Application of Autoassociative Neural Networks to Health 

Monitoring of the CAT 7 Diesel Engine

by Andrew J. Bayba, David N. Siegel, and Kwok Tom

ARL-TN-0472 February 2012

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Army Research Laboratory

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ARL-TN-0472 February 2012

Application of Autoassociative Neural Networks to Health 

Monitoring of the CAT 7 Diesel Engine

Andrew J. Bayba, David N. Siegel, and Kwok Tom

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution unlimited. 
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Application of Autoassociative Neural Networks to Health Monitoring of the 

CAT 7 Diesel Engine

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14. ABSTRACT

An autoassociative neural network (AANN) algorithm was applied to fault detection and classification for seeded fault testing 

on a Caterpillar C7 diesel engine. Data used for this work is a subset from the seeded fault testing performed at the U.S. Army 

Tank and Automotive Research, Development and Engineering Center (TARDEC) test cell facilities. This report extends 

previous work performed on fault detection and classification performed by the U.S. Army Research Laboratory (ARL) on the 

C7 engine by including analysis using AANN [1]. We believed that AANN would be particularly useful in the diagnosis of 

faults in these tests because the correlation of several sensors appeared to be nonlinear. Although AANN performed quite 

well, the results were similar to the previous work using linear Principal Component Analysis (PCA) Statistics. We believe 

that the potential benefit in using AANN was not achieved due to the nature of the tests analyzed—in particular, data 

collection at discrete set-points in engine operation—and that within these set-point regimes, the sensor readings tend to be 

linearly correlated.

15. SUBJECT TERMS

Health monitoring, diesel engine health, CAT 7 diesel engine, diesel engine prognostics and diagnostics

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Andrew J. Bayba

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iii

Contents

List of Figures iv

List of Tables iv

1. Introduction 1

2. Experimental 1

3. Data for Analysis 3

4. Analysis 5

5. Discussion 12

6. Conclusion and Recommendations 13

7. References 14

List of Symbols, Abbreviations, and Acronyms 15

Distribution List 16
iv

List of Figures

Figure 1. Instrumented CAT 7 engine in the TARDEC test cell....................................................2

Figure 2. Typical stepped control of engine speed for a performance run. ....................................3

Figure 3. Neural net design. Every node connection is not shown for clarity................................6

Figure 4. AANN SPE ROC curves for various bottleneck node counts for Regime 1...................8

Figure 5. AANN SPE ROC curves for various bottleneck node counts for Regime 2...................8

Figure 6. AANN SPE ROC curves for various bottleneck node counts for Regime 3...................9

Figure 7. AANN SPE ROC curves for various bottleneck node counts for Regime 4...................9

Figure 8. SPE health value for differing levels of air restriction (Regime 4)...............................11

Figure 9. SPE health value for differing levels of exhaust restriction (Regime 4).......................12

List of Tables

Table 1. Baseline performance runs................................................................................................4

Table 2. Seeded fault performance runs. .........................................................................................4

Table 3. Signals recorded from CAN and dyno..............................................................................5

Table 4. Operating regimes for analysis. ........................................................................................5

Table 5. AANN Optimum bottleneck node count and detection results (for 0% false 

positives)..................................................................................................................................10

Table 6. Detection rate comparison of AANN and PCA..............................................................10

Table 7. AANN Sensor-fault contribution results. .......................................................................11
1

1. Introduction

The U.S. Army is very interested in monitoring the health of equipment in the field. The Army, 

in general, wishes to reduce operating cost, while the commander in the field is concerned about

availability of equipment for operations, as well as arranging for logistical support to minimize 

downtime. To these ends, the U.S. Army Research Laboratory (ARL) has been investigating 

methods for health monitoring and assessment, with an emphasis on high value/high risk and 

high volume/high maintenance items. In the case of high volume and the high maintenance 

items, the overall cost can be significantly reduced simply due to the extensive amount of time 

and resources it costs to maintain them. The Caterpillar C7 engine fits into the category of high 

maintenance and moderate-to-high volume, thereby making it a good candidate for study. In the 

spring and summer of 2010, the U.S. Army Tank and Automotive Research, Development and 

Engineering Center (TARDEC) performed “seeded fault” testing of the C7 engine in which a 

variety of operating parameters were perturbed. Seeded faults in this case describe these 

perturbations and, in most cases, have not been shown to actually degrade engine performance, 

nor to permanently damage the engine. 

This report is a continuation of previous work on fault detection and classification performed by 

ARL on the Caterpillar C7 engine (1). It adds to the previous work by including analysis using 

an autoassociative neural network (AANN) approach. The previous report examined detection 

and classification using Correlation Analysis and statistics from linear Principal Component 

Analysis (PCA). The PCA statistics, T-Square, and Square Prediction Error (SPE), showed good 

results, but we believed that improvements could be made because of the suspected nonlinearity 

in sensor correlations. The AANN approach is a proven way of implementing nonlinear PCA (3). 

The data used in this analysis is the same as that used in the PCA analysis of reference 1.

2. Experimental

A military version of the Caterpillar C7 diesel engine (Model C7 DITA) was installed and 

instrumented in a dynamometer (dyno) test cell at TARDEC’s facilities in Warren, MI (figure 1). 

The basics of the setup and data collected are described here; for a detailed description of the 

experiment, see reference 2. The test cell supported provision of fuel, coolant, inlet air, and 

exhausting of the engine, as well as a load (eddy current dyno, computer controlled). Data were 

collected from a variety of data acquisition systems. The data acquisition systems were 

coordinated in time and time-stamped, but generally operated autonomously from each other at 

different sampling rates and, therefore, required synchronization in data processing later on.

Sources for the data included existing engine sensors, test cell sensors, and a few installed 
2

sensors. The data from the existing sensors were extracted from the controller-area network 

(CAN) vehicle bus. The data from the test cell sensors included such items as exhaust 

temperature and were recorded by the cell data acquisition system (DAQ). The test cell data were 

recorded at close to the same rate as the CAN data. The data from the installed sensors were

recorded by a separate DAQ at a much higher rate and are referred to as “analog data”. A small 

portion of the data that is referred to as “digital data” is primarily used for timing. There were 

also sensors inserted and data collected by the Pennsylvania State University (Penn State) 

Applied Research Laboratory. Both the CAN and dyno data were collected at a relatively low 

rate and provided to ARL at 1 Sample/s, and could be monitored continuously during a test run. 

The analog data were collected at 10 kiloSamples/s and, due to the high rate, “snapshots” of data 

of between 1 and 30 s were collected at select times during a test run. The Penn State data were 

collected independently of the TARDEC data at 102.4 kiloSamples/s. 

Figure 1. Instrumented CAT 7 engine in the TARDEC test cell.

Test runs were performed with various seeded faults and no fault cases. A test run consisted of 

running the engine through a stepwise sequence of designated speeds for a short time at each 

speed, as shown in figure 2, all with either no fault or with a particular seeded fault. The engine 

speeds with associated duration were duplicated for all the tests. It should be noted that the time 

duration at a given speed set-point was not precisely controlled. 
3

Figure 2. Typical stepped control of engine speed for a performance run.

3. Data for Analysis

The data described here is identical to the data used in the PCA analysis of reference 1; it is 

repeated here for clarity. The focus of this analysis is on the performance test data since these 

files have several baseline runs, along with several seeded fault runs. Baseline runs were test 

sequences at the beginning of a test day in which there was no fault but the standard test 

sequence was followed, and as such are viewed as “healthy states.” Table 1 shows the 15 

baseline runs that were identified. For PCA and AANN-based methods, training data is required; 

the first column of table 1 shows the runs that were selected for training (50% of the runs, 

randomly selected). Table 2 shows the 33 seeded fault performance runs; however, three of these 

test runs are considered a baseline condition since their gain was set to 1.0, which is the nominal 

value. As a note, several files contained more than one run, where the additional runs were 

various levels of the same fault type.
4

Table 1. Baseline performance runs.

Table 2. Seeded fault performance runs.

From the data described, we determined to initially work with the 45 signals from the CAN and 

dyno. Working with this set of low-cost sensor and CAN bus signals provides a path for a 

practical onboard implementation, and thus, is conducted prior to considering vibration and other 

signals for developing health models. Since the signals were from different data acquisition 

Baseline Performance Test # Date MatLAB File Name Run # in File Train (0) or Test (1) 

Training 1 May 27, 2011 PerfM3_JP8_May27_ext 1 0

Training 2 May 27, 2011 PerfM3_JP8_May27_ext 2 0

Test 1 June 1, 2011 Perf_Jun1_ext 1 1

Training 3 June 3, 2011 Perfor_Jun3_ext 1 0

Training 4 June 8, 2011 Perfor_Jun8_par 1 0

Test 2 June 10, 2011 Perfor_Jun10_ext 1 1

Training 5 June 15, 2011 Perfor_Jun15_ext 1 0

Test 3 June 16, 2011 Perfor_Jun16_ext 1 1

Test 4 June 22, 2011 Perfor_C_Jun22_ext 1 1

Test 5 June 29, 2011 Perfor_jun29_ext 1 1

Test 6 July 1, 2011 Perf_Jul1_ext 1 1

Training 6 July 6, 2011 Perfor_Jul6_ext 1 0

Training 7 July 8, 2011 Perfor_Jul8_ext 1 0

Test 7 July 27, 2011 Perfor_Jul27_ext 1 1

Test 8 August 3, 2011 Perfor_ext3_ext 1 1

Test # Date MatLAB File Name Fault Type Run in File Severity

9 May 27, 2011 PerfM3_IntRestr_May27_ext IntakeAir Restric Test 1 Pos # 4

10 May 27, 2011 PerfM3_IntRestr_May27_ext IntakeAir Restric Test 2 Pos # 6

11 June 8, 2011 PerfM3_OilP_Jun8_par OilPress High Gain 1 Gain 1.0

12 June 8, 2011 PerfM3_OilP_Jun8_par OilPress High Gain 2 Gain 0.7

13 June 8, 2011 PerfM3_OilP_Jun8_par OilPress High Gain 3 Gain 1.3

14 June 10, 2011 PerfM3_AirChgT_Jun10_ext Air Charge Temperature Increase 1 Increased by 20oF

15 June 10, 2011 PerfM3_AirChgT_Jun10_ext Air Charge Temperature Increase 2 Increased by 30oF

16 June 10, 2011 PerfM3_AirChgT_Jun10_ext Air Charge Temperature Increase 3 Increased by 50oF

17 June 15, 2011 Perfor3_AirRestr_Jun15_ext AirRestriction Low 1 Pos # 2

18 June 15, 2011 Perfor3_AirRestr_Jun15_ext AirRestriction Low 2 Pos # 3

19 June 15, 2011 Perfor3_AirRestr_Jun15_ext AirRestriction Low 3 Pos # 4

20 June 15, 2011 Perfor3_B_AirRestr_Jun15_ext AirRestriction High 1 Pos #5

21 June 15, 2011 Perfor3_B_AirRestr_Jun15_ext AirRestriction High 2 Pos #6

22 June 15, 2011 Perfor3_C_AirChgT_high_Jun15_ext AirChgHigh 1

23 June 15, 2011 Perfor3_C_AirChgT_high_Jun15_ext AirChgHigh 2

24 June 16, 2011 PerforM3_AirChg_low_Jun16_ext AirCharge 1

25 June 16, 2011 PerforM3_AirChg_low_Jun16_ext AirCharge 2

26 June 16, 2011 PerforM3_AirChg_low_Jun16_ext AirCharge 3

27 June 29, 2011 PerfM3_B_AirIntRes_Jun29_ext IntRestriction 1 Pos #5

28 June 29, 2011 PerfM3_B_AirIntRes_Jun29_ext IntRestriction 2 Pos #6

29 June 29, 2011 PerfM3_B_AirIntRes_Jun29_ext IntRestriction 3 Pos #7

30 July 6, 2011 PerforM3_B_BoostG_Jul6_ext Boost 1 Gain 0.85

31 July 6, 2011 PerforM3_B_BoostG_Jul6_ext Boost 2 Gain 0.95

32 July 6, 2011 PerforM3_B_BoostG_Jul6_ext Boost 3 Gain 1.00

33 July 13, 2011 PerforM3_ExhRestr_Jul13_ext ExhRestr 1 60%

34 July 13, 2011 PerforM3_ExhRestr_Jul13_ext ExhRestr 2 55%

35 July 13, 2011 PerforM3_ExhRestr_Jul13_ext ExhRestr 3 50%

36 July 13, 2011 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 1 42%

37 July 13, 2011 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 2 46%

38 July 13, 2011 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 3 50%

39 August 3, 2011 PerforM3_InjPresG_ext3_ext InjPress 1 Gain 1.0

40 August 4, 2011 PerforM3_InjPresG_ext3_ext InjPress 2 Gain 0.9

41 August 5, 2011 PerforM3_InjPresG_ext3_ext InjPress 3 Gain 1.1
5

systems, they had to be aligned and interpolated to the same 1Sample/s acquisition rate, as well 

as reformatted to allow them to be processed together. The CAN and dyno signals are identified 

in table 3. The 32 signals highlighted in orange were used in the analysis. The other 13 signals 

were not included because they are either operating conditions or have a low amount of 

variability.

Table 3. Signals recorded from CAN and dyno.

The data was collected at steady state operating points in the performance runs. Consequently, 

for the analysis, the 32 signals highlighted in table 3 were divided into four operating regimes, as 

shown in table 4. To avoid transient effects, the first and last 20 s in a particular operating regime 

were not included in the calculations.

Table 4. Operating regimes for analysis.

Regime No Engine RPM Engine Load Pedal %

1 1620–1820 60–100 80–100

2 1820–2020 60–100 80–100

3 2020–2200 60–100 80–100

4 2220–2420 60–100 80–100

4. Analysis

AANN is an approach for performing nonlinear principal component analysis. Its usage in 

diagnostics and health monitoring has ranged from sensor diagnostics (4), to aircraft engine 

health monitoring (5), to diesel engine diagnostics (6). Some researchers classify the AANN 

method as a multivariate state estimation technique, since the AANN model provides an 
6

estimated sensor value for each sample. As shown in figure 3, a five-layer feed-forward network 

was employed. It is trained with the same targets and inputs, and is, thus, forced to try to produce 

the output by only using the small set of nodes in the bottleneck layer.

Figure 3. Neural net design. Every node connection is not shown for clarity.

The approach is similar to the PCA-based monitoring method in that both methods map the 

incoming data into a reduced number of components. The primary differences in AANN are the 

mapping/de-mapping functions, training, and the evaluation of health. For AANN, the sigmoid 

functions of the mapping and de-mapping layers can account for nonlinearities in the sensor 

correlations that are not possible in normal PCA. Also, while training of the PCA models

involved a matrix computation and is deterministic, the AANN model is trained using a back 

propagation algorithm that solves an optimization problem and is not deterministic. And finally, 

the assessment of health based on the AANN model involves an evaluation of the de-mapped 

reduced set, while PCA is concerned with the analysis of the reduced set, itself. To be more 

specific, the AANN model is derived by training the network using healthy data such that the 

output matches the input; any subsequent healthy data that the model is applied to should show a 

negligible difference in its output layer values. However, when the model is applied to faulted 

data, a difference in the output layer values is expected to appear. This difference is the basis for 

the health estimate of the AANN method. The AANN method is computationally much more 

time consuming than PCA. The steps in the process are listed as follows:

1. Select regime and signal subset (four regimes).

2. Specify a training data set, defined as a portion of the baseline/healthy data, and normalize 

the data by subtracting the mean and dividing by the standard deviation.
7

3. Train the AANN models for each regime.

4. Save the AANN models and normalization information.

5. Normalize all the senor data using the training normalization information.

6. Calculate the AANN-based SPE health value and the mean of these for each block of data

(6).

The network consisted of a 32 × 12 × f × 12 × 32 architecture. The same 32 senor signals were 

used in the PCA analysis of reference 1. The number of nodes, f, in the bottleneck layer was 

varied to evaluate the performance of the network by checking the health assessment results. The 

value of f was varied from 1 to 10; a bottleneck layer with f = 8 was compared to the PCA model 

of reference 1, since the PCA model had 7 to 8 principle components retained. The number of 

mapping and de-mapping nodes could have been varied, but were maintained at 12 because,

generally, the number of bottleneck nodes has more influence on the model performance (6). The 

network was trained with baseline data from each of the four operating regimes. More 

specifically, the training set was defined to be 50% of the baseline data, where a random number 

generator was used to select which runs were used. The default training settings in the Matlab 

neural network toolbox were used and appeared to work well; however, adjustment of these 

settings could be considered for future work.

To monitor engine health and to determine which sensors are contributing more to the degraded 

engine performance, the traditional AANN approach is used—that is, to monitor SPE and 

examine the residuals. The residuals, E, are calculated based on equation 1 and are the difference 

between the modeled and actual data values:

  xn  n  xn   xn

E x y 1

1

 1

, (1)

where n are the number signals,

  xn x 1

are the actual signal values, and 

  xn

y 1

are the modeled

values. SPE is the sum of the residuals squared (summed from residuals for each sensor), 

equation 2:





n

i

Ei

SPE

1

2

(2)

To evaluate health of the system based on the SPE, a threshold must be established above which 

the engine will be considered to be in a faulted state. A meaningful threshold can be obtained 

with the application of a receiver operating curve (ROC Curve). The ROC Curve is a common 

way of showing classification/detection results as a function of false positives and false negatives 

as a threshold is varied (7). Figures 4–7 show a series of ROC Curves for various values of the 

bottleneck nodes for each regime. Table 5 lists best performing node counts for each regime for a 

0% false alarm rate. The corresponding detection rate is also provided.
8

Figure 4. AANN SPE ROC curves for various bottleneck node counts for Regime 1.

Figure 5. AANN SPE ROC curves for various bottleneck node counts for Regime 2.
9

Figure 6. AANN SPE ROC curves for various bottleneck node counts for Regime 3.

Figure 7. AANN SPE ROC curves for various bottleneck node counts for Regime 4.
10

Table 5. AANN Optimum bottleneck node count and detection results (for 0% false positives).

Regime No Best node count, f False Positives (%) Detection Rate %

1 8 0 80

2 4 0 90

3 1–10 (except 5) * 0 83.3

4 9 0 90

*All, except 5, had an equal detection rate. 5 nodes gave a detection rate of 80%.

As is shown in the figures, all bottleneck node counts gave good detection rates for all regimes

(greater than 75% True Positives for 0 False Positives), except node counts of 2, 3, and 6 for 

regime 2. An additional measure of node count performance, calculating the Area under the ROC 

Curve for each node count, which spans the full trade space, was not performed because the lack 

of variation was obvious. A bottleneck layer node count of 8 provides good results for all 

regimes, 0 false positives with a detection rate of 80–83.3%. It also provides for a direct 

comparison to the PCA method used in reference 1. Eight PCs were selected in that analysis to 

produce a high-quality SPE statistic (85% of the variance was explained using eight PCs.) Table 

6 compares the results of PCA and AANN using a node count of 8 for all regimes. It is seen that 

there is very little difference between the detection rates for the AANN and PCA methods. 

Table 6. Detection rate comparison of AANN and PCA.

Regime No False Positives AANN Detection 

Rate

PCA Detection 

Rate %

1 0 80 77

2 0 83.3 80

3 0 83.3 86.6

4 0 83.3 83.3

Other results of interest include which sensors contributed most to particular faults and whether 

AANN could provide a measure of severity within a particular fault type. The contribution 

results are listed in table 7, where highest contributors are identified in the last 2 columns. The 

contribution information is of significant value if it is determined that the system should be 

monitored for a particular fault. Figures 8 and 9 are plots of air restriction and exhaust restriction 

faults at various levels. The figures indicate that AANN can provide a measure of fault severity.
11

Table 7. AANN Sensor-fault contribution results.

Figure 8. SPE health value for differing levels of air restriction (Regime 4).

 Test # MatLAB File Name Fault Type Severity Q Contribution 1 Q Contribution 2

9 PerfM3_IntRestr_May27_ext IntakeAir Restric Test Pos # 4 'T-OilGalley' 'T-ExhB4Turbo2'

10 PerfM3_IntRestr_May27_ext IntakeAir Restric Test Pos # 6 'ECM1-Boost' 'Sensor-Boost'

12 PerfM3_OilP_Jun8_par OilPress High Gain Gain 0.7 'ECM1-OilPres' 'EngOilP'

13 PerfM3_OilP_Jun8_par OilPress High Gain Gain 1.3 'ECM1-OilPres' 'EngOilP'

14 PerfM3_AirChgT_Jun10_ext AirCharge Temp high Shift Increased by 20oF 'IntManiAirT' 'Sensor-AirIntMani'

15 PerfM3_AirChgT_Jun10_ext AirCharge Temp high Shift Increased by 30oF 'IntManiAirT' 'Sensor-AirIntMani'

16 PerfM3_AirChgT_Jun10_ext AirCharge Temp high Shift Increased by 50oF 'IntManiAirT' 'Sensor-AirIntMani'

17 Perfor3_AirRestr_Jun15_ext AirRestriction Low Pos # 2 'InjCtrlP' 'Boost'

18 Perfor3_AirRestr_Jun15_ext AirRestriction Low Pos # 3 'Torque' 'P-ExhB4Turbo2'

19 Perfor3_AirRestr_Jun15_ext AirRestriction Low Pos # 4 'P-ExhB4Turbo2' 'AirFlow'

20 Perfor3_B_AirRestr_Jun15_ext AirRestriction High Pos #5 'T-OilGalley' 'P-AirB4Mani'

21 Perfor3_B_AirRestr_Jun15_ext AirRestriction High Pos #6 'ECM1-Boost' 'Sensor-Boost'

22 Perfor3_C_AirChgT_high_Jun15_ext AirChgHigh 'IntManiAirT' 'Sensor-AirIntMani'

23 Perfor3_C_AirChgT_high_Jun15_ext AirChgHigh 'Sensor-AirIntMani' 'ECM1-AirIntMani'

24 PerforM3_AirChg_low_Jun16_ext AirCharge 'T-IntAirMani' 'ECM1-AirIntMani'

25 PerforM3_AirChg_low_Jun16_ext AirCharge 'ECM1-AirIntMani' 'IntManiAirT'

26 PerforM3_AirChg_low_Jun16_ext AirCharge 'P-ExhB4Turbo2' 'Boost'

27 PerfM3_B_AirIntRes_Jun29_ext IntRestriction Pos #5 'P-ExhB4Turbo2' 'AirFlow'

28 PerfM3_B_AirIntRes_Jun29_ext IntRestriction Pos #6 'P-ExhB4Turbo2' 'AirFlow'

29 PerfM3_B_AirIntRes_Jun29_ext IntRestriction Pos # 7 'Sensor-Boost' 'ECM1-Boost'

30 PerforM3_B_BoostG_Jul6_ext Boost Gain 0.85 'ECM1-Boost' 'Boost'

31 PerforM3_B_BoostG_Jul6_ext Boost Gain 0.95 'Sensor-Boost' 'P-ExhB4Turbo2'

33 PerforM3_ExhRestr_Jul13_ext ExhRestr 60% 'P-ExhStack' 'T-ExhStack'

34 PerforM3_ExhRestr_Jul13_ext ExhRestr 55% 'P-ExhStack' 'P-ExhB4Turbo2'

35 PerforM3_ExhRestr_Jul13_ext ExhRestr 50% 'P-ExhStack' 'T-ExhStack'

36 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 42% 'P-ExhStack' 'ECM1-Boost'

37 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 46% 'P-ExhStack' 'T-ExhStack'

38 PerforM3_B_ExhRestr_Jul13_ext ExhRestr 50% 'P-ExhStack' 'T-ExhStack'

40 PerforM3_InjPresG_ext3_ext InjPress Gain 0.9 'Sensor-InjPres' 'T-ExhB4Turbo2'

41 PerforM3_InjPresG_ext3_ext InjPress Gain 1.1 'Sensor-InjPres' 'T-ExhB4Turbo2'
12

Figure 9. SPE health value for differing levels of exhaust restriction (Regime 4).

5. Discussion

The AANN monitoring method can be considered a nonlinear version of the PCA method. This 

method was examined because of the suspected nonlinearity in sensor correlations, and, hence,

its potential benefit. However, very little difference is seen in the detection rate between AANN 

and PCA. The sensor correlations are still believed to be nonlinear; however, the explanation for 

lack of benefit using AANN is that the “Performance” test data was acquired and analyzed at 

discrete set-points in vehicle operation. Consequently, the sensor correlations are likely linear

within these regimes and the benefit of using AANN was not realized. Regarding network 

architecture and analysis, the selection of the number of nodes in the mapping/de-mapping and 

bottleneck layers are model parameters that can be varied to optimize the AANN monitoring 

method. In general the mapping/de-mapping layer nodes have less of an effect than bottleneck 

layer nodes (6), and were not varied because it was seen that the number of bottleneck layer 

nodes had little effect on the results. However, one of the drawbacks of using the AANN method

is that it is difficult to generalize on what are the optimum number of nodes in the bottleneck 

layer and in the mapping and de-mapping layers.
13

6. Conclusion and Recommendations

The AANN results for the detection rate are very good, and the fault severity corresponded with 

the SPE health value. The results for AANN and normal PCA (linear) were quite similar, with an 

almost identical false positive and detection rate. Because AANN is much more computationally 

intensive, we concluded that for the data set analyzed, PCA statistics provide a better method of 

obtaining health values. A potential option for future work would be to compare PCA and 

AANN using the “MiniMap” series of runs, which is more suitable to analyze as a single data set 

without separation into regimes. In this case, any correlation nonlinearities should be “active”

and the AANN method would be expected to perform better than PCA. Finally, as in the 

previous report ARL-TR-5677, reference 1, it should be pointed out that the engine testing was 

not seeded fault testing in a traditional sense; that is, the “seeded faults” were temporary 

perturbations in operating conditions and were not known to cause degradation in engine 

performance (let alone permanent damage). Consequently, what is “detected” in this work are 

these perturbations; additionally, the missed detections are at the lower levels of these 

perturbations. With this in mind, both the PCA and AANN analysis should be considered as 

performing very well.
14

7. References

1. Bayba, A.; Siegel, D.; Tom, K.; Ly, C. Approaches to Health Monitoring of the CAT 7 

Diesel Engine; ARL-TR-5677; U.S. Army Research Laboratory: Adelphi, MD, September

2011.

2. MIS 2000, Final Report, A Dynamic Neural Network Approach to CBM, Cooperative 

Agreement W911-09-2-0036.

3. Kramer, M. A. Nonlinear Principal Component Analysis Using Autoassociative Neural 

Networks. AIChe Journal, 37 (2), 233–243.

4. Hines, J. W.; Uhrig, R. E. Use of Autoassociative Neural Networks for Signal Validation. 

Journal of Intelligent and Robotic Systems 1997, 21, 143–154.

5. Hu, X.; Qiu, H.; Iyer, N. Multivariate Change Detection for Time Series Data in Aircraft 

Engine Fault Diagnostics. IEEE Proceedings of Systems, Man and Cybernetics, 2007.

6. Antory, D. Application of a Data-driven Monitoring Technique to Diagnose Air Leaks in an 

Automotive Diesel Engine: A Case Study. Mechanical Systems and Signal Processing 2007, 

21 (2), 795–808.

6. Hines, J. W.; Garvey, D.; Seibert, R.; Usyin, A. Technical Review of On-line Monitoring 

Techniques for Performance Assessment: volume 2: Theoretical Issues, U.S NRC, 2008.

7. Zweig, M.; Campbell, G. Receiver-operating Characteristics (ROC) Plots: A Fundamental 

Evaluation Tool in Clinical Medicine. Clinical Chemistry, 39 (4), 561–577.
15

List of Symbols, Abbreviations, and Acronyms

AANN autoassociative neural network

ARL U.S. Army Research Laboratory

CAN controller-area network

DAQ data acquisition system

Dyno dynamometer

PCA Principal Component Analysis

ROC Curve receiver operating curve

SPE Square Prediction Error

TARDEC U.S. Army Tank and Automotive Research, Development and Engineering Center
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