Ali Abdul-Aziz 

Cleveland State University, Cleveland, Ohio 

Mark R. Woike

Glenn Research Center, Cleveland, Ohio

Nikunj Oza and Bryan Matthews

Ames Research Center, Moffett Field, California

George Y. Baaklini

Glenn Research Center, Cleveland, Ohio

Propulsion Health Monitoring of a 

Turbine Engine Disk Using Spin Test Data

NASA/TM—2010-216743

September 2010

7650–45
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Ali Abdul-Aziz 

Cleveland State University, Cleveland, Ohio 

Mark R. Woike

Glenn Research Center, Cleveland, Ohio

Nikunj Oza and Bryan Matthews

Ames Research Center, Moffett Field, California

George Y. Baaklini

Glenn Research Center, Cleveland, Ohio

Propulsion Health Monitoring of a 

Turbine Engine Disk Using Spin Test Data

NASA/TM—2010-216743

September 2010

7650–45

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

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NASA/TM—2010-216743 1

Propulsion Health Monitoring of a 

Turbine Engine Disk Using Spin Test Data 

Ali Abdul-Aziz

Cleveland State University

Cleveland, Ohio 44115

Mark R. Woike

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Nikunj Oza and Bryan Matthews

National Aeronautics and Space Administration

Ames Research Center

Moffett Field, California 94035

George Y. Baaklini

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Abstract

On line detection techniques to monitor the health of rotating engine components are becoming 

increasingly attractive options to aircraft engine companies in order to increase safety of operation and 

lower maintenance costs. Health monitoring remains a challenging feature to easily implement,

especially, in the presence of scattered loading conditions, crack size, component geometry and materials 

properties. The current trend, however, is to utilize noninvasive types of health monitoring or 

nondestructive techniques to detect hidden flaws and mini cracks before any catastrophic event occurs.

These techniques go further to evaluate materials’ discontinuities and other anomalies that have grown to 

the level of critical defects which can lead to failure. Generally, health monitoring is highly dependent on 

sensor systems that are capable of performing in various engine environmental conditions and able to 

transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall 

performance of the engine system. Efforts are under way at the NASA Glenn Research Center through 

support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such 

sensor technology for a wide variety of applications (Refs. 1 to 5). These efforts are focused on 

developing high temperature, wireless, low cost and durable products.

Therefore, in an effort to address the technical issues concerning health monitoring of a rotor disk, 

this paper considers data collected from an experimental study using high frequency capacitive sensor 

technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-likedisk-to predict the disk faults and assess its structural integrity. The experimental results collected at a 

range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics 

Laboratory are evaluated using multiple data-driven anomaly detection techniques (Refs. 11 to 15) to 

identify anomalies in the disk. This study presents a well rounded evaluation of an online health 

monitoring of a rotating disk using these high caliber sensors and test the capability of the in-house spin 

system.
NASA/TM—2010-216743 2

1.0 Introduction

Health monitoring of critical turbine engine components is becoming a high interest topic among 

engine companies and associated aviation industry. Generally, health monitoring is conducted via sensor 

systems that are capable of functioning in a harsh environment and able to transmit a signal upon 

detecting the presence of a dominant crack. These systems must operate in a neutral fashion and cause no 

interference to the overall performance of the engine system. However, developments and 

accomplishment of this technology are dependent on various factors which include running parallel 

analytical and experimental studies to assess their applicability and success. Such efforts are under way to 

pursue this endeavor technology which covers high temperature, wireless, low cost and durable sensors 

for a wide variety of hot engine applications (Refs. 1 to 5).

Other means of inspections and health monitoring are available through nondestructive evaluation 

(NDE) techniques which are typically used to perform periodic checkups during major engine overhauls 

to locate any cracks that may have formed and to prevent premature catastrophic failure (burst) of the 

engine. However, some if not most of these techniques can be both costly and impractical to inspect such 

complex geometries and large components. Among those available techniques is the Fluorescent 

Penetrant Inspection (FPI) which is the lowest from a cost point of view. However, FPI often fails to 

disclose cracks that are tightly closed during rest or that are below the surface. The next system that can 

be most effective is the eddy current approach which can detect cracks along and below the surface, but it 

requires careful setup and operation, while allowing only a small portion of the disk to be practically 

inspected. Therefore, the need for more reliable diagnostic tools and high level techniques for damage 

detection and health monitoring of rotating components is of extreme importance and highly fundamental

to maintaining engine safety, reliability and life assessment (Ref. 5). 

The NASA Aviation Safety Program is aware of these concerns and is actively working on promoting 

the development of technologies that would contribute towards a reduction in the fatal aviation accidents 

and assist in improving safety as a whole. It is a team effort that includes a partnership between NASA, 

the Federal Aviation Administration (FAA), the aviation industry and the Department of Defense (Ref. 4).

This has motivated the Optical Instrumentation and NDE Branch at the NASA Glenn Research Center at 

Lewis Field to consider taking on a constructive role to help develop health monitoring technologies to 

detect rotor damage prior to any catastrophic events in support of these research plans (Refs. 5 and 6).

This role consists of running controlled spin tests to investigate and implement various sensing 

innovations for local and global detection of rotor damage. These tests will incorporate performing 

systematic evaluation of crack detection methodologies through management of highly controlled crack 

initiation and growth tests. Rotors test articles resembling turbine engine disk are to be spun at rotational 

speeds up to 12,000 rpm. Comparison of test data for baseline disks without any damage with those 

having an artificially induced damage (notch) will be conducted to appraise the findings. The subject of 

crack detection has been addressed by many researchers where vibration response of a cracked rotor 

passing through the critical speed is examined utilizing a simple hinge model for small cracks (Refs. 8 to 

10). Bently (Ref. 9) presented a rotor crack detection procedure based on the start-up and shutdown 

vibration monitoring. 

This paper presents a summary and discussion of the results obtained from a joint analyticalexperimental study that included spin testing of a rotor disk along with a detailed output from a multiple 

data-driven anomaly detection techniques to identify the anomalies in the rotor (Refs. 11 to 14).

Experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn 

Research Center’s Rotordynamics Laboratory are used to identify and verify crack detection methods 

using these multiple technical approaches. Results reported are discussed and evaluated. This study 

presents a select evaluation of online health monitoring of rotating disks with emphasis on comparing 

crack detection algorithms, simulated analytical crack detection settings and the latest up-to-date sensor

technology along with a high caliber in house spin testing system.
NASA/TM—2010-216743 3

2.0 Technical Approach

2.1 Capacitive Probe Sensor Technology

The capacitive sensors are designed to monitor the electrical property of “capacitance” to initiate and 

take measurements. Capacitance is defined as a field that exists between two conductive surfaces within 

some rational proximity. Variations in the distance between the surfaces lead to changes in the 

capacitance rate. This capacitance rate change is used by the sensors to indicated difference in position of 

a target. High-performance displacement sensors use small sensing surfaces and as result are positioned 

close to the targets (0.25 to 2 mm).

Figure 1 shows the disk specimen and the induced crack-notch along with the tip clearance probes. 

The test specimen disk has an outside diameter of 23.495 cm (9.25 in.) respectively; a bore and an outside 

rim thickness of 2.54 cm (1 in.) and 3.175 cm (1.25 in.); the thickness of the web is 0.254 cm (0.10 in.), 

and the cross section and height of the blades are 3.175- by 0.330-cm (1.25- by 0.13 in.) and 0.838 cm 

(0.33 in.), respectively. It has rotor like blades, total of 32, evenly spaced around the circumference. Eight 

holes, 0.508 cm (0.20 in.) diameter each, were drilled through the disk half-way in the rim. The holes 

were spaced every 45° and they were designed for future studies as possible mass add-on points or notch 

initiation sites.

The materials used to manufacture the disk specimen were nickel base alloy Haynes X-750 and 

Grade 2-Titanium. This paper focuses on the Haynes X-750 material disk which weighs approximately 

10.75 lb (4.88 kg). For blade tip clearance measurements, a capacitive sensor system was installed, see 

Figure 1. These types of sensors are based on a DC offset rather than a modulation technique. The DC 

voltage, in conjunction with the motion of the rotor, allowed the current system to record three channels 

at a rate of 1 MHz each. The experimental testing included running tests under transients and steady state 

conditions such as ramp-up, ramp-down and cruise conditions. 

The controlled speed applied during the current testing ranged from a minimum of 1,000 up to a 

maximum of 10,000 rpm with acceleration-deceleration rates of 60 rpm/second. This insured surpassing 

the critical speed of 2,610 rpm and leading to post critical state. Experiments covered base line runs with 

undamaged disk and with damaged disk via artificially induced notch, shown in Figure 1.

Figure 1.—Test disk with a notch.

Capacitive 

Sensor
NASA/TM—2010-216743 4

(a) Constant amplitude (b) Variable Amplitude

Figure 2.—Simulated engine mission history profiles.

The notch had a width of 0.381 mm (0.015 in.) as per wire thickness and burn area of the electric 

discharge machining (EDM) process. The notch region was intentionally selected to be in the rim area 

due to the fact that the finite element results revealed that this section encounters the highest stress level 

in the disk during the spin operation (Refs. 1 and 2). Systemic considerations were emphasized to 

preserve consistency of the operating parameters and other experimental conditions during the removalreinstallation process of the disk specimen in both situations, baseline no-notch and notch state. 

Figure 2 shows the mission history profiles applied in this experimental work for the disks, the 

baseline unnotched and the notched disk. These profiles were derived on the basis of revolutions per 

minute data obtained on different flights comprising different flight maneuvers (Ref. 15). Figure 2(a) 

shows the constant amplitude portion which is one of the test mission profiles applied in the experiment. 

It clearly demonstrated less aggressive and smoother profile compared to the variable amplitude profile 

shown in Figure 2 (b), starting with a minimum of 200 rpm up to a maximum of 10,000 and a shutdown 

to 0 rpm. Meanwhile, Figure 2(b) represents the variable amplitude engine history which reflects steep 

and sharp changes in the rotational speed across the entire cycle, subjecting the disk to high and complex 

loading conditions in an attempt to fatigue the disk with the expectation that all existing anomalies would 

appear in the test data. The speed was varied sharply from a minimum of 200 rpm up to 10,000 rpm at 

various intervals. Testing the rotor under these conditions provided a good simulation to collect data 

under various loading conditions which the disk may experience during nontypical operating 

environment. Furthermore, these experiments supplied valuable assessments for both, the crack detection 

scheme and the structural durability of the disk materials. 

Experimental data under both applied mission profiles are represented in Figures 3 and 4. The data in 

Figure 3 are produced under the constant amplitude mission for both the baseline no-notch and the 

notched disks. The mission history is shown along with the trace vibration vector and bode plots for the 

phase and amplitude response. A clear observation is noted for both, the baseline and the notched disk; a 

closed loop representation for the trace of vibration vector is seen for the baseline disk while a gap in the 

loop is present for the notched disk. This behavior offers the first hint that we have a difference in the 

vibration response for the two structures indicating the presence of some type of anomaly. Such 

observation has been reported in Reference 7, where a crack in the rotor disk is documented via the 

presence of distorted trace of the vibration vector distribution and a rise in the phase and amplitude 

response upon surpassing the critical speed stage.

Time, Seconds

0 200 400 600 800 1000

Rotaional Speed, Rpm

0

2000

4000

6000

8000

10000

12000

Time, Seconds

0 100 200 300 400 500 600

Rotational Speed, Rpm

0

2000

4000

6000

8000

10000

12000
NASA/TM—2010-216743 5

Figure 3.—Bode Plots—9 minutes comparison test for the disk with and without notch, constant amplitude mission 

profile.

Figure 4.—Bode Plots—9 minutes comparison test for the disk with and without notch, variable amplitude mission 

profile.

This conduct is obvious in Figure 3 for the notched disk. A rise in the phase and amplitude response 

is clearly noted. While a flat and steady decrease in these parameters is shown for the baseline rotor

which implies that it is a healthy rotor. Again, similar remarks for the variable amplitude mission history 

profile are seen in Figure 4. A rise is reported in the phase and amplitude response as soon as the disk 

crosses the 5,000 rpm level up to the 9,000 rpm where a notable no changes response is apparent. The 

difference between the data reported for the variable and the constant amplitude profile is confined to a 

higher blade tip clearance for the variable amplitude (nearly about 22 percent higher). Therefore, 

examination of the above data verified that the detection scheme applied in the experiment seemed to 

deliver a reasonable set of results indicating the presence of a defect in the rotor disk. However, this 

confirmation has to be further documented by conducting more tests for different rotors under similar 

operating conditions to authenticate that this behavior in the data is strictly due to existing structural 

defects in the rotor and not due to a system related unbalance. 

Notch No-Notch Bode Plots

Amplitude VS. Rpm

Phase VS. Rpm

Mission Profile Trace Of Vibration Vector

Notch No-Notch
NASA/TM—2010-216743 6

In addition to the experimental work and to complement the findings, additional or parallel health 

monitoring assessments of the rotors were conducted using other nonexperimental approaches. Three 

different techniques were applied: Orca, IMS and OCSVM (Refs. 11 to 14). These techniques were 

simply limited to employing simulation type analysis to examine the experimental data under the same 

operating conditions employed in the tests and using the data collected (blade tip clearance) as a key input 

to the analytical simulation to check the viability of the detection techniques and to evaluate the 

performance of each methodology. These methodologies provide a method that can monitor the health of 

a system based on a certain fidelity which trains the model to identify normal system parameters from 

abnormal ones. This is implemented by defining groups of consistent system parameter data via 

examining and generalizing from examples of nominal system data. With extensive training data set, the 

knowledge based produced by these techniques should have most or all of the consistent parameter value 

combinations to effectively portray and monitor nominal system operation. Upon learning how the system 

should behave under nominal operating conditions, these data analyses techniques could easily identify 

abnormal behavior and display faulty message to the system operators. Three phases of simulation 

analyses were conducted for each test mission cycle applied. They are described in some details in the 

subsequent subsections. 

3.0 Detection Algorithms

3.1 Orca

Orca (Ref. 12) is an outlier detection algorithm which uses the Euclidean distance nearest neighbor 

based approach to determine outliers. For computational efficiency, it employs a modified pruning 

technique which allows it to perform in near linear time. For each point in the test data set, where a point 

is a row in the data set consisting of measurements taken at a single point in time which is in general

consists of displacements quantity, Orca calculates the nearest neighbor points from the reference data set. 

The output from Orca is a distance score which represents the average distance to its k-nearest neighbors; 

the more anomalous the point is the higher the score, since the nearest neighbors are farther away. More 

information about this algorithm can be found in Reference 12.

3.2 Inductive Monitoring System (IMS)

IMS is a cluster based modeling method. The algorithm is given a set of nominal data points and 

builds a model by agglomerative clustering of the data points. The resulting model is used to generate 

anomaly scores for new data. For each test data point, IMS finds its distance to the nearest cluster’s 

boundary. The score that is reported is the sum of the squares of the distances from the test data point to 

the dimensional bounds of the closest cluster. If a data point falls entirely within the cluster bounds, it is 

assigned a score of zero. More information about IMS can be found in Reference 11.

3.3 One-Class Support Vector Machine (OCSVM)

OCSVM is a one-class nonlinear kernel based algorithm that maps the training data to a higherdimensional feature space and then linearly separates nominal data from anomalies in that feature space. 

The idea is that such a model corresponds to a nonlinear model in the original data space, but still 

maintains the benefit of a linear model in that it is guaranteed to return the model with the lowest error 

over the training set. The algorithm identifies a subset of the training data (called the “support vectors”),

which is used to generate a hyperplane model. The anomaly score that is reported is the distance from the 

test data point to the hyperplane as measured in the feature space. More information on OCSVM can be 

found in References 13 to 14.
NASA/TM—2010-216743 7

3.4 Cycle Runtime Procedure

Figure 5 through 8 provides the scores for the three different techniques. In Figures 6 and 8 the 

anomaly scores are portrayed as positive values and any nominal points are represented as zero. This was 

done to allow for similar comparison across the algorithms. It is important to note that OCSVM allows 

some nominal sample points in the training set to be classified as anomalous. This percentage is governed 

by the Nu parameter, which is set by the user, but for which we set currently at the default value of 

10 percent. Due to this characteristic of the algorithm, 10 percent of all nominal data tested may result in 

anomalous classification. In Tables 1 and 2 the choice for fixing correct classification to 90 percent was to 

make the comparisons fair between all three algorithms, since OCSVM, in this case, was optimized for a 

correct classification of 90 percent.

TABLE 1.—CONSTANT AMPLITUDE—5,000 TO 10,000 RPM RESULTS

Algorithm Correct detection rate,

%

False alarm rate,

%

Accuracy,

%

Area under ROC

5% false 

alarm rate

Orca 100 5 97.55 1.00

IMS 93 5 94.02 0.93

OCSVM 100 5 97.55 0.99

90% correct 

detection

Orca 90 0.58 94.62 1.00

IMS 90 0.36 94.73 0.93

OCSVM 90 1.26 94.28 0.99

TABLE 2.—VARIABLE AMPLITUDE CYCLE-1,000 TO 10,000 RPM DATA

Algorithm Correct detection rate,

%

False alarm rate,

%

Accuracy,

%

Area under ROC

5% false 

alarm rate

Orca 100 5 98.33 0.99

IMS 87 5 89.82 0.87

OCSVM 99 5 98.28 0.99

90% correct 

detection

Orca 90 1.99 92.68 0.99

IMS 90 100 59.88 0.87

OCSVM 90 2.96 92.36 0.99

Figure 5.—Results of Data Driven Anomaly Detection Technique; ORCA and Inductive monitoring system (IMS).
NASA/TM—2010-216743 8

Figure 6.—Results of Data Driven Anomaly Detection 

Technique; OCSVM Algorithm.

Figure 7.—Results of Data Driven Anomaly Detection Technique; ORCA and Inductive monitoring system (IMS).

Normal Data 

Range
NASA/TM—2010-216743 9

Figure 8.—Results of Data Driven Anomaly Detection Technique; 

One-Class Support Vector Machines (OCSVM).

4.0 Analysis-Results

4.1 Constant Amplitude Cycle Runtime Procedure

For the constant amplitude cycle (Fig. 2(a)) data analysis, the data was recorded in two sets: “normal” 

and “notched”. The rotational speed for the disk continuously varied from 5,000 to 10,000 rpm for both 

normal and notched runs. In this analysis, only the 32 gap measurements were considered. Orca, 

Inductive Monitoring System (IMS) and One-Class Support Vector Machines (OCSVM) were chosen to 

evaluate this data. The normal run’s data was randomly divided in half. One half was used for training 

and the remainder half was used for validation. The means and standard deviations for all 32 channels are 

calculated for the training data and used to z-score normalize (normalize each channel to have zero mean 

and unit standard deviation) both the training and testing data sets. The plots, Figures 5 to 8, are the 

global anomaly scores for each of the algorithms on both the validation normal and the notched runs over 

time.

4.2 Variable Amplitude Cycle Runtime Procedure

An additional data set had rotational speed varying from 1,000 to 10,000 rpm; Figure 2(b) was 

evaluated using all three algorithms. For the analysis of 1,000 to 10,000 rpm data, the algorithms 

performed comparably to the 5,000 to 10,000 rpm analysis. However, in the category of false alarm rates 

when fixing the correct detection at 90 percent IMS appears to have trouble, with a false alarm rate of 

100 percent. It also appears that Orca has an edge in this category with a false alarm rate of 1.99 percent

where OCSVM is slightly higher at 4.57 percent.

It is also important to note that Orca and OCSVM performed well when fixing the false alarm rate at 

5 percent, which results in a 100 percent correct detection rate. The correct detection rate is defined as the 

percentage of faulty sample points that fall above a fixed threshold and are correctly identified as faulty.

A correct detection rate of 100 percent does not necessarily imply that the false alarm rate is 0 percent, 
NASA/TM—2010-216743 10

since the false alarm rate is defined as the percentage of nominal sample points that fall above the same 

threshold and are labeled faulty. False alarms may still be present if there is an overlap in score 

magnitudes between faulty and nominal sample points. In this case a threshold was chosen that yielded 

5 percent false positives and resulted in 100 percent correct detection. The area under the ROC curve

parameter also shows a slightly better performance for Orca and OCSVM with areas of 0.99 and 0.95 

respectively than IMS’s area of 0.87, see Table 2. 

Therefore, based on the information reported in Figures 5 to 8, it can be concluded that the detection 

techniques performed as expected and indications of malfunctions are seen in the scored representations 

of highly spiked distributions for the notched disk, while no spikes are noted for the baseline no notch 

disk. This applies to the data reported in Figures 5 to 8. The evaluation of the data reported from these 

techniques is also tied to the scores produced as a result of the simulation. For instance, the higher the 

score, the more anomalous is the structure.

All three algorithms appear to perform quite well in distinguishing between the notched disk run and 

the hold out nominal data. Orca and OCSVM seem to show slightly better numbers across a few of the 

metrics. In Table 1 the metrics used for comparison are correct detection, false alarm rate, accuracy, and 

area under the Receiver Operating Characteristic (ROC) curve, ROC stands for Receiver operating 

characteristic. It is a plot of false positives versus true positives. The ideal curve is one that has a 90 bend 

in it shooting straight up with the false positive = 0 and holding the true positive = 1 across resulting in an 

area of 1.00. When fixing the false alarm rate at 5 percent, Orca and OCSVM both have 100 percent

correct detection rate of the notched anomalous data, where IMS’s correct detection rate is at 93 percent.

When fixing a threshold so that the correct detection rate is 90 percent, all three show very good false 

alarm rates, see Table 1. An additional metric, that is independent of choosing a threshold, is measuring 

the area under the ROC curve. When comparing this metric across the three algorithms, all three methods 

are reporting very good areas with Orca and OCSVM doing slightly better than IMS. 

Conclusions

A health monitoring study of a turbine-engine-like rotor disk was conducted through combined 

experimental and data-driven anomaly detection techniques. The extent of the work involved conducting

simulation spin tests on turbine-engine-like rotors with and without an artificially induced notch at 

different rotational loading levels. Bode plots of data from these tests provided indications of differences 

induced by the fault. Additional health monitoring verification/evaluation was performed by using three 

different automatic data-driven detection algorithms: Orca, OCSVM and IMS. These techniques were 

simply limited to employing simulation type analysis to examine the experimental data under the same 

operating conditions employed in the tests data collected (blade tip clearance) was used as a key input to

the analytical simulation to check the viability of the detection techniques and to evaluate the 

performance of each methodology. Two different engine mission profile cycles were evaluated which 

covered variable amplitude and constant amplitude type speed variation where the rotational speed was 

varied from a minimum of 1,000 to 10,000 rpm at different time intervals. The results obtained showed 

that the detection algorithms are capable of predicting anomalies in the rotor disk with very good 

accuracy. Each detection scheme performed differently under the same experimental conditions and each 

delivered a different level of precision in terms of detecting a fault in the rotor. Overall rating showed that 

both the Orca and OCVSM performed better than the IMS technique. 

Furthermore, the experimental data acquired proved to be crucial in assisting data-driven anomaly 

detection techniques in assessing the health conditions of the rotor disk. The experimental data added 

more information about the crack detection scheme applied. It showed that the blade tip clearance tends to 

be higher upon existence of a notch. Bode plots offered more asserting results concerning the crack 

detection. This is noted for both, the baseline and the notched disk; a closed loop representation for the 

trace of vibration vector is seen for the notch disk while a gap in the loop is present for the notched disk.

This behavior offers the first hint that we have a difference in the vibration response for the two structures 

indicating the presence of some type of anomaly. However, additional work and testing must be 
NASA/TM—2010-216743 11

continued to develop, improve and link this combined analytical-experimental tool to offer a more precise 

and accurate appraisals of the health of rotating components. One last note, although this work is still in 

its early stages, however it is very promising and the expectation to put it this into a practical crack 

detection technology is quite high.

References

1. Ali Abdul-Aziz, G. Abumeri, Mark Woike and George Baaklini ,”NDE Using Sensor Based 

Approach to Propulsion Health Monitoring of a Turbine Engine Disk” , submitted for presentation at 

the SPIE Smart Structure/NDE, 8–12 March 2009, San Diego, CA.

2. Ali Abdul-Aziz, Mark Woike, John Lekki and George Y. Baaklini;” Health Monitoring of a Rotating 

Disk Using a Combined Analytical-Experimental Approach,” NASA Technical Memorandum, 

NASA/TM—2009-215675, Sep. 2009.

3. Abdul-Aziz, A., R.T. Bhatt and G.Y Baaklini; “Criteria for Crack Deflection/Penetration in Coated 

Ceramics; a Parametric Study”. Proceedings the 14th SPIE’s Annual International Symposium on 

Nondestructive Evaluation for Health Monitoring and Diagnostics, Mar. 18–22, 2007, San Diego, CA.

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Health Monitoring and Diagnostics, Feb. 26–Mar. 2, 2006, San Diego, CA.

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International Symposium on Nondestructive Evaluation for Health Monitoring and Diagnostics, Feb.

26–Mar. 2, 2006, San Diego, CA.

6. Abdul-Aziz, A., J. J. Trudell, George Y. Baaklini; “Finite Element Design Study of a Bladed, Flat 

Rotating Disk to Simulate Cracking in a Typical Turbine Disk, Part II” Proceedings the 11th SPIE’s 

Annual International Symposium on Nondestructive Evaluation for Health Monitoring and 

Diagnostics, Feb. 26–Mar. 2, 2006, San Diego, CA.

7. Wayne C. Hass and Michael J. Drumm;”Detection, Discrimination and Real-Time Tracking of 

Cracks in Rotating Disks,” IEEE, 2002

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4. TITLE AND SUBTITLE

Propulsion Health Monitoring of a Turbine Engine Disk Using Spin Test Data 

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6. AUTHOR(S)

Abdul-Aziz, Ali; Woike, Mark, R.; Oza, Nikunj; Matthews, Bryan; Baaklini, George, Y. 

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WBS 645846.02.07.03.11.03 

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National Aeronautics and Space Administration 

John H. Glenn Research Center at Lewis Field 

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14. ABSTRACT

This paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip 

clearance and tip timing measurements in a rotating turbine engine-like-disk-to predict the disk faults and assess its structural integrity. The 

experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics 

Laboratory are evaluated using multiple data-driven anomaly detection techniques to identify abnormalities in the disk. Further, this study 

presents a select evaluation of an online health monitoring scheme of a rotating disk using high caliber sensors and test the capability of the 

in-house spin system. 

15. SUBJECT TERMS

Rotor dynamics; Spin testing; Data driven anomaly detection; Online health monitoring 

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