of the Conference of the Advisory Group for Aerospace Research and Development- AGARD-CP-558, Rotterdam, Netherland, 25-28April, 1994.

16.   Webley, P.W. Virtual globe visualisation of ash-aviation encounters with the special case of the 1989 Redoubt-KLM incident. Computers Geosciences, 2011, 37(1), 25-37.

17.   AGARD CP 558: Erosion corrosion and foreign object damage effects on gas turbines. In the Proceedings of the Conference of the Advisory Group for Aerospace Research and Development. Rotterdam, Netherland, 25-28 April, 1994.

18.   Narayanamurthy, R.V.; Unnikrishnan, V.; Amarnath,  B.G. & Srinivasa, K. Erosion problem of axial compressor blades of a turboshaft engine. In Proceedings of the VIII International Symposium on Air breathing Engines (ISABE), Cincinnati, Ohio,USA,1987.

19.   Samuel, M.P.; Rao, V.S. & Unnikrishnan, V.  Problems associated with operation of aeroengines in marine environment. In Proceedings of NRB Seminar on Naval Materials: Present and Futuristic Trends, NMRL, Mumbai, India, 2000.

19.   Hoffman, M.E. & Hoffman, P.C. Corrosion and fatigue research-structural issues and relevance to naval aviation. Int. J. Fatigue, 2001, 23(1), 1-10.

20.   Mood, A.M. The distribution theory of runs. Ann. Math. Stat., 1940, 11, 367-92.

21.   Canavos, G.C. Applied probability and statistical methods. Little Brown & Co. Ltd., USA, 1984.

 Dr Mathews P. Samuel Dr Mathews P. Samuel received BTech in the 1993 and MS (Mechanical Engg) from IIT, Madras in 1996. In 2010 he got PhD from Indian Institute of Science (IISc), Bangalore. Currently he is a scientist at RCMA(Engines), Center for Military Airworthiness and Certification, Bangalore, India.