Irebert R. Delgado and Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Lance J. Antolick

RMCI, Inc., Redstone Arsenal, Alabama 

Daniel R. Wade

U.S. Army Aviation Engineering Directorate, Redstone Arsenal, Alabama

Continued Evaluation of Gear Condition 

Indicator Performance on Rotorcraft Fleet

NASA/TM—2013-217876

June 2013
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Irebert R. Delgado and Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Lance J. Antolick

RMCI, Inc., Redstone Arsenal, Alabama 

Daniel R. Wade

U.S. Army Aviation Engineering Directorate, Redstone Arsenal, Alabama

Continued Evaluation of Gear Condition 

Indicator Performance on Rotorcraft Fleet

NASA/TM—2013-217876

June 2013

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

Airworthiness, Condition Based Maintenance (CBM), and Health and Usage Monitoring (HUMS)

sponsored by the Helicopter Association International (HAI)

Huntsville, Alabama, February 11–13, 2013
Acknowledgments

The authors would like to thank the NASA Rotary Wing Project, the U.S. Army Apache PM and AMCOM G-3 CBM office 

located at Redstone Arsenal and Traci Stadtmueller of the FAA Technical Center for their assistance and support of this work.

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NASA/TM—2013-217876 1

Continued Evaluation of Gear Condition Indicator

Performance on Rotorcraft Fleet

Irebert R. Delgado and Paula J. Dempsey

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Lance J. Antolick

RMCI, Inc.

Redstone Arsenal, Alabama 35808

Daniel R. Wade

U.S. Army Aviation Engineering Directorate

Redstone Arsenal, Alabama 35808

Abstract

This paper details analyses of condition indicator performance for the helicopter nose gearbox within 

the U.S. Army’s Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two 

specific analyses. A mean condition indicator level analysis was performed where condition indicator

performance was based on a ‘batting average’ measured before and after part replacement. Two specific 

condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data 

sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear 

photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 

1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators 

Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of 

micropitting and spalling. Input pinion nose gear box condition indicators were found to be more 

responsive to part replacement during overhaul than their corresponding output gear nose gear box

condition indicators.

Acronyms

AEA Army Engineering Analysis

CBM Condition-Based Maintenance

CI Condition Indicator

DA1 Condition Indicator: Diagnostic Algorithm 1

FM0 Condition Indicator: Zero Order Figure of Merit

FM4 Condition Indicator: Figure of Merit 4

NGB Nose Gearbox

RMS root-mean square

SI Condition Indicator: Sideband Index

TSA Time-Synchronous Averaged Data

TDA Tear-Down Analysis
NASA/TM—2013-217876 2

Introduction

This paper further analyzes condition indicator (CI) performance based on the U.S. Army helicopter 

nose gearbox (NGB) vibration data as previously reported by Antolick et al. (Ref. 1). Antolick and others 

reviewed several helicopter NGB data sets with known gear damage against several gear specific 

condition indicators. A ‘batting average’ was used to assess condition indicator performance in which the 

total number of true negatives and true positives were divided by the total number of available data 

points. The purpose of this analysis was to correlate CI performance to available tear-down analysis

(TDA) and Army Engineering Analysis (AEA) wear data and to evaluate how specific CIs respond to part 

replacement. CIs are used in the Army’s Condition-Based Maintenance (CBM) Program to monitor 

helicopter transmissions and are required to be compliant with U.S. Army ADS-79C-HDBK (Ref. 2). The 

handbook prescribes required detection rates for CIs. This analysis evaluates current CIs used to monitor 

U.S. Army helicopter NGBs and is based, in part, on the average CI value both before and after part 

replacement. The analysis also takes a closer look at TDAs in relation to gear damage as noted per

ANSI/AGMA 1010-E95 Standards (Ref. 3). A total of seven NGB data sets were used in the CI versus 

gear wear analysis.

Figure 1.—U.S. Army Helicopter with white circle showing nose gearbox location (Ref. 4).
NASA/TM—2013-217876 3

Figure 2.—U.S. Army Helicopter Nose Gearbox showing input pinion and output gear locations (Ref. 5).

Methodology and Results

Dataset Down-Select

An initial pool of 20 data sets was reviewed and reduced to 10 data sets due to insufficient CI data or 

lack of TDA photographs. Data sets selected for the study are shown in Table 1. Due to data availability 

limitations, some data sets were used for either the Mean CI analysis or CI versus gear wear analysis as 

described below. Seven data sets were used for both analyses.

TABLE 1.—HELICOPTER NGB DATA SETS 

USED IN MEAN CI AND TDA ANALYSIS.

NGB

designation

Mean CI level 

analysis

CI versus gear 

wear analysis

01 Y Y

02 Y Y

03 Y N

04 Y Y

05 Y Y

06 Y Y

07 Y Y

08 Y N

09 Y N

10 Y Y
NASA/TM—2013-217876 4

Condition Indicator Descriptions

Condition indicators FM0 (Ref. 6), FM4 (Ref. 1), SI (Ref. 7), and DA1 (Ref. 1) were used in the 

analysis.

• FM0 is the peak-to-peak value of the time synchronous averaged (TSA) data normalized by the sum 

of the spectral components of the TSA at the regular gear meshing frequencies.

∑ ( ) =

= n

i i A f

PP

1

FM0 (1)

where: PP = peak-to-peak level of signal average

A = amplitude at mesh frequency (i=1_ and harmonics (i>1 )

• FM4 is calculated from TSA data with filtering performed in the frequency domain on the TSA.

FM4 = 

( )

2

1

2

1

4

( ) 1

1















 −

−

∑

∑

=

=

N

n

n

N

n

n

d d

N

d d

N

(2)

where: d = difference signal

d = mean value of difference signal

• Sideband index (SI) is a measure of local gear faults and is defined as the average of the first order 

sidebands of the fundamental gear meshing frequency. The increase in magnitude of the sidebands of 

the fundamental gear meshing frequency drives this CI and indicates a local fault.

( ) ( )

2

SI , 1 , 1 R x R x sb

I

sb

I − + + = (3)

where: R = regular meshing components

sb = sideband

• DA1 is the RMS of the TSA subtracted from the mean of the synchronous time average. DA1 detects 

the overall energy increase in the signal indicating a distributed gear fault.

DA1 = RMS(TSA−TSA) (4)

Statistical Analysis Based on Mean CI Levels

Two statistics were used to assess CI performance:

∆mean = CI pre−replacement −CI post−replacement (5)















 − ∆ = −

− −

pre replacement

pre replacement post replacement % mean 100

CI

CI CI

(6)
NASA/TM—2013-217876 5

A positive value in ∆mean was marked green (e.g., good part indication). A negative value was marked 

red (e.g., bad part indication). The percent difference in the mean, %∆mean, was evaluated at the 25 and 

50 percent level. Percentages exceeding these arbitrary minimum values were marked green, respectively.

Based on the data set analyzed thus far an initial uncertainty of ±10 percent is given on the CI data until 

more data sets can be analyzed. These data points are marked as orange. Values not meeting these 

baseline percentages and above the 10 percent uncertainty range were marked yellow (e.g., marginal).

Percentages that were negative and exceeded the 10 percent uncertainty range were marked red. Figure 3

gives an example of this mean CI analysis for condition indicator, input pinion SI, NGB Tail 04. This 

graphical result is repeated 80 times for each CI, input pinion/output gear, NGB Tail combination and is 

tabulated in Table 2 and Table 3. Further, the CIs were ranked in terms of ‘batting average’ similar to 

Antolick et.al. (Ref. 1) as follows:

Batting Average = [ ( )]

[#Green #Yellow #Red]

#Green 2

#Yellow

+ +

+

(7)

A summary is shown in Table 4. On the input pinion side, DA1 and SI performed best in all three 

statistical measures: ∆mean, ∆mean(25 percent), and ∆mean(50 percent). Both DA1 and SI condition 

indicators performed relatively well on the output gear side. Input pinion FM4 performed the worst. FM4 

again performed poorly on the output gear side. FM0 results were mixed. On the input pinion side, with 

the exception of FM4, the CIs performed better than their output gear side counter-parts. Based on this 

analysis DA1 and SI were used in the subsequent CI versus gear wear analysis.

Figure 3.—Example mean CI analysis result for NGB Tail 04, Input Pinion SI.
NASA/TM—2013-217876 6

TABLE 2.—MEAN CI ANALYSIS RESULTS: TAILS 01 TO 06

Tail Time period Statistic In_DA1 In_FM0 In_SI In_FM4 Out_DA1 Out_FM0 Out_SI Out_FM4

01

Pre

Replacement

min 8.31 4.48 1.79 2.02 6.98 3.83 1.39 2.46

max 54.06 14.29 8.00 3.00 54.67 9.09 10.79 3.30

mean 21.72 5.94 4.26 2.56 21.80 5.50 4.76 2.93

Post

Replacement

min 0.42 3.67 0.07 2.02 0.36 3.40 0.05 2.38

max 25.38 19.58 1.70 3.61 24.09 31.72 1.28 3.43

mean 5.99 6.91 0.75 2.55 4.39 6.72 0.66 2.84

∆ mean 15.72 –0.97 3.50 0.01 17.40 –1.23 4.10 0.09

%∆ mean (25%) 72.41 –16.37 82.31 0.53 79.84 –22.32 86.08 3.02

%∆ mean (50%) 72.41 –16.37 82.31 0.53 79.84 –22.32 86.08 3.02

02

Pre

Replacement

min 0.01 3.19 0.00 2.47 0.03 3.22 0.00 2.36

max 90.39 25.95 7.13 4.02 89.62 82.23 5.02 3.68

mean 66.98 4.53 4.33 2.83 66.66 6.61 4.17 3.01

Post

Replacement

min 0.57 3.34 0.15 2.20 0.77 3.19 0.13 2.50

max 18.97 15.74 2.30 4.05 17.61 47.55 2.31 4.49

mean 12.18 4.74 1.39 2.91 11.13 4.35 1.35 3.22

∆ mean 54.80 –0.20 2.94 –0.08 55.53 2.27 2.82 –0.22

%∆ mean (25%) 81.82 –4.45 67.97 –2.87 83.31 34.28 67.65 –7.19

%∆ mean (50%) 81.82 –4.45 67.97 –2.87 83.31 34.28 67.65 –7.19

03

Pre

Replacement

min 31.09 4.42 3.85 2.38 24.69 3.17 1.40 2.66

max 55.46 6.14 7.22 2.92 45.65 4.34 2.54 4.43

mean 38.39 5.16 5.02 2.58 30.32 3.64 1.99 3.06

Post

Replacement

min 3.96 3.13 0.30 2.13 5.20 3.24 0.68 2.48

max 50.61 9.88 1.75 3.69 50.41 11.24 2.80 3.33

mean 11.77 4.88 0.80 2.77 12.37 5.88 1.29 2.78

∆ mean 26.61 0.28 4.22 –0.19 17.95 –2.25 0.71 0.28

%∆ mean (25%) 69.33 5.34 83.98 –7.37 59.21 –61.82 35.41 9.21

%∆ mean (50%) 69.33 5.34 83.98 –7.37 59.21 –61.82 35.41 9.21

04

Pre

Replacement

min 18.79 4.10 3.29 2.41 18.70 3.79 2.25 1.89

max 42.66 6.42 5.92 3.62 42.18 7.95 3.60 2.80

mean 36.10 4.61 4.09 2.98 34.60 4.48 2.83 2.51

Post

Replacement

min 2.82 3.07 0.31 2.18 2.29 3.40 0.25 2.47

max 58.21 7.71 2.02 4.33 57.35 17.22 4.62 4.55

mean 13.48 4.05 0.99 2.80 14.45 5.55 1.93 3.03

∆ mean 22.63 0.55 3.10 0.19 20.14 –1.07 0.89 –0.51

%∆ mean (25%) 62.67 12.01 75.85 6.24 58.22 –23.79 31.61 –20.45

%∆ mean (50%) 62.67 12.01 75.85 6.24 58.22 –23.79 31.61 –20.45

05

Pre

Replacement

min 5.23 3.32 0.69 2.26 5.59 3.41 0.86 2.38

max 49.40 5.49 6.09 3.57 46.04 9.00 4.20 3.41

mean 13.35 4.16 1.48 2.80 13.26 4.50 1.64 2.89

Post

Replacement

min 18.30 2.87 0.38 2.12 18.79 3.22 0.56 1.96

max 69.34 3.42 1.52 4.33 75.30 4.99 3.71 3.64

mean 29.76 3.10 0.84 2.91 31.59 4.07 1.86 2.47

∆ mean –16.40 1.06 0.63 –0.12 –18.34 0.43 –0.22 0.41

%∆ mean (25%) –122.81 25.48 42.92 –4.12 –138.32 9.56 –13.62 14.27

%∆ mean (50%) –122.81 25.48 42.92 –4.12 –138.32 9.56 –13.62 14.27

06

Pre

Replacement

min 8.19 5.42 1.58 2.00 11.26 4.73 0.59 2.12

max 156.15 38.83 14.75 3.59 47.67 58.20 4.20 4.94

mean 81.17 18.08 6.89 2.64 28.72 9.53 2.07 3.04

Post

Replacement

min 10.88 3.67 1.10 1.95 9.22 3.64 1.97 2.83

max 30.97 6.41 1.53 2.27 32.55 6.41 3.45 3.27

mean 20.93 5.04 1.32 2.11 20.88 5.03 2.71 3.05

∆ mean 60.24 13.04 5.57 0.53 7.84 4.51 –0.64 –0.01

%∆ mean (25%) 74.22 72.10 80.87 20.04 27.29 47.25 –30.74 –0.46

%∆ mean (50%) 74.22 72.10 80.87 20.04 27.29 47.25 –30.74 –0.46
NASA/TM—2013-217876 7

TABLE 3.—MEAN CI ANALYSIS RESULTS TAILS 07 TO 10 WITH BATTING AVERAGE CALCULATION

Tail Time period Statistic In_DA1 In_FM0 In_SI In_FM4 Out_DA1 Out_FM0 Out_SI Out_FM4

07

Pre

Replacement

min 7.09 4.06 0.42 2.11 6.27 3.62 0.78 2.41

max 27.60 14.98 3.19 3.25 22.85 9.60 2.45 3.54

mean 14.04 5.98 1.17 2.66 12.11 5.07 1.36 2.86

Post

Replacement

min 5.48 3.61 0.27 2.46 5.94 4.09 0.49 2.56

max 17.44 15.97 1.30 4.09 18.21 20.14 2.61 3.49

mean 8.92 7.32 0.55 3.03 10.07 9.24 1.05 2.97

∆ mean 5.13 –1.34 0.62 –0.37 2.04 –4.17 0.30 –0.11

%∆ mean (25%) 36.50 –22.35 52.95 –13.75 16.83 –82.18 22.45 –3.75

%∆ mean (50%) 36.50 –22.35 52.95 –13.75 16.83 –82.18 22.45 –3.75

08 Pre

Replacement

min 0.02 4.43 0.00 2.46 0.03 4.80 0.00 2.71

max 13.98 6.58 1.15 3.64 13.83 10.05 1.68 3.08

mean 8.38 5.53 0.82 2.81 8.78 6.87 1.14 2.95

Post

Replacement

min 0.31 2.83 0.08 2.23 0.40 3.11 0.05 2.34

max 58.81 28.85 3.81 4.70 56.87 40.84 4.58 3.74

mean 22.79 3.62 1.39 2.94 23.71 4.55 2.60 2.84

∆ mean –14.41 1.92 –0.57 –0.13 –14.94 2.32 –1.46 0.11

%∆ mean (25%) –172.00 34.67 –70.43 –4.60 –170.15 33.80 –128.03 3.60

%∆ mean (50%) –172.00 34.67 –70.43 –4.60 –170.15 33.80 –128.03 3.60

09

Pre

Replacement

min 0.57 3.02 0.17 1.80 0.68 3.07 0.09 2.06

max 52.51 31.30 3.53 4.49 51.59 21.45 3.00 3.55

mean 17.15 4.48 1.06 2.40 17.11 4.82 1.89 2.84

Post

Replacement

min 1.37 2.71 0.20 2.16 0.99 3.06 0.19 2.41

max 39.49 13.40 2.00 3.87 40.64 13.62 3.99 3.77

mean 19.53 3.52 1.00 2.76 20.69 4.17 2.49 2.85

∆ mean –2.38 0.96 0.06 –0.36 –3.58 0.65 –0.60 0.00

%∆ mean (25%) –13.86 21.40 5.60 –15.12 –20.95 13.56 –31.86 –0.09

%∆ mean (50%) –13.86 21.40 5.60 –15.12 –20.95 13.56 –31.86 –0.09

10

Pre

Replacement

min 10.62 2.98 0.75 2.14 9.44 3.39 2.24 2.35

max 70.44 8.35 6.40 3.93 71.42 16.59 11.51 4.16

mean 25.26 4.35 3.09 2.86 26.10 4.90 4.48 2.93

Post

Replacement

min 3.48 2.86 0.23 2.11 5.99 3.08 0.42 2.44

max 85.19 11.27 2.01 3.99 88.11 21.77 5.28 3.73

mean 12.87 4.61 0.75 2.76 15.05 7.24 2.12 2.90

∆ mean 12.38 –0.27 2.34 0.10 11.05 –2.34 2.37 0.03

%∆ mean (25%) 49.03 –6.17 75.73 3.62 42.32 –47.74 52.82 1.10

%∆ mean (50%) 49.03 –6.17 75.73 3.62 42.32 –47.74 52.82 1.10

∆ mean

Green 7 5 8 1 7 4 6 1

Orange 0 3 1 6 0 1 0 8

Yellow 0 0 0 0 0 0 0 0

Red 3 2 1 3 3 5 4 1

Percent 0.700 0.500 0.800 0.100 0.700 0.400 0.600 0.100

%∆ mean(25%)

Green 7 3 8 0 6 3 5 0

Orange 0 3 1 6 0 1 0 8

Yellow 0 2 0 1 1 1 1 1

Red 3 2 1 3 3 5 4 1

Percent 0.700 0.400 0.800 0.050 0.650 0.350 0.550 0.050

%∆ mean(50%)

Green 5 1 7 0 4 0 3 0

Orange 0 3 1 6 0 1 0 8

Yellow 2 4 1 1 3 4 3 1

Red 3 2 1 3 3 5 4 1

Percent 0.600 0.300 0.750 0.050 0.550 0.200 0.450 0.050
NASA/TM—2013-217876 8

TABLE 4.—RANK SUMMARY (BATTING AVERAGE) OF

THEPERFORMANCE OF 4 CIS BASED 

ON STATISTICAL MEAN LEVELS

Rank summary

Input gear CIs

DA1 FM0 SI FM4

∆mean 0.700 0.500 0.800 0.100

%∆mean (25%) 0.700 0.400 0.800 0.050

%∆mean (50%) 0.600 0.300 0.750 0.050

Output gear CIs

DA1 FM0 SI FM4

∆mean 0.700 0.400 0.600 0.100

%∆mean (25%) 0.650 0.350 0.550 0.050

%∆mean (50%) 0.550 0.200 0.450 0.050

Standard Gear Wear Terminology

U.S. Army documents give specific maintenance procedures for the helicopter NGB. Specific 

inspection instructions during NGB teardown for the input pinion and output gear shaft teeth require ‘no 

pitting, scuffing, scoring, metal flow, or measurable wear steps.’ Also, inspection definitions are given on 

pitting, scoring, and wear. Pitting is described as ‘small indentations in a surface.’ Army TDAs provide 

both descriptive and photo documentation of gear wear during NGB tear-down. However, some 

inconsistencies were observed in describing the gear damage seen from one TDA report to another.

Standardizing the description of gear wear would provide a number of advantages to the CBM 

community:

1. Provide data on component performance with respect to the specific way the gear failed (e.g., 

wear, scuffing, plastic deformation, contact fatigue, cracking, fracture, bending fatigue).

2. Provide data on CI performance.

3. May provide insight on improving overall helicopter safety, maintenance practices, and 

performance.

Describing the gear wear from Army TDAs using the terminology from the ANSI/AGMA 101-E95

standard, Table 5, is one specific method of standardizing the gear wear descriptions. Gear pitting per the 

AGMA standard is classified as contact fatigue which is subdivided into three general modes: pitting 

(macropitting), micropitting, and subcase fatigue. Macropitting is further divided into specific modes or 

degrees, including initial pitting, progressive pitting, flaking, and spalling. This is shown in Figure 4.

Further definitions for the specific mode or degree of pitting can provide clues to the gear inspector 

responsible for classifying the type of gear failure. Definitions are given as follows per ANSI/AGMA 

1010-E95, with example photographs, in Figure 5 through Figure 10.

• Initial Pitting—Small pits less than 1 mm in diameter. They occur in localized areas and tend to 

redistribute the load by removing high asperities.

• Progressive Pitting—Characterized by pits significantly larger than 1 mm in diameter. Pitting of 

this type may continue at an increased rate until a significant portion of the tooth surface has pits 

of various shapes and sizes.

• Flake Pitting—Pits that are relatively shallow but large in area. The fatigue crack extends from an 

origin at the surface of the tooth in a fan shaped manner until thin flakes of material break out and 

form a triangular crater. 

• Spalling—Progressive pitting where pits coalesce and form irregular craters that cover a 

significant area of the tooth surface.
NASA/TM—2013-217876 9

• Micropitting—Gear surface appears frosted, matted, or gray stained. Under magnification, the 

surface appears to be covered by very fine pits.

• Subcase Fatigue—Origin of the fatigue crack is below the surface of the gear teeth in the 

transition zone between the case and core. Fatigue beach marks may be evident on the crater 

bottom formed by propagation of the main crack.

Depending on material, processing, use, etc., pitting damage may not appear exactly as described

above. A database of typical pitting damage should be considered for the gear in question and used in 

place of the sample descriptions noted above. 

Figure 4.—Example gear failure mode nomenclature: Contact Fatigue.

Figure 5.—Example of Initial Pitting. Extracted from ANSI/AGMA 1010-E95, Appearance 

of Gear Teeth Terminology of Wear and Failure, with the permission of the publisher, 

the American Gear Manufacturers Association, 1500 King Street, Suite 201, Alexandria, 

Virginia 22314.

Specific 

mode or 

degree

General 

mode Class

Contact 

Fatigue

Pitting

(Macropitting)

Initial

Progressive

Flake

Spall

Micropitting

Subcase 

fatigue
NASA/TM—2013-217876 10

Figure 6.—Example of Progressive Pitting. Extracted from ANSI/AGMA 1010-E95, 

Appearance of Gear Teeth Terminology of Wear and Failure, with the permission of the 

publisher, the American Gear Manufacturers Association, 1500 King Street, Suite 201, 

Alexandria, Virginia 22314.

Figure 7.—Example of Flake Pitting. Extracted from ANSI/AGMA 1010-E95, Appearance 

of Gear Teeth Terminology of Wear and Failure, with the permission of the publisher, 

the American Gear Manufacturers Association, 1500 King Street, Suite 201, Alexandria, 

Virginia 22314.
NASA/TM—2013-217876 11

Figure 8.—Example of Spalling. Extracted from ANSI/AGMA 1010-E95, Appearance of 

Gear Teeth Terminology of Wear and Failure, with the permission of the publisher, the 

American Gear Manufacturers Association, 1500 King Street, Suite 201, Alexandria, 

Virginia 22314.

Figure 9.—Example of Micropitting. Extracted from ANSI/AGMA 1010-E95, Appearance 

of Gear Teeth Terminology of Wear and Failure, with the permission of the publisher, 

the American Gear Manufacturers Association, 1500 King Street, Suite 201, Alexandria, 

Virginia 22314.
NASA/TM—2013-217876 12

Figure 10.—Example of Subcase Fatigue. Extracted from ANSI/AGMA 1010-E95, 

Appearance of Gear Teeth Terminology of Wear and Failure, with the permission of the 

publisher, the American Gear Manufacturers Association, 1500 King Street, Suite 201, 

Alexandria, Virginia 22314.

Consider the following spiral bevel gear wear description from an Army tear-down analysis (TDA) 

Report.

“The input gear and the output gear teeth both showed abnormal wear. Not only did they show 

abnormal wear but it was uneven wear and bluing that appears to be from excessive heating. The 

input gear was marked with ... and is shown in Figures 6.3-1 thru 6.3-6. Figure 6.3-1 is an overall 

picture, Figures 6.3-2 to 6.3-5 show the uneven wear and Figure 6.3-6 is a close up of the teeth. 

The output gear was marked on one side with ... and is shown in Figures 6.3-7 thru 6.3-21. 

Figure 6.3-7 is an overall view, Figures 6.3-8 to 6.3-12 shows the uneven wear, Figures 6.3-13, 

14, and 15 are close ups, Figures 6.3-16 to 6.3-20 are taken under a low power microscope, and 

Figure 6.3-21 shows the heating on the gear. DMWR 1-3010-204 Page 0057 00-6 shows the 

loaded contact pattern limits. The splines on the output gear also showed wear and is shown in 

Figures 6.3-22 and 6.3-23. A Non Destructive Inspection (NDI) of the output gear did not show 

evidence of cracks.”

Figure 6.3-16 of the TDA shown below as Figure 11 could be further classified, at least on one of the 

output gear teeth, as either progressive pitting or micropitting. A dimension marker on the original wear 

photo would aid in better estimating damage feature dimensions. Further, the use of a numbering scheme 

could streamline the identification of gear damage particularly during tear-down analyses at a U.S. Army 
NASA/TM—2013-217876 13

Engine Overhaul Depot. Consider the following numbering scheme for Table 5 shown in Table 6 which is 

similar to a coding scheme for a work breakdown structure. The Class of gear mode failure represents the 

top of the hierarchy. The General Mode and Specific Mode or Degree represents subsequent sub-classes.

Thus, the progressive pitting example from Figure 11 can be re-categorized as 4.3.2. Performing TDA 

gear wear descriptions using this classification system could streamline and standardize analyses. Another 

potential advantage of using this system is further refinement of failure prediction algorithms.

Figure 11.—Output gear damage—progressive pitting.
NASA/TM—2013-217876 14

TABLE 5.—NOMENCLATURE OF GEAR FAILURE MODES (ANSI/AGMA 1010-E95)

Class General mode Specific mode or degree Not preferred

Wear Adhesion Mild Running-in wear

Moderate 

Severe (see scuffing) Scoring

Scratching

Abrasion Mild, moderate, severe Cutting 

Burnishing

Polishing Mild, moderate, severe

Corrosion

Fretting corrosion

Scaling

Cavitation

Erosion

Electrical discharge

Ripple

Scuffing Scuffing Mild, moderate, severe Scoring

Cold scuffing

Hot scuffing

Welding

Galling

Seizing

Plastic deformation Plastic deformation Indentation Bruising 

Peening

Denting

Brinelling

Cold flow Permanent deformation

Hot flow Overheating

Rolling

Tooth hammer

Rippling Fish scaling

Ridging

Burr

Root fillet yielding

Tip-to-root interference

Contact fatigue Pitting (macropitting) Initial 

Progressive Destructive

Flake Arrow head

Spall

Micropitting Frosting

Gray staining

Peeling

Subcase fatigue Case crushing

Cracking Hardening cracks Quenching cracks

Grinding cracks

Rim and web cracks

Case/core separation Internal rupture

Fatigue cracks

Fracture Brittle fracture Fast fracture

Ductile fracture Smearing

Mixed mode fracture Semi-brittle

Tooth shear

Fracture after plastic deformation

Bending fatigue Low-cycle fatigue

High-cycle fatigue Root fillet cracks

Profile cracks

Tooth end cracks
NASA/TM—2013-217876 15

TABLE 6.—NUMBERING SCHEME FOR NOMENCLATURE 

OF GEAR FAILURE MODES (ANSI/AGMA 1010-E95)

Class General mode Specific mode or degree

1.0 Wear

1.1 Adhesion

1.1.1 Mild 

1.1.2 Moderate 

1.1.3 Severe (see scuffing) 

1.2 Abrasion

1.2.1 Mild 

1.2.2 Moderate 

1.2.3 Severe 

1.3 Polishing

1.3.1 Mild 

1.3.2 Moderate 

1.3.3 Severe 

1.4 Corrosion 

1.5 Fretting corrosion

1.6 Scaling 

1.7 Cavitation

1.8 Erosion 

1.9 Electrical discharge 

1.10 Rippling

2.0 Scuffing 2.1 Scuffing

2.1.1 Mild 

2.1.2 Moderate 

2.1.3 Severe 

3.0 Plastic 

deformation 3.1 Plastic deformation

3.1.1 Indentation 

3.1.2 Cold flow 

3.1.3 Hot flow 

3.1.4 Rolling 

3.1.5 Tooth hammer 

3.1.6 Rippling 

3.1.7 Ridging 

3.1.8 Burr 

3.1.9 Root fillet yielding 

3.1.10 Tip-to-root interference 

4.0 Contact fatigue

4.1 Subcase fatigue

4.2 Micropitting

4.3 Pitting (macropitting)

4.3.1 Initial

4.3.2 Progressive

4.3.3 Flake

4.3.4 Spall

5.0 Cracking

5.1 Hardening cracks

5.2 Grinding cracks

5.3 Rim and web cracks

5.4 Case/core separation

5.5 Fatigue cracks

6.0 Fracture

6.1 Brittle fracture

6.2 Ductile fracture

6.3 Mixed mode fracture

6.4 Tooth shear

6.5 Fracture after plastic deformation

7.0 Bending fatigue

7.1 Low-cycle fatigue

7.2 High-cycle fatigue

7.2.1 Root fillet cracks

7.2.2 Profile cracks

7.2.3 Tooth end cracks
NASA/TM—2013-217876 16

CI Versus Gear Wear Analysis

A pool of seven helicopter NGB tails were used out of a possible twenty available. To be included in 

this analysis, each tail required gear wear pictures to gauge the mode of gear failure per ANSI/AGMA 

1010-E95 standards. However, some gear wear pictures were unavailable from existing TDAs and AEAs.

Thus, certain tail numbers were left out of the analysis as noted above in Table 1. Condition Indicators

DA1 and SI were analyzed with respect to 25 and 50 percent positive response levels. Positive responses 

are defined such that the change in mean CI levels were at least 25 and 50 percent lower than prior to the 

damaged component being changed. The CIs were chosen based on the mean CI level analysis as

described above in Table 2 and Table 3. Five possible gear wear modes were determined from available 

TDA/AEA gear wear pictures: 1) micropitting, 2) progressive pitting, 3) flake pitting, 4) spalling, and 5) 

brittle fracture. The numbering scheme in Table 6 is used to describe the various gear failure modes.

Table 7(a) and Table 8(a) show positive response levels at the 50 and 25 percent levels. Per available 

TDA photos, for every helicopter tail listed in Table 7(a) for a specific gear wear mode, at least three

teeth exhibited that particular wear pattern. Each of the seven helicopter NGB tails are assigned a row 

within each Gear Wear Mode so that tails having gears exhibiting multiple wear modes are readily seen.

For example, Tail 02 is assigned the second row of each Gear Wear Mode and exhibits a 50 percent

positive response for micropitting for both output gear CIs, DA1 and SI as well a 50 percent positive 

response for spalling at the input pinion CIs, DA1 and SI.

The positive responses were tabulated by number and percentage for each set of CI and specific gear 

wear mode pair, Table 7(d) to (e) and Table 8(d) to (e). At the 25 percent level, more positive responses 

were received for the input pinion CIs (33 percent or 23/70) than the output gear CIs (23 percent or 

16/70). At the 50 percent level the positive responses decreased for both input pinion CIs (24 percent or 

17/70) versus output gear CIs (16 percent or 11/70).

The CI/Gear Wear Mode combination with the most positive responses was input pinion SI with 

micropitting and spalling both at 57 percent at the 50 percent Positive Response Level (Table 7(d)) and 

with micropitting at 71 percent at the 25 percent Positive Response Level (Table 8(d)). On the output gear

NGB side, output gear SI had the most positive responses with micropitting at 43 percent at the 

50 percent Positive Response Level (Table 7(d)) while output gear DA1 had the most positive responses 

with micropitting at 57 percent at the 25 percent Positive Response Level (Table 8(d)).

Positive responses were also tabulated by percentage for each CI against any gear wear mode, 

Table 7(c) and Table 8(c). At the 50 percent Positive Response Level, SI had the most responses on the 

input pinion NGB side (31 percent) and on the output gear NGB side (20 percent). At the 25 percent

positive response level, SI had the most responses on the input pinion NGB side (34 percent) while DA1

had the most responses on the output gear NGB side (26 percent).

Finally, positive responses were tabulated by percentage for each specific gear wear mode versus 

input pinion CIs and output gear CIs, in Table 7(b) and Table 8(b). At the 50 percent Positive Response 

Level, spalling (50 percent) was the most dominant gear wear mode observed on the input pinion NGB 

side while micropitting (36 percent) was the most dominant gear wear mode observed on the output gear

NGB side. At the 25 percent Positive Response Level, micropitting was the most dominant gear wear 

mode observed on the input pinion NGB side (64 percent) and on the output gear NGB side (50 percent).

It is noted that micropitting was the only Gear Wear Mode observed in all seven helicopter NGB tails at 

the 25 percent Positive Response Level.
NASA/TM—2013-217876 17

TABLE 7.—HELICOPTER NGB TAILS SHOWING CI 50 PERCENT POSITIVE 

RESPONSE AFTER COMPONENT REPLACEMENT VERSUS GEAR WEAR MODE

[Blank spaces in (a) indicate no damage was found with the corresponding gear wear mode.]

(a) AH–64 NGB tails showing CI 50% positive response after component replacement versus gear wear mode.

Gear wear mode

AGMA 1010 ↓

Condition indicator

Input pinion Output gear

In_DA1 In_SI Out_DA1 Out_SI

4.2 Micropitting

01 01 01 01

02 02

04 04

07

10 10

4.3.2 Progressive

01 01

10 10

4.3.3 Flake

01 01

4.3.4 Spalling

01 01 01 01

02 02

06 06

10 10

6.1 Brittle fracture

10

(b) Percent 'positive' response input NGB

and output NGB versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 42.9 35.7

4.3.2 7.1 21.4

4.3.3 14.3 0.0

4.3.4 50.0 21.4

6.1 7.1 0.0
NASA/TM—2013-217876 18

TABLE 7.—CONCLUDED.

(c) Percent 'positive' response CI versus any wear.

In_DA1 In_SI Out_DA1 Out_SI

4.2

17.1 31.4 11.4 20.0

4.3.2

4.3.3

4.3.4

6.1

(d) Percent 'positive' response CI versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 28.6 57.1 28.6 42.9

4.3.2 0.0 14.3 14.3 28.6

4.3.3 14.3 14.3 0.0 0.0

4.3.4 42.9 57.1 14.3 28.6

6.1 0.0 14.3 0.0 0.0

(e) Cells with 'positive' response.

In_DA1 In_SI Out_DA1 Out_SI

4.2 2 4 2 3

4.3.2 0 1 1 2

4.3.3 1 1 0 0

4.3.4 3 4 1 2

6.1 0 1 0 0

(f) Number tails per CI versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 7 7 7 7

4.3.2 7 7 7 7

4.3.3 7 7 7 7

4.3.4 7 7 7 7

6.1 7 7 7 7
NASA/TM—2013-217876 19

TABLE 8.—AH-64 NGB TAILS SHOWING CI 25 PERCENT POSITIVE 

RESPONSE AFTER COMPONENT REPLACEMENT VERSUS GEAR WEAR MODE

[Blank spaces in Table 8(a) indicate no damage was found with the corresponding gear wear mode.]

(a) AH–64 NGB tails showing CI 25% positive response after component replacement versus gear wear mode

Gear wear mode

AGMA 1010 ↓

Condition indicator

Input pinion Output gear

In_DA1 In_SI Out_DA1 Out_SI

4.2 Micropitting

01 01 01 01

02

04 04

05

06

07 07

10 10 10 10

4.3.2 Progressive

01 01

10 10 10 10

4.3.3 Flake

01 01

4.3.4 Spalling

01 01 01 01

02 02

06 06

10 10 10 10

6.1 Brittle fracture

06

10 10

(b) Percent 'Positive' response input NGB and output NGB versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 64.3 50.0

4.3.2 14.3 28.6

4.3.3 14.3 0.0

4.3.4 57.1 28.6

6.1 14.3 7.1
NASA/TM—2013-217876 20

TABLE 8.—CONCLUDED.

(c) Percent 'Positive' response CI versus any wear.

In_DA1 In_SI Out_DA1 Out_SI

4.2

31.4 34.3 25.7 20.0

4.3.2

4.3.3

4.3.4

6.1

(d) Percent 'Positive' response CI versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 57.1 71.4 57.1 42.9

4.3.2 14.3 14.3 28.6 28.6

4.3.3 14.3 14.3 0.0 0.0

4.3.4 57.1 57.1 28.6 28.6

6.1 14.3 14.3 14.3 0.0

(e) Cells with 'Positive' response.

In_DA1 In_SI Out_DA1 Out_SI

4.2 4 5 4 3

4.3.2 1 1 2 2

4.3.3 1 1 0 0

4.3.4 4 4 2 2

6.1 1 1 1 0

(f) Number tails per CI versus wear mode.

In_DA1 In_SI Out_DA1 Out_SI

4.2 7 7 7 7

4.3.2 7 7 7 7

4.3.3 7 7 7 7

4.3.4 7 7 7 7

6.1 7 7 7 7

Conclusions

Based on the results of this study the following conclusions can be made:

1. From the Mean Condition Indicator Level Analysis, Diagnostic Algorithm 1 and Sideband Index

were found to perform best while FM4 performed the worst. FM0 results were mixed. These 

findings were generally consistent with conclusions by previous studies.

2. Condition indicators, Sideband Index and Diagnostic Algorithm 1 were found to respond well to 

component changes where micropitting and spalling were the observed gear wear mode.

3. Overall, input nose gearbox condition indicators were found to respond better to part changes due 

to gear wear than corresponding output nose gearbox condition indicators.

4. Based on the condition indicator versus gear wear analysis, improvements to condition indicators

should target both input Diagnostic Algorithm 1 and input Sideband Index to detect either gear 

tooth micropitting or spalling.
NASA/TM—2013-217876 21

Recommendations for Improved Analysis Capabilities:

1. It is possible that not all gear teeth were photographed in the tear-down analysis reports. Thus 

actual damage levels may differ than from what was reported. It is suggested that all gear teeth on 

damaged nose gear box be photographed in a consistent manner such that the type of damaged 

can be documented per ANSI/AGMA 1010-E95 standards.

2. If not already implemented, any damaged gears should be stored for further analysis.

References

1. “Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet,” Antolick, Lance; 

Branning, Jeremy; Dempsey, Paula; Wade, Daniel. 66th American Helicopter Society International 

Annual Forum 2010, Phoenix, AZ. 11-13 May 2010.

2. ADS-79C-HDBK, Aeronautical Design Standard Handbook for Condition Based Maintenance 

Systems for U.S. Army Aircraft. 12 Jan. 2012.

3. ANSI/AGMA 1010-E95. Appearance of Gear Teeth – Terminology of Wear and Failure.

4. http://aircraft-photographs.s3-website-eu-west-1.amazonaws.com/Airshow-h2.jpg

5. “A Perspective on condition Monitoring in U.S. Army Aviation,” Keller, Jonathan, Ph.D. Wind 

Turbine Condition Monitoring Workshop, 20 Sep. 2011.

6. “An Investigation of Gear Mesh Failure Prediction Techniques,” Zakrajsek, James J. NASA TM–

102340, Nov. 1989.

7. Szczeptanik, J., “Time Synchronous Averaging of Ball Mill Vibration,” Mechanical Systems and 

Signal Processing, Vol. 3, No. 1, p. 99, Jan. 1989.
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Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet 

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Delgado, Irebert, R; Dempsey, Paula, J.; Antolick, Lance, J.; Wade, Daniel, R. 

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14. ABSTRACT

This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army’s Condition-Based 

Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was 

performed where condition indicator performance was based on a ‘batting average’ measured before and after part replacement. Two 

specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition 

indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were 

categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition 

indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and 

spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their 

corresponding output gear nose gear box condition indicators. 

15. SUBJECT TERMS

Health and usage monitoring; Helicopter; Transmission; Gears; Damage detection 

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