Aidan W. Rinehart

Vantage Partners, LLC, Brook Park, Ohio

Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Integrated Architecture for Aircraft Engine

Performance Monitoring and Fault Diagnostics:

Engine Test Results

NASA/TM—2015-218448

January 2015

AIAA–2014–3924
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Aidan W. Rinehart

Vantage Partners, LLC, Brook Park, Ohio

Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Integrated Architecture for Aircraft Engine

Performance Monitoring and Fault Diagnostics:

Engine Test Results

NASA/TM—2015-218448

January 2015

AIAA–2014–3924

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

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Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2015-218448 1 

An Integrated Architecture for Aircraft Engine Performance 

Monitoring and Fault Diagnostics: Engine Test Results 

Aidan W. Rinehart 

Vantage Partners, LLC 

Brook Park, Ohio 44142 

Donald L. Simon 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

This paper presents a model-based architecture for performance trend monitoring and gas path fault 

diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique 

analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and 

isolation purposes. Diagnostic results from the application of the approach to test data acquired from an 

aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented 

nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path 

seeded-faults under steady-state operating scenarios although some fault misclassifications are noted 

during engine transients. Recommendations for follow-on maturation and evaluation of the technique are 

also presented. 

Nomenclature 

C-MAPSS40k commercial modular aero-propulsion system simulation 40 k lbf thrust 

H fault influence matrix 

HPC high pressure compressor 

HPT high pressure turbine 

LPC low pressure compressor 

LPT low pressure turbine 

 m fault magnitude 

mˆ estimated fault magnitude 

PBM performance baseline model 

RTSTM real time self-tuning model 

R sensor measurement covariance matrix 

u command vector 

VIPR vehicle integrated propulsion research 

WSSEE weighted sum of squared estimation errors 

WSSR weighted sum of squared residuals 

 y sensor vector 

y

~ sensor residual vector 

yˆ estimated sensor vector 

y

~ˆ estimated sensor residual vector 
NASA/TM—2015-218448 2 

Introduction 

Conventional aircraft engine gas path diagnostic approaches are designed for ground-based post-flight 

processing of “snapshot” measurement data collected at a limited quantity of operating points each flight 

(Refs. 1 to 3). However, advances in onboard processing and flight data acquisition capabilities are 

providing access to increased quantities of flight data and enabling new diagnostic approaches. Analyzing 

full-flight streaming measurement data, either onboard in real-time or post-flight, can help reduce diagnostic 

latency and improve overall engine safety and reliability. In response to this, several research efforts 

conducted within the aviation community focused on the analysis of streaming engine measurement data. 

Merrington et al. applied analytical redundancy methods to process aircraft gas turbine engine transient 

measurement data (Ref. 4), and Kerr et al. (Ref. 5), Dewallef et al. (Ref. 6), and Borguet et al. (Ref. 7) have 

each proposed Kalman filter-based approaches for the on-line processing of aircraft engine measurement 

data for diagnostic purposes. However, these approaches have primarily focused on either performance 

estimation or fault diagnostics as opposed to performing both tasks concurrently. Additionally, the 

approaches that have focused on joint performance estimation and fault diagnostics have only considered 

turbo machinery faults, not actuator or sensor faults. Recently, NASA has developed a model-based 

performance trend monitoring and gas path diagnostic architecture designed to process streaming full-flight 

aircraft engine measurement data (Refs. 8 to 10). This architecture provides the dual functionality of 

estimating and trending engine performance parameters and diagnosing the occurrence of gas path system 

faults and isolating the faults including turbo machinery, actuator or sensor faults. The fault isolation is 

completed under the assumption that only one gas path system fault will be present at a given time. 

Simulation studies have shown that this architecture holds promise for analyzing full-flight engine data 

(Ref. 9). This paper will present results from the application of this architecture on engine test data, 

including both nominal and faulty engine operating scenarios. The remainder of this paper proceeds as 

follows. First, the introduction of the architecture and the various components comprising it are explained. 

Next, the application of the architecture for processing engine test data is discussed and the associated 

results are presented. This is followed by a discussion of the results, planned future work, and conclusions. 

Architecture Description 

A block diagram schematic of the NASA-developed model-based performance trend monitoring and 

gas path fault diagnostic architecture is shown in Figure 1 (Ref. 10). It contains three main components. 

These include: (1) a Real-Time Self Tuning Model (RTSTM); (2) a Performance Baseline Model (PBM); 

and (3) a Fault Diagnostics module. Each component is further discussed in the subsections below. 

Figure 1.—Performance trend monitoring and gas path fault diagnostic architecture. 
NASA/TM—2015-218448 3 

Real-Time Self Tuning Model 

The RTSTM is a piecewise linear model that is self-tuned by a Kalman tracking-filter. The Kalman 

filter estimates model state variables and a set of model tuning parameters reflecting performance 

deterioration within the engine. Through these Kalman filter estimates, the RTSTM accounts for changes in 

engine dynamics and changes from the gradual performance deterioration that aircraft gas turbine engines 

naturally experience as they wear over time from standard use. The RTSTM was designed to provide 

accurate continuous real-time estimates of engine performance parameters over the lifetime of the engine. 

Performance Baseline Model 

The PBM is built from the same piecewise linear model as used in construction of the RTSTM. 

However, in the case of the PBM, no Kalman filter tracking input is applied. The PBM continuously 

receives actuator commands and sensed fan speed measurements as inputs. The PBM also receives tuning 

parameter updates periodically from the RTSTM. These periodic tuner updates enable the PBM to adapt 

to gradual long-term engine performance deterioration. However, unlike the RTSTM, which continuously 

receives updates via a Kalman filter, the periodic tuner updates provided to the PBM prevents engine 

performance changes, potentially due to a fault, from being instantaneously absorbed into the model. In 

this form, the PBM serves as a baseline for describing recent engine performance because it is updated at 

a slower rate than the RTSTM. By comparing sensed engine outputs to PBM estimated outputs, an abrupt 

performance change, which may be indicative of a fault, can be detected. 

Fault Diagnostics 

The overall fault diagnostics logic is a two-step process consisting of fault detection and then fault 

isolation. Fault detection is performed by monitoring a vector of measurement residuals, y

~, between 

sensed engine outputs, y, and the PBM estimated outputs, yˆ , defined by Equation (1). 

y y yˆ ~  (1) 

If a gas path fault impacting engine performance occurs, the sensed engine and PBM outputs are 

expected to diverge resulting in an increase in the residuals. Fault detection is accomplished by 

calculating a weighted sum of squared residuals (WSSR) value based on the measurement residual vector 

and comparing this calculated value against a threshold value. The WSSR is calculated as 

WSSR y R y ~ T 1 ~ (2) 

where R is the sensor measurement covariance matrix. Each time step, a new WSSR value is calculated 

based on the most recent sample of measurement data. Then the WSSR value is compared against a 

defined anomaly detection threshold. Logic for recognizing a fault initiates when the WSSR value exceeds 

the threshold. The fault recognition logic requires the high WSSR value to persist for a period to filter out 

anomalous spikes. After this persistence metric is met, then fault isolation logic is engaged to identify the 

root cause for the fault. For the first portion of a test, the WSSR calculation is not compared against the 

threshold. This is a WSSR blackout period to prevent engine start up dynamics from triggering a false 

alarm. The length of the engine start up dynamics defines the duration of the WSSR blackout period. 

Fault isolation is accomplished through a linear least-squares estimation approach implemented with 

the assumption that at a given time only one fault may be present. This assumption is reasonable based on 

the high reliability of gas turbine engines and the low probability that any fault will be present. The 

isolation process compares the residual vector against a matrix of coefficients that were pre-calculated at 

different points throughout the engine operating envelope. This pre-calculated matrix is referred to as the 
NASA/TM—2015-218448 4 

Fault Influence Matrix, H, and it is a collection of single matrices assembled to allow interpolation 

calculations. The Fault Influence Matrix is populated with data collected while running the PBM under 

nominal and faulty operating scenarios. The resulting residuals between the nominal and the faulty PBM 

outputs, normalized by the magnitude of the fault, m, form a column of the H matrix. Each of the H

matrix columns pertains to a specific fault at a particular operating condition. Equation (3) defines the 

calculation of a single element of the fault influence matrix, Hi,j, where i is the index of engine sensor in 

the residual vector and j is the index of fault type. 

j

i

i j

m

y H

~

, (3) 

Upon fault detection, the observed measurement residual vector, y

~, is further processed to isolate the 

most likely root cause for the fault based on the type of faults contained in the H matrix. To do so an 

estimated magnitude for each possible fault is first calculated as follows 

m   H R H H R y T

j j

T

j j

~ ˆ 1 1 1    (4) 

These estimated fault magnitudes are used to produce an estimated vector of sensor residuals for each 

fault type as shown in Equation (5) 

j H jm j y ˆ ~ˆ (5) 

Finally, fault isolation is performed by identifying the fault type that produces the estimated sensor 

residual vector that most closely approximates the observed measurement residual vector in a weighted 

least squares sense. A vector of values pertaining to the weighted sum of squared estimation errors 

(WSSEE) is calculated using Equation (6) for each potential fault type. The fault type yielding the 

smallest WSSEE, indicating the closest match to the provided fault types contained in H, is classified as 

the fault. 

  j

T

j j WSSEE y y R y y ~ ~ˆ ~ ~ˆ   1 (6) 

Application 

This section will cover the application of this performance trend monitoring and gas path fault 

diagnostic architecture using data obtained from the NASA Vehicle Integrated Propulsion Research 

(VIPR) engine tests (Ref. 12). The VIPR program is a series of ground-based engine tests conducted to 

mature aircraft engine health management technologies. To date, two VIPR tests (denoted as VIPR I and 

VIPR II) have been conducted, with a third test, VIPR III, scheduled to occur in 2015. These tests are 

ongoing at the NASA Armstrong Flight Research Center, formerly the Dryden Flight Research 

Center/Edwards Air Force Base, on a C-17 aircraft equipped with Pratt & Whitney F117 turbofan 

engines. The VIPR tests include baseline runs where the test engine is operating normally without faults 

as well as runs with faults applied that are non-damaging to the engines. These fault cases are created by 

operating the engine with mis-scheduled 14th stage bleed valve and station 2.5 bleed valve actuators. 

These faults provide a good test case because operating the actuators differently than their normal 

scheduled operation will result in observable gas path variations. 

The diagnostic architecture applied for analyzing the available VIPR data was implemented using a 

NASA-developed 40,000 lbf thrust class engine simulation called Commercial Modular Aero-Propulsion 

System Simulation 40k (C-MAPSS40k) (Ref. 11). Exercising the C-MAPSS40k simulation produced a 
NASA/TM—2015-218448 5 

data set that was reduced to the piecewise linear model used to construct the RTSTM and the PBM shown 

in Figure 1. The models generated from C-MAPSS40k displayed notable differences in performance 

compared to the nominal steady-state performance data recorded from the normal operation of the F117 

turbofan engines used in the VIPR tests. To account for these model to engine differences a method was 

developed and applied to modify, or re-trim, the piecewise linear models based on nominal steady-state 

performance data obtained in the VIPR tests (Ref. 10). This enabled better model to engine performance 

agreement and helped to improve overall diagnostic results. Table 1 contains the vector of eight gas path 

sensor measurements and Table 2 contains the four actuator commands. The two sensors in Table 1 

denoted by an asterisk, T25 and P25, were only included during VIPR II testing. 

The asterisk next to the BLD14 symbol in Table 2 indicates the 14th stage bleed valve was not one of 

the actuator commands included in the C-MAPSS40k model used to generate the piecewise linear 

models. As a result, the standard model-based method for calculating the column of the Fault Influence 

Matrix corresponding to the 14th stage bleed valve fault is not possible. Instead, 14th stage bleed valve 

measurement residual vectors were calculated based on actual VIPR data collected during the 14th stage 

bleed valve fault testing. Data collected at given steady-state power setting conditions were averaged and 

referenced against PBM estimated outputs to provide residuals analogous to those simulated for other 

fault types. This method allowed creation of Fault Influence Matrices that included the 14th stage bleed 

valve fault. 

Prior to analyzing VIPR data with the performance trend monitoring and fault diagnostic architecture, 

Equation (4) was used to construct a Fault Influence Matrices, and the WSSR anomaly detection 

thresholds were defined. The Fault Influence Matrices were constructed assuming eight possible single 

fault types, as identified in Table 3. While the only faults evaluated during the VIPR testing were a 

station 2.5 bleed fault and a 14th stage bleed valve fault, the six additional fault types were included as 

TABLE 1.—GAS PATH SENSOR MEASUREMENTS 

Symbol Description 

N1 Fan speed 

N2 Core speed 

T25* Low pressure compressor exit temperature 

T35 High pressure compressor exit temperature 

T5 Low pressure turbine exit temperature 

P25* Low pressure compressor exit pressure 

Ps3 High pressure compressor exit pressure 

P5 Low pressure turbine exit pressure 

*- Research sensors unique to VIPR II engine data. 

TABLE 2.—ACTUATOR COMMANDS 

Symbol Description 

Wf Fuel flow 

VSV Variable stator vane 

BLD25 Station 2.5 bleed valve 

BLD14* 14th stage bleed valve 

*- Valve not modeled in C-MAPSS40k 

TABLE 3.—FAULT INFLUENCE MATRIX FAULT TYPES 

Fault index Fault types 

1 Fan 

2 Low pressure compressor 

3 High pressure compressor 

4 High pressure turbine 

5 Low pressure turbine 

6 Station 2.5 bleed valve 

7 Variable stator vane 

8 14th stage bleed valve 

 
NASA/TM—2015-218448 6 

possible fault scenarios to provide a more representative and challenging fault isolation problem. The 

anomaly detection threshold and persistency values were established through a manual process of running 

non-faulted VIPR data through the architecture and selecting threshold and persistency values that did not 

produce false alarms. In conducting this process, the WSSR signal and the measurement residual vector, y

~, 

were filtered using a median filter to help reduce signal noise, and removed outliers in an effort to 

improve overall diagnostic results. The WSSR anomaly detection threshold and persistency values applied 

for analyzing the VIPR I and VIPR II data are not identical. This variation is because the test engine used 

in the VIPR II test contained two additional sensors, P25 and T25, that the VIPR I test engine did not. The 

increase in the number of sensors produced a larger WSSR while no fault was present. This required a 

slight increase in the anomaly detection threshold for VIPR II tests. 

Results 

The following is a sample of the results obtained from processing VIPR I and VIPR II data through 

the performance trend monitoring and fault diagnostic architecture. The illustration in Figure 2 describes 

a typical engine test. As illustrated in Figure 2, the tests were segmented into two parts, a steady-state 

portion followed by a transient power sweep portion. The steady-state portion involved stepping up and 

stepping down the engine fan speed. The steady-state testing held the engine fan speed at a constant value 

for a short period, and then the fan speed was changed to a new level. The transient portion included two 

transient operations. The first transient slowly ramped the fan speed up and down; whereas, the second 

transient was faster. All of the test cases presented below, except for the VIPR II baseline test, which did 

not include the transient power sweeps, followed the test format as depicted in Figure 2. The test cases 

containing faults have the fault inserted for the entire test unless otherwise noted. 

Results from the VIPR I and VIPR II baseline runs where the engine was operating nominally without 

any faults are shown in Figures 3 and 4, respectively. In these figures, and all remaining figures of the 

document, parameter names and engineering units have been omitted due to the proprietary nature of the 

data. The top subplots of each figure show an unspecified gas path parameter plotted against time. For these 

subplots, the blue line represents the sensed engine measurement, the red line represents the PBM produced 

estimate, and the green line represents the RTSTM estimate. For both Figures 3 and 4 the green line is not 

visible due to the close agreement between the RTSTM estimate and the sensed engine measurement. 

Figure 2.—Representative VIPR event test sequence. 

Time

Power Settin

g

Steady State Power Sweep Transient Power Sweep
NASA/TM—2015-218448 7 

The sensed and PBM estimated values also match very well. No faults were present during the tests 

conducted to acquire the data displayed in Figures 3 and 4, so the close agreement in the sensed and PBM 

estimated values was expected and desired. The middle subplots displays the WSSR values plotted against 

time in blue and the selected anomaly detection threshold are represented with a dash-red trace. For these 

two cases, the WSSR signal remained below the established anomaly detection threshold throughout the 

entire run. Since the WSSR signal never exceeded the threshold, the fault isolation logic never engaged and 

“No Fault” is reported over the duration of these test cases. The bottom subplots of Figures 3 and 4 show the 

fault classification plotted against time, which in the baseline cases was “No Fault”. 

Figure 3.—Baseline VIPR I results. 

Figure 4.—Baseline VIPR II results. 
NASA/TM—2015-218448 8 

Figures 5 and 6 contain results from VIPR I and VIPR II test cases when the station 2.5 bleed valve 

was intentionally failed open for the entire test case. The gas path parameter depicted in the top subplots 

of Figures 5 and 6 show very close agreement between the sensed measurement and the PBM estimate for 

the majority of this test. This is expected as the station 2.5 bleed valve is normally scheduled open during 

lower power settings and modulates closed as the engine increases in power setting. During lower power 

settings, the sensed engine parameter measurements and PBM produced estimates show close agreement, 

because the failed open valve position is the same as the normally scheduled position. Once the engine is 

operating at higher power settings, sensed engine measurements and PBM estimates begin to diverge and 

Figure 5.—Station 2.5 bleed valve fault VIPR I results. 

Figure 6.—Station 2.5 bleed valve fault VIPR II results. 
NASA/TM—2015-218448 9 

the presence of the fault becomes apparent. This is evident in the WSSR signal shown in the middle 

subplots of Figures 5 and 6. In the middle subplots, the WSSR signal can be seen to increase as the engine 

increases to higher power settings and eventually surpassing the anomaly detection threshold. The fault 

isolation logic engages after the WSSR has exceeded the anomaly detection threshold and it remains 

above the threshold for a period that satisfies the persistency requirement. Comparing the middle subplots 

of Figures 5 and 6, there is a noticeable difference in the duration that the WSSR signal exceeds the 

threshold. The VIPR II test in Figure 6 shows a wider range of power settings in which an anomaly was 

detected compared against the VIPR I test in Figure 5. In addition, the bottom subplot of Figure 6 shows 

that faults are detected for both transients near the end of the test while in Figure 5 a fault is only briefly 

detected during the second transient. The improved fault detection displayed in VIPR II data can be 

attributed to the additional P25 and T25 sensors added for this test. The faults that were misclassified can 

be attributed to dynamic modeling inaccuracies contained in the PBM. The misclassifications in Figure 5 

occurred during the second transient sweep. For the results illustrated in Figure 6, misclassifications 

occurred in both the steady-state and transient portions of the test. The misclassification during the 

steady-state portion occurred while the engine speed was transitioning to a new power setting. Therefore, 

this error was not a steady-state operation misclassification. The other misclassifications in Figure 6 were 

during the transient sweep portions of the test. However, both test results illustrated in Figures 5 and 6 

correctly classify the fault as a station 2.5 bleed valve fault during steady-state operation. As evident in 

the data from these experiments, accurate diagnostics during large rapid transient engine operation proved to 

be challenging for both VIPR I and VIPR II tests. The analysis shows faults were occasionally incorrectly 

classified during these tests; however, they were both correct while analyzing the steady-state data. 

Figures 7 and 8 contain VIPR I and VIPR II results for a 14th stage bleed valve fault case. The top 

subplots in Figures 7 and 8 show divergence between the sensed measurement and the PBM estimate for 

most of the test case. In Figure 7 the divergence is present from the beginning of the test until 2600 sec 

when the fault is removed. The top subplot of Figure 8 begins with the sensed measurement and PBM 

estimate in relative agreement for the first 300 sec, then the fault was inserted. After fault insertion, the 

PBM estimate diverges from the sensed measurement. The middle subplots in Figures 7 and 8 show that 

the calculated WSSR value has exceeded the anomaly threshold, which indicates a fault. Figure 7 shows 

the WSSR signal surpassing the threshold for the entire test until the fault is removed. Figure 8 illustrates 

that the WSSR signal was above the threshold once the fault was inserted around 300 sec; however, the 

WSSR signal did drop below the threshold at 2150 sec and at 2600 sec. These drops in WSSR signal below 

the threshold occurred at low power settings. This point indicates that the sensed measurements and the 

PBM estimates show relatively close agreement, even though the fault is still present during this time. 

The bottom subplots of both VIPR I and II tests show accurate fault classification during most of the 

steady-state portions of the test. At the very beginning of the VIPR I test, “No Fault” is reported even 

though the middle subplot of Figure 7 indicated an anomaly is present. This is due to the detection logic 

suppressing any faults for the first 40 sec of data to insure engine start up dynamics do not trigger a false 

alarm. The bottom subplot of Figure 7 shows a few instances where the fault is identified as something 

other than the 14th stage bleed valve fault. These are very brief instances, and they occurred while the 

engine was transitioning between power settings. The bottom subplot of Figure 8 displays a few incorrect 

fault classifications when the WSSR signal dropped below the threshold and reported there was “No 

Fault” detected. In addition, this subplot illustrates a brief misclassification when reporting a LPC fault 

during an engine transient. The incorrect classifications show that there are areas for improvement with 

the method. However, despite these inaccuracies the method proves to be very accurate for steady-state 

engine operation. 
NASA/TM—2015-218448 10 

Figure 7.—14th stage bleed valve fault VIPR I results. 

Figure 8.—14th stage bleed valve fault VIPR II results. 
NASA/TM—2015-218448 11 

Discussion 

Analysis of the VIPR engine test data marks the first time the model-based performance trend 

monitoring and gas path fault diagnostic architecture was applied for processing real engine data. Overall, 

the results are encouraging. The first step of fault diagnostics, fault detection, was shown to avoid false 

alarms when presented nominal data and to correctly detect anomalies when presented faulty data. 

Furthermore, the second step of the fault diagnostics, fault isolation, was found to correctly identify the 

fault type during steady-state engine operating conditions. However, this process did experience fault 

misclassifications during engine transients. This suggests that the PBM provides good steady-state 

agreement with the engine, but it exhibits some issues when working with dynamic transient behavior. 

More work is needed to investigate potential enhancements to help improve the PBM transient accuracy 

and the architecture diagnostic during transient performance. The ultimate goal would be to improve the 

architecture and model to a point where real-time processing of engine data could occur with confidence 

in the fault detection and isolation capabilities during transients. However, the first step is to implement 

this method as a ground based post-processing tool. In this scenario, the architecture could be easily 

modified to disengage during transients and only convey the fault most frequently diagnosed throughout 

the entire flight. The results shown above suggest that the architecture could successfully provide that 

type of analysis. 

While the architecture is capable of combined performance trend monitoring and gas path fault 

diagnostics, this paper only reports on an assessment of the fault diagnostic results. The performance 

trend monitoring aspect of the architecture, provided by the RTSTM Kalman filter, was not able to be 

fully evaluated based on the available VIPR I and VIPR II data because the engines underwent little to no 

performance variations during these tests. However, the VIPR III test to be conducted in 2015 will 

intentionally degrade the engine through volcanic ash ingestion testing. As such, the VIPR III test data is 

expected to provide the opportunity to assess the performance deterioration trend monitoring functionality 

of the architecture. 

Conclusion 

The model-based approach to gas path fault detection and isolation presented in this paper is a 

promising architecture for the processing of streaming engine sensor data. During steady-state operating 

periods, the architecture was able to avoid false alarms and was consistently able to correctly identify and 

classify the two bleed valve faults introduced in the VIPR test cases presented in this paper. The approach 

did experience fault misclassifications during engine transients. Inaccuracies between the model dynamics 

and the engine dynamics are believed to be the cause of these misclassifications, and further work is 

needed to focus on improving the model dynamic accuracy. The continuation of the VIPR test series 

includes an upcoming third test where engine performance will be degraded via volcanic ash ingestion 

testing. This test will provide data for evaluating the effectiveness of the performance trend monitoring 

estimation capability of the architecture. 

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