The Dynamics of Aircraft Degradation and Mechanical Failure
LEONARD MACLEAN 1,
* ALEX RICHMAN 2 STIG
LARSSON 3 VINCENT
RICHMAN 4
ABSTRACT
This paper looks at the predictability of system failures of
aging aircraft. We present a stochastic, dynamic model for the
trajectory of the operating condition with use. With failure defined
as the operating condition below a critical level, the dynamics of
the number of failures with accumulated use is developed. The
important factors in the prediction of mechanical failures are the
number of previous repairs and the time since last repair. Those
factors are related to repair procedures, with the time of repair
and the extent of repair (fraction of good-as-new) being variables
under the control of the operator. The methodology is then applied
to data on non-accident mechanical failures affecting safety that
result in unscheduled landings.
KEYWORDS: Aircraft failures, aircraft degradation and
repair, airline schedule reliability.
INTRODUCTION
An aircraft is a complex machine composed of many interrelated
parts, components, and systems. Electrical and mechanical systems
are designed with an expected life length, where length refers to
time units (hours) of use. As the aircraft and systems age and their
use accumulates, they gradually degenerate until they are no longer
able to perform the functions for which they were designed; that is,
the system is in a failed state.
A nonfunctional part, component, or system can be upgraded
through replacement or repair, in which case the condition of the
aircraft is restored to some degree. Maintenance can be based on the
condition; that is, items are repaired when they fail. However,
failure during operation can have serious consequences, so detection
of items with a high probability of failure through periodic
inspection becomes a major component of maintenance.
The failure rate (the probability of failure at a point in time)
for a degenerating system increases with use and age. Figure
1 depicts alternative patterns of failure rates for an aircraft
that undergoes periodic maintenance (a similar figure appears in
Lincoln 2000). In case A, the aircraft has an increasing failure
rate with age and reaches an acceptability threshold, at which point
the aircraft would need to be replaced. The failure rate declines
with periodic maintenance, but the improvement through maintenance
diminishes over time. The threshold is not reached in case B, likely
because of increased effort and cost put into maintaining the
aircraft.
The cost of maintenance required to keep aircraft airworthy
(below the threshold) is a major concern of operators. Although
replacement time was set by manufacturers at 20 years for many
aircraft models, this life length was extended by operators. An
assumption has been made that aircraft operating condition can be
kept at an acceptable level beyond the intended life through
maintenance, but costs are high.
In 1997, 46% of U.S. commercial aircraft were over 17 years of
age and 28% were over 20 years. In 2001, 31% of the U.S. commercial
fleet were over 15 years of age, and those aircraft accounted for
66% of the total cost of maintenance per block hour.1
Although aging (the degeneration in operating condition with
accumulated use) inevitably occurs, it is modified by a number of
factors: quantity and quality of repair work; intensity of use;
deferral of the schedule for planned maintenance; and the
environment (Alfred et al. 1997). It should be noted, however,
unscheduled maintenance accounts for up to 60% of the overall
maintenance workload (Phelan 2003).
In addition to the possibility of maintaining an airworthy
operating condition, other reasons exist for not retiring an
aircraft from the fleet: the high cost of new aircraft; the increase
in demand requiring an expanded fleet; and an earlier shortage of
production capacity for new planes (Friend 1992). These and other
factors result in a large number of aircraft in use beyond their
planned retirement. A claim could be made that commercial aircraft
are being strained to perform well beyond their intended operating
life. Of course, if this is true we would expect that either the
rate of aircraft failure would show a corresponding increase or the
operating hours per aircraft would decrease because planes would be
out of service for repairs more frequently.
In the United States, Service Difficulty Reports (SDRs) contain
records of the safety problems an aircraft experiences during
operation. This database, maintained by the Federal Aviation
Administration, is considered a potential source of important
information on aircraft failures (Sampath 2000). A study comparing
failure rates by carrier identified significant factors that explain
the differences in the rate of SDRs across carriers (Kanafani et al.
1993). Because accumulated aircraft use (age) was not included in
that study, degradation with age and differences over time in the
safety of individual aircraft were not considered.
A THEORY OF DEGRADATION AND REPAIR
With age and accumulated use, the many interrelated parts and
components in an aircraft can be assumed to degrade. The operating
condition or airworthiness of the aircraft is based on the status of
individual parts, components, and systems, with the items that are
most degenerated being the main determinants. A certain level of
degeneration implies failure; that is, the item is no longer
operational. As well, failure of certain components or combinations
of components may render the aircraft not airworthy, which means the
aircraft is in a failed state. To address the failure of operating
systems, airline management undertakes a program of maintenance,
with scheduled preventive maintenance and unscheduled
repair/replacement of failed parts, components, and systems. This
section presents a conceptual model for the degradation and repair
of aircraft. The model provides a foundation for hypotheses about
operations that can be tested with field data.
Degradation
To characterize the degradation process, consider that the
operating condition of an aircraft is captured by an unobserved
health status index. The value of the index is derived from the
condition of the various parts, components, and systems in the
aircraft. Let t be the age of an aircraft, defined by the
accumulated hours of use, and let
Y(t) = the health status of an aircraft at age
t.
The status is a dynamic stochastic process, with the change in
status at any age being a random variable. Assume that the average
condition declines with age, but at any point variation in the
status, based on environmental factors and operating
characteristics, can occur. The dynamics of degradation at a point
in time can be represented by a stochastic differential equation as
d Y (t ) = μ t d t +
σ t d Z
t (1)
where μt < 0 is the degradation
rate, σt > 0 is a scaling factor,
and dZt is an independent random process.
For example, if the random process is white noise, the stochastic
differential equation defines a Wiener process, and the distribution
of the health status at a given age is Gaussian (Aven and Jensen
1998). So, with starting state y0 and constant
parameters μ and σ, the distribution of status after
t time periods is Normal,
Y (t) ∝ N (y
0 + μ t , t σ
2) (2)
Mechanical Failure
In this degradation framework, at any age (hours of use) the
possibility exists that the status of a item during operation will
drop below the critical level for functionality and the component
reaches a failure state. Degradation and failure of components lower
the value of the health status index Y. In particular,
failure of parts and components included on a minimum equipment list
(MEL) indicates the aircraft remains airworthy. Beyond the MEL,
moderate mechanical failures that occur during aircraft operation
would render the aircraft not airworthy. Assume that the critical
health status level y* defines airworthiness. Then an
aircraft failure occurs when Y (t ) <
y*.
Based on the stochastic model, the many parts, components, and
systems have a probability of failure during operation and,
therefore, the aircraft has a probability of failure. For an
airworthy aircraft, the important variable is the time to failure.
Let T be the length of life (hours of use before failure) of
an aircraft, with the probability distribution F (t )
= Pr (T ≤ t ), and the corresponding density
f (t ). Then
is the failure rate at time t (Aven and Jensen 1998). The
failure time distribution is determined by the failure rate
because
In the example, where the state dynamics are defined by a Wiener
process, the failure time is inverse Gaussian with density
. (3)
Repair
Failure during operation may precipitate unscheduled maintenance,
particularly when items beyond the MEL fail and consequently the
aircraft is not airworthy. The repair/replacement of failed items is
called on-condition repair. On-condition repair brings the system
back to the operating status expected of the system given its age,
that is, the same status as just prior to failure. With these
minimal repairs (Block et al. 1985), the aircraft failure
rate is unchanged since other parts, components, and systems are
still in the degraded state attained just before repair. Typically,
moderate mechanical failures result in such minimal repair.
In addition to unscheduled maintenance, the whole system is
subject to time-based or block repair, where items are
inspected and replaced/refurbished before failure. This scheduled
preventive maintenance improves the operating condition to a status
greater than expected for its age and correspondingly reduces the
system failure rate (Brown and Proschan 1983). To incorporate repair
into the degradation model, the age variable is partitioned into
intervals based on the block repair times. Assume that the first
scheduled block repair is at age (hours of use) τ, and
subsequent block repairs are at regular intervals of δ hours
of use, where δ ≤ τ. Then age t can be written
as
t = I τ + k δ +
r (4)
where I = 1 if t ≥
τ, I = 0 if t <
τ
if t ≥ τ, k = 0 if
t < τ and
r = t - I τ - k δ.
The notation]x[defines the greatest integer less than
x. Equation (4) gives the age in terms of (I +
k) = the number of repairs, and r = the use since the
last block repair. The intervention with a block repair improves the
health status of an aircraft above the level expected for its age.
Let the improvement level from a block repair at age t be up
to the line
y (t) = α + β
t, (5)
where α > y* and β ≤ 0.
The repair line is theoretical and the important parameter is
β, which describes the repair policy to return the aircraft
to a fraction of good-as-new at scheduled times. If β = 0,
then repair always brings the plane to the same health status
regardless of age.
To simplify the presentation, repair policies that are equivalent
in terms of the total repair effort will be considered. Let
L = the expected length of the operating life
of an aircraft.
Assume that all feasible repair policies have the same total
repair over the expected life of the aircraft. That is,
,
for some constant α*. With this condition, the repair
policy is determined by β, which also determines the
distribution of repair over the aircraft's lifetime.
The partition of age at block repairs generates renewal cycles
for the degradation process, with the first cycle starting at the
initial status y0, and subsequent cycles beginning
at the status defined by the repair line:
y j (τ, δ, β)
= α + β t j
, (6)
where t j = τ + (j - 1) δ,
j = 1, ., k. The repair policy is defined by:
τ- the hours of use until the first block repair; δ-
the hours of use between subsequent block repairs; and β- the
repair fraction. Using the definitions of
tj and yj, the
increase in the health status at each block repair from β can
be calculated. The policy determines the starting state and length
of renewal phases or cycles for the degeneration process. Figure
2 illustrates the cycles of degradation and repair for an
aircraft.
NUMBER OF FAILURES
In each renewal phase of the degradation model, there is a chance
that the aircraft fails; that is, its status drops below the
critical level y*. Let Tj = time to
critical condition y* in cycle j starting from
yj-1, j = 1,2,... . The failure time
distribution for Tj is written as
Fj(s | τ, δ, β), with density
fj(s | τ, δ, β), where the
repair policy (τ, δ, β) determines the starting status. Given
the failure time distribution, the failure rate in the jth
cycle is
. (7)
Consider
N(t) = the number of aircraft failures
to age t for repair policy (τ, δ,
β). (8)
Because
the expected number of failures is
. (9)
In the failure rate for each renewal phase, the probability
distribution for the time to failure has the same form, but the
starting state in each phase declines if β < 0. With
tj = τ + (j - 1)δ, and
the starting state in phase j + 1 as
,
define
ψ (x, y j) =
-ln (1 - F j + 1
(x)). (10)
Thus, ψ(x,yj) is the expected
number of failures between times 0 and x in phase j +
1, with failure time distribution Fj+1
and starting state yj. Then
. (11)
The degradation process and repair policy are determined by
parameter values, and those policies determine the properties of the
expected number of failures over time. Let ψ′x and
ψ′y denote first derivatives of ψ with
respect to x and y, respectively. Thus,
ψ′x is the change in expected failures with use
(degradation) within a phase, and ψ′y is the
change with respect to the phase starting state, determined by the
block repair policy. The following general results establish the
expectations for mechanical failures when the degeneration model
applies.
Proposition 1 (degeneration): If the health status of an
aircraft degenerates with use, then between block repairs, the
failure rate with use increases, as does the expected number of
failures in a fixed-width use interval.
In the dynamic model, degeneration follows from μ < 0.
With degradation, ψ′x = λ > 0, and
ψ′′x > 0, which implies an increasing failure
rate between repairs.
Proposition 2 (imperfect repair): If the block repair is
imperfect, then the failure rate with use since the last repair is
nondecreasing with the number of previous block repairs, and the
expected number of failures in a fixed (use since last repair)
interval is nondecreasing with the number of repairs. If the repair
fraction decreases over time, then the expected number of failures
is increasing.
In the model, ψ′y > 0. If α <
y0, β = 0, then the expected number of
failures in a fixed interval is constant after the initial block
repair. If β < 0, then the starting state y
decreases, with increasing failure rates in successive phases
between block repairs.
Proposition 3 (repair interval): If the imperfect repair
fraction is decreasing over time, then the expected number of
failures in a fixed-use interval increases/decreases as the block
repair interval increases/decreases.
Block repair increases the health status above that expected for
accumulated use, so more block repairs (shorter times between block
repairs) raise the expected value of y and decrease the
failure rate and number of failures.
FAILURE MODEL
The link between the latent state model for degradation/repair
and the model for the number of failures shows how the operating
practices of airlines can manifest themselves in mechanical
failures, safety problems, and unscheduled maintenance. Historical
data on failures and maintenance will have that complex relationship
embedded. The information on failures and block repairs is
available, but the degradation rate and extent of repair (fraction
of good-as-new) are unknown. However, from Proposition 1, the time
since the last block repair reflects degradation, and from
Proposition 2, the extent of the repair is directly related to the
number of block repairs. The transformation of equation (11) for the
expected number of failures to an expression in terms of the number
of block repairs and the time since the last block repair is
achieved by a series approximation to the function for
E(N(t)).
From the model, the average level of repair is α*.
Consider the first order approximation to ψ(δ, y)
around (δ, α*):
ψ (δ, y j) ≈
C0 (τ, δ, α*) +
C1 (τ, δ, α*) ×
j. (12)
In the last (incomplete) phase, a second order approximation to
the number of failures around (0,α*) is reasonable, assuming
the failure rate is increasing monotonically with use. Then
ψ (r, yk + 1) ≈
D0 (τ, δ, α*) +
D1 (τ, δ, α*) k
+ D2 (τ, δ,
α*) r
+ D3 (τ, δ,
α*) r
2. (13)
The coefficients in the approximating functions are defined by
derivatives of ψ, evaluated at (τ, δ, α*).
Substituting the approximations in equations (12) and (13) into
equation (11), the expected number of failures has the form
E (N (t)) ≈ B
0 + B 1 I + B
2 k + B 3 k
2
+ B 4 r + B
3 r
2. (14)
(Note that .)
A representation of the number of failures is shown in figure
3.
Thus, B1 is the expected number of failures in
the first phase. {B2, B3}
capture the expected number of failures in subsequent phases, and
{B4, B5} capture the expected
number in the last (incomplete) phase. The coefficients in the
expected number function that relate to the propositions are
B3 and B5. If the block repair
is imperfect, then B3 > 0. Figure
3 shows this effect with the failure function starting above the
origin at block repair times. An accelerated failure rate between
block repairs implies B5 > 0, which is shown
with a steeper slope in successive phases between repairs.
The approximating equation for the expected number of failures is
in a very suitable form for analysis. Consider an observation window
(interval) (t1,t2), where
t2 - t1 < δ. With
t1 = I1τ +
k1δ + r1, and
t2 = I2τ +
k2δ + r2, the number of
failures in the interval (t1,t2)
is approximately
η = E (N (t
1, t 2)) = E (N (t
2)) - E (N (t 1)),
so that
η = B 1 (I
2 - I 1) + B 2
(k 2 - k 1) + B 3
(k 22 - k
21)
+ B 4 (r 2 -
r 1) + B 5 (r
22 - r
21). (15)
This change model relates the number of failures in an
observation window to the degeneration and the block repairs in the
window. In equation (15), (I2 -
I1) = 1 if the first repair is in the interval,
and zero otherwise; (k2 - k1) =
1 if a later repair occurs in the window, and zero otherwise.
MODEL TESTING
We used data from AlgoPlus (2004) to test the failure model on
operating failures and AvSoft (2004) on aircraft use. For the
purposes of this study, an operating failure is defined as an
unscheduled landing due to mechanical problems affecting safety.
Thus, an unscheduled landing is a record of an operating condition
at or below a critical or intervention level. In figure 2, the
unscheduled landings (failures) occur when the health status drops
to the critical level, where airworthiness fails. It is also
possible that components fail and the event does not lead to an
unscheduled emergency landing. As mentioned earlier, a minimum
equipment list details which components may fail without the need
for an unscheduled landing. In terms of the degradation/repair
model, the critical condition line is below the condition for
failures on the MEL, so that reaching the critical line implies
unsafe operation and a need to interrupt the flight of an aircraft.
Data
The record of unscheduled landings over time provides an
information base for analyzing the degeneration in the operating
condition of an aircraft. The AlgoPlus data contain detailed records
on all unscheduled landings as reported in the Service Difficulty
Reports for all commercial aircraft in the United States. The AvSoft
data maintain records on departures and flying hours for all
commercial aircraft in North America. Both datasets have the serial
number, chronological age, model, and carrier/operator for each
aircraft.
An observation window from 1990 to 1995 inclusive was chosen, and
all aircraft operating during that time for three operators and two
models were selected for this study. For each aircraft, the
following information was recorded: 1) model; 2) operator; 3) age on
December 30, 1995; 4) use (block hours, cycles) by month from
January 1990 to December 1995; 5) dates out of service for at least
one month between 1990 and 1995; and 6) number of unscheduled
landings between 1990 and 1995. We interpreted the out-of-service
period in the observation window as a time when scheduled repair was
undertaken. The identification of these periods is within a record
of otherwise continuous use. Outside the observation window, the
block repair (preventive maintenance) cycle was set at 10 years for
the first block repair and 8 years for subsequent block repairs.
This is based on the recommendations for D-check cycles.2
Of course, in practice the time of block repairs would be variable
across aircraft and using a fixed value (outside the window) could
reduce the power of the fitted models.
Table
1 presents a brief description of the aircraft in the dataset.
For the aircraft in the study group, table 1 shows substantial
differences across models and operators in the age of aircraft as of
December 1995 and the number of unscheduled landings between January
1990 and December 1995.
The definition of age in the degradation of aircraft refers to
hours of use rather than chronological age. However, an aircraft
operator might make little distinction between airworthy aircraft of
varying ages when making decisions on use. To consider this point,
we looked at the relationship between flying hours per month and
chronological age in the data for the period 1990 to 1995. The
correlation in the data between monthly flying hours and age is
r = 0.07. The intensity of use appears almost constant across
age, indicating that aircraft are not being used less as they age.
With constant use per unit time, the accumulated hours of use are
almost a scalar multiple of chronological age. So, calendar time was
used in the model for predicting the number of failures; that is,
the time between block repairs and the time since the last repair
will be measured in calendar time rather than accumulated hours of
use.
Regression Model
The formulation of a change model for the number of failures
creates a framework suitable for observation and statistical
analysis. Based on the model in equation (15), consider the
regression model
, (16)
where
N(t1,t2) = the number
of failures between ages t1 and
t2,
X1 = the indicator for the first
τ-repair in the interval,
X2 = the indicator for the kth
δ-repair in the interval, k ≥ 1,
X3 = the difference between the squared number
of repairs, k 22 - k
21 ,
X4 = the difference in residual times,
r2 - r1,
X5 = the difference in squared residual times,
(r 22 - r
21), and
ε = the random error.
In the regression model, assume that the unscheduled landings and
item failures from degradation are directly related to the number of
block repairs and the time since the last block repair. There are
also other factors such as repair skill level, maintenance
philosophy, and operational environment involved in unscheduled
landings (Phelan 2003). We will assume that these other factors are
associated with the operator. As well, the aircraft model is a
factor in failure rates. So, the coefficients in the regression
model depend on the aircraft model and the aircraft operator. This
is reflected in the regression model with a superscript q on
the coefficients.
The coefficients in the regression model are counterparts of the
coefficients in the failure model, and appropriate tests
characterize the role of degradation and repair on failures for a
particular model and operator combination. Table
2 displays the relevant research hypotheses.
A comparison of the coefficients for different model and operator
combinations would reveal differences in model degeneration rates
and/or differences in operator maintenance practices. To include
comparisons, an expanded regression equation is defined. Consider
the indicator variables:
U = 1 for model M1 and 0 otherwise
V1 = 1 for operator O1 and 0
otherwise
V2 = 1 for operator O2 and 0
otherwise.
The regression equation for defining model and operator effects
is
. (17)
An equivalent formulation, which reveals the effect on
coefficients, is
. (18)
To simplify notation, consider the vectors
.
Table
3 shows the variations on the regression equation.
Table
4 presents the hypotheses for testing the effect of differences
in models and operators.
Fitted Model
The maintenance policies of a carrier as well as the particular
design (components and systems) in an aircraft model are major
factors in the operating characteristics of an aircraft. Selecting a
single aircraft model from a single carrier removes the complication
of varying models and carriers, and thus the assumption of constant
degradation and repair parameters is reasonable. This experimental
setting is ideal for focusing on the degradation of the operating
condition with accumulated use. As such, the data for operator
O2 were used with degradation model (16), because
the O2 fleet consisted only of B737 aircraft.
Because the number of failures is a counting variable, the error
variance is not likely to be constant. Therefore, an iteratively
reweighted least squares estimation method was used, where the
weights were reciprocals of the fitted values (McCullagh and Nelder
1989). The effect of weighting was minimal, so the unweighted sums
of squares are reported. The results from fitting the degradation
model to the operator O2 data are given in table
5.
Clearly the overall fit of the change model is strong (F =
96.22). Furthermore, the individual components in the model are
highly significant. Maintenance in the observation window and time
since maintenance are important factors in predicting the number of
unscheduled landings that occur in the window. Of particular
significance is the acceleration in the number of repairs
(increasing failure rate) as time since repair increases . With reference to Proposition 1, the
regression provides the following result.
Result 1 (degradation): The rate of unscheduled landings
increases with time since the last block repair.
Furthermore, there is evidence that the block repair is not as
good-as-new, because the sign on X1 =
I2 - I1 is negative and the sign
on X2 = k2 -
k1 is positive. A test on the difference is highly significant (P <
0.001). However, the data did not allow for a test of diminishing
repair fraction. The aircraft in the operator O2
fleet are relatively new and the maximum is k = 1. The
regression gives the analogous result for Proposition 2.
Result 2 (imperfect repair): The rate of unscheduled
landings decreases after a block repair, with the decrease greater
for the initial repair than for subsequent repairs.
To consider the issue of differential effects for the aircraft
model and operator, the additional terms with indicator variables
were included. Table
6 presents the regression results. The additional sum of squares
for models and operators indicated in table 6 are considered after
including other effects. That is, the outcome (number of unscheduled
landings) was adjusted for the model effect when considering the
operator effect, and it was adjusted for the operator effect when
considering the model effect. In both cases, the effects are
statistically significant (i.e., there is a differential effect for
operators and models). In the context of the regression equation,
the effect of maintenance on unscheduled landings was not the same
for the operators in the study. As well, the effect of the time
since maintenance was not the same for the models selected.
Result 3: The relationship between the rate of unscheduled
landings and the time since the last block repair and the number of
block repairs depends on the aircraft model and operator.
Using the indicator variables, it is possible to write out the
fitted equation for each (operator and model) type. The estimates
for equation parameters are given in table
7. The equation for operator O2(q = 3)
is slightly different from the equation using only
O2 data, owing to the greater variation in using
multiple operators and models. However, it is a good reference for
understanding the changes in the equation with the operator/model
variations. The biggest operator effect is the difference of
O3(q = 2,5) from the others on the estimate
. For aircraft models, the estimate of is most affected.
DISCUSSION
The operating condition of aging aircraft has been a hotly
discussed topic for more than a decade. The Federal Aviation
Administration's position is that the operation of older aircraft is
an economic decision and not a safety issue; that is, aircraft can
be repaired to a safe operating condition and the cost of those
repairs is the issue.
This study considers the trajectory of the health status of an
individual aircraft, with an emphasis on episodes where flights are
interrupted because of mechanical failures affecting safety. In the
context of a model for mechanical failure, two experiments were
carried out. In the first experiment, a single model and carrier
were analyzed for the potential impact of aircraft age (accumulated
use) and repair on schedule reliability. The study assumes that all
the selected aircraft are equivalent except for age, and the fleet
management practices of the carrier remain consistent over time. In
this setting, the variability in the failure rate (unscheduled
landings) can be partly attributed to the aging of the aircraft and
incomplete repair during preventive maintenance. The second
experiment involved multiple operators and aircraft models. With the
same failure model, the differential effect of operational practices
and aircraft design can be studied.
The following can be concluded from the results of this
study.
- The percentage of variation in unscheduled landings that can
be explained by degradation with age and incomplete repair is
high.
- Age (accumulated hours of use) has a statistically significant
effect on failures (unscheduled landings), with an increasing
failure rate as age increases.
- The improvement in the operating condition with planned
preventive maintenance is not to good-as-new.
- The relationship between failures and degradation differs from
model to model.
- The relationship between failures and repair differs from
operator to operator.
The clear relationship between unscheduled landings and
degradation/repair in the regression model has implications for the
maintenance policies of operators. The operator has control over the
repair intervals-(τ,δ) and the repair effort
β-the fraction of good-as-new. The dependence of the
regression coefficients on the maintenance parameters
(τ,δ,β) is implied in this paper, but that
connection can be made more explicit by using the actual derivatives
in the series approximations. In that way, changes in the values of
the maintenance parameters would translate into changes in the rate
of unscheduled landings. Therefore, an operator could explore the
outcome (in terms of unscheduled landings) of changes in the repair
parameters, for example, the block repair interval.
The purpose of our research was to establish the feasibility of
predicting unscheduled landings from data on use and maintenance. An
earlier study (Nowlan and Heap 1978) found that 89% of aviation
mechanical malfunctions were unpredicted using operating limits or
undertaking repeated checks of equipment. The results of this work
indicate that important problems in the operation of aircraft can be
studied with existing field data. Use of these results in the
management of an airline would require additional study, but a step
in that direction has been taken here.
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END NOTES
1. A block hour refers to flying time in
hours, including takeoff and landing.
2. A D-check refers to the major
maintenance and overhaul programs in which the aircraft is
completely stripped down and inspected, with many parts and
components replaced or refurbished.
ADDRESSES FOR CORRESPONDENCE
1 Corresponding Author:
L. Maclean, School of Business Administration, Dalhousie University,
Halifax, Canada B3H 3J5. E-mail:L.C.MacLean@dal.ca
2 A. Richman, AlgoPlus
Consulting Ltd., Halifax, Canada B3H 1H6. E-mail:arichman@algoplusaviation.com
3 S. Larsson, School of
Business Administration, Dalhousie University, Halifax, Canada B3H
3J5. E-mail:S.O.Larsson@dal.ca
3 V. Richman, Sonoma
State University, Rohnert Park, CA 94928-3609. E-mail:Vincent.richman@sonoma.edu
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