Aerospace and Electronic Systems Magazine April 2017 - 14

Airplane Flight Safety Using Error-Tolerant Data Stream Processing
Table 5.

Comparison of Error Signatures and Binary Residuals
Error Functions and Signatures
Expected value in normal mode

Zero

Zero

Expected value in error modes

Not zero, value is compared to
signatures and classified into
different modes.

Not zero, value is not further
considered.

Number of error functions/residuals
for fault isolation

One

Multiple

Need to estimate signatures for
different error modes

Yes

No

Can detect single source fault

Yes

Yes

Can isolate single source fault

Yes, but it depends on accurate fault
modeling.

Yes, but it depends on multiple
models/residuals.

Can isolate most common faults

Yes

Yes

onds. From Figure 13a, PILOTS indicates that the estimated weight
is reasonably close to the real weight during the first cruise phase,
but is not very close during the second phase. That is, the differences between the estimated and real weights are at most ∼643 kg
for the first cruise phase and ∼1,935 kg for the second cruise phase.
The PILOTS corrected weight discrepancy between the two phases
can be explained by inaccuracy of the airspeed estimation during
the second cruise phase, which may be caused by variables that
are not considered such as angle of attack, center of gravity, and
aircraft engine and configuration settings. The results reveal that
the PILOTS airspeed prediction model is not accurate enough to
precisely estimate the weight from the airspeed; but nonetheless, it
is able to detect the underweight condition.

DISCUSSION
COMPARISON OF ERROR SIGNATURES AND BINARY
RESIDUALS
In the PILOTS programs, error functions and signatures are used
for fault detection and isolation. Error signatures are compared
to binary error residuals in Table 5. The expected value of error
functions and residuals under normal conditions is zero. When the
value of an error function deviates from zero, it is compared to a
vector of error signatures that are determined theoretically based
on models of different fault types. Using mode likelihood vectors, the most likely mode is estimated which may correspond to a
single sensor fault or multiple sensor faults. In the case of a single
sensor fault, PILOTS uses logical redundancy to estimate the correct value for the erroneous data. In the simplified FDIR methods
implemented in the PILOTS declarative language, the value of a
residual was considered as binary. As shown in the SpeedCheck
program, one error function and four signatures are used to detect
and isolate different fault conditions. Similar work could be done
14

Binary Residuals

using residual based methods, with three different residuals for isolation [22].
PILOTS works best in detecting and isolating faults within
the assumptions used to generate the error signatures. In SpeedCheck, a pitot tube clearance ratio of 0.1-0.33 is assumed when
pitot tubes fail due to icing, and a ground speed indication of
zero when GPS fails (GPS denied). If the pitot tube clearance
ratio is not in the expected range, or ground speed fails in some
other unanticipated way, the PILOTS program will most likely
report "unknown error". While in a binary residual based method,
with properly chosen redundant residuals, the unanticipated fault
would be correctly isolated. Even though the error signaturebased approach is less capable of isolating unanticipated errors
with respect to residual-based approaches, the error signatures
require fewer error functions (redundancy models) and may be
therefore less sensitive to modeling errors, demonstrating very
good performance in properly anticipated error conditions. Also,
in some FDIR research for automotive applications [27] [28], the
residuals are not considered as binary values, but are assumed
to have different distributions according to different modes. In
these cases, the residual based methods are similar to the error
signatures approach.
Assuming sensor faults exhibit characteristic error function
"signatures," PILOTS is able to alert aircrew of possible anomalies. Trusting the output is based on the fidelity of the error and
signatures model; however, even properly designed simple analytics could provide the aircrew timely updates of possible sensor faults using additional information from weather forecasting,
other sensors, and physical models of airplane parameters such
as weight profiles. Increasing the fidelity of any of these methods
would enhance the results from PILOTS and would be needed for
any aviation equipment with flight passengers. In summary, error
signatures require modeling specific fault modes, but have the potential to enhance pilot awareness complementing residual-based
error detection and isolation techniques.

IEEE A&E SYSTEMS MAGAZINE

APRIL 2017



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