Inverse Normalized Energy Based Feature using Wavelet for 

Trend Prognosis on Mechanical Coupling for the Common 

Remotely Operated Weapon Station (CROWS)

by Canh Ly, Andrew Bayba, and Derwin Washington

ARL-TR-5327 September 2010

Approved for public release; distribution unlimited. 
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Army Research Laboratory

Adelphi, MD 20783-1197

ARL-TR-5327 September 2010

Inverse Normalized Energy Based Feature using Wavelet for 

Trend Prognosis on Mechanical Coupling for the Common 

Remotely Operated Weapon Station (CROWS)

by Canh Ly, Andrew Bayba, and Derwin Washington

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution unlimited. 
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Inverse Normalized Energy Based Feature using Wavelet for Trend Prognosis on 

Mechanical Coupling for the Common Remotely Operated Weapon Station 

(CROWS)

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Canh Ly, Andrew Bayba, and Derwin Washington

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U.S. Army Research Laboratory

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14. ABSTRACT

In this report, we present an innovated method for diagnosing and predicting loosened bolts on the elevation control motor 

(ECM) of the Common Remote Operated Weapons Station (CROWS) system in order to prevent a disastrous fault or failure in 

the gear mechanism that loose bolts could cause. Our method uses the ―symlet‖ wavelet to de-noise non-stationary vibration 

signals from a tri-axial accelerometer mounted on the ECM. We calculated the ratio of the normalized energy of the ―baseline,‖

in which all the bolts were tightened, to cases in which the bolts were loosened to different levels of torque. The normalized 

energy signals were calculated from the output of Fast Fourier Transform (FFT) spectral components at the frequency band of 

the residual signals―the difference between the ―raw‖ experimental data and the de-noised data. We then conducted a series of 

controlled experiments where we deliberately loosened the ECM bolts to demonstrate the system’s diagnostic and prognostic 

capability. Based on the experimental data and results from our method, we showed that we can detect a fault and the trend of 

the loosening bolts on the weapons station. If faults are detected early enough, appropriate measures can be to taken to enhance 

the reliability of the weapons station.

15. SUBJECT TERMS

Inverse normalized energy, diagnostic, prognostic, CROWS, trend prognosis

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Canh Ly

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iii

Contents

List of Figures iv

List of Tables iv

1. Introduction 1

2. CROWS Data Acquisition 1

2.1 Data Collection Hardware and Software.........................................................................3

2.2 Accelerometer Mounting.................................................................................................4

3. Inverse Normalized Energy Algorithm 7

4. Results 8

5. Conclusion 13

6. References 14

Appendix A. Instrumentation Specifications 15

List of Symbols, Abbreviations, and Acronyms 20

Distribution List 21
iv

List of Figures

Figure 1. CROWS data acquisition system.....................................................................................3

Figure 2. Movement of one cycle of the weapons system..............................................................4

Figure 3. Accelerometer mounting position on the SU. .................................................................5

Figure 4. Accelerometer mounting position on the Gun/Actuator Assembly. ...............................5

Figure 5. Accelerometer mounting position on the pedestal assembly. .........................................6

Figure 6. Accelerometer mounting position on the elevation control unit. ....................................6

Figure 7. Flowchart of Inverse Normalized Energy Algorithm......................................................7

Figure 8. Raw data. .........................................................................................................................9

Figure 9. De-noised signals...........................................................................................................10

Figure 10. Residual signals...........................................................................................................11

Figure 11. Energy spectra. ............................................................................................................12

Figure 12. Feature values..............................................................................................................13

Figure A-1. SoMat eDAQ-lite. .....................................................................................................16

Figure A-2. EICP-B. .....................................................................................................................17

Figure A-3. SMITC.......................................................................................................................17

Figure A-4. Vibra-Metrics model 3000. .......................................................................................18

List of Tables

Table 1. CROWS system 1 identifiers............................................................................................2
1

1. Introduction

Engineering systems, such as aircraft, industrial processes, manufacturing systems, 

transportation systems, electrical and electronic systems, etc., are becoming more complex and 

are subject to failure modes that adversely impact their reliability, availability, safety, and 

maintainability. Such critical assets are required to be available when needed, and thus must be 

maintained on the basis of their current condition rather than on the basis of scheduled or 

breakdown maintenance practices. Moreover, online, real-time fault diagnosis and prognosis can 

assist the operator in avoiding catastrophic events (Ly et al., September 2009). Recent advances 

in Condition-Based Maintenance and Prognostics and Health Management (CBM/PHM) have 

prompted the development of new and innovative algorithms and advanced architectures (Ly et

al., August 2009) for fault, or incipient failure, diagnosis and failure prognosis aimed at 

improving the performance of critical systems.

We present an innovated method for diagnosing and predicting loosened bolts on the elevation 

control motor (ECM) of the Common Remote Operated Weapons Station (CROWS) system. Our

method uses a ―symlet‖ wavelet, one of the built-in functions in the MATLAB Wavelet Toolbox,

to de-noise nonstationary vibration signals from a tri-axial accelerometer mounted on the ECM. 

Then, we calculate the ratio of the normalized energy of the ―baseline,‖ in which all bolts are

tightened, to cases in which the bolts are loosened at different levels of torque. Finally, we 

determine the inverse normalized energy signals from the output of Fast Fourier Transform 

(FFT) spectral components at a frequency band of the residual signals―the differences between

the ―raw‖ experimental data and the de-noised data. Further, we conduct a series of controlled 

experiments where we deliberately loosen the ECM bolts to demonstrate the system’s diagnostic 

and prognostic capability. Based on the experimental data and results from our method, we show 

that we can detect both faults and the trend of the loosening bolts on the CROWS. 

Section 2 outlines the CROWS data acquisition. Section 3 provides the description of the inverse 

normalized energy algorithm. Section 4 gives the results of the experimental data performed on 

the CROWS S/N 071 (URM-1 configuration).

2. CROWS Data Acquisition

The U.S. Army Research Laboratory (ARL) uses the CROWS as a test bed to perform 

prognostics and diagnostics (P&D) on Army assets. Using the CROWS, ARL has evaluated

sensors, data collection and transmission techniques; accelerated testing methodologies; and 

developed P&D algorithms and hardware (Bayba et al., 2010). In March 2009, we performed a 

series of controlled experiments on the CROWS where we systematically and gradually loosened 
2

bolts securing critical components of the system. These experiments can be considered a form of 

accelerated testing, in which data show various levels of ―damage.‖ Damage here is defined as 

degradation in bolt tightness (torque) that will eventually lead to system failure. 

The CROWS is a two-axis stabilized weapons system remotely operated using a joystick or 

programmed scan cycle. The specific unit under test was a CROWS S/N 071 (URM-1

Configuration). The system had a M2 0.50 cal machinegun mounted. The system did not have 

the add-on armor plating typically in place on a fielded system. Table 1 contains the 

nomenclature/identification/serial numbers from the unit under test. From our viewpoint the 

system is composed of four primary subunits: 

• Pedestal Assembly: The pedestal assembly is the main compartment to which virtually 

everything else is mounted. It receives and distributes electrical power and control, and 

houses the azimuth motor and control electronics; the sensor unit motor and control 

electronics; and the control electronics for the gun actuator.

• Elevation Unit: The elevation unit houses the elevation control motor and motor control 

electronics. It mounts to the pedestal assembly.

• Sensor Unit (SU): The SU houses the video and thermal imaging hardware and the laser 

rangefinder. It mounts to the pedestal assembly.

• Gun/Actuator Assembly: The gun/actuator assembly includes the gun, gun mounting cradle 

with firing mechanisms, and a linear actuator whose action arms the gun and pulls the 

trigger. It mounts to the pedestal assembly.

Table 1. CROWS system 1 identifiers.

Assembly Unit Marking/Nameplate

Complete CROWS unit Stabilized Remote Weapon System

PN 21677-0200-011 Rev E

S/N: 071

Cage 11871

Subassembly

Pedestal assembly Pedestal Assembly

PN 21677-1000 Rev F

S/N: 071

SU Fire Control Systems PTY LTD

S/N: AA4183

Ref No ASY06077

EFCS-T Sensor Unit

Cage Z5731

Elevation control unit Elevation

Bar Code 21677-3000-00 00086

Gun/actuator assembly Electric Cylinder Model 12-0342

S/N: 03122A113631

OPTCODER

Type: LDA-185-1000CE

IDC P/N 802-002

Note: Unit markings/nameplate information was recorded from the actual assembly components.
3

2.1 Data Collection Hardware and Software

This section describes CROWS data acquisition system (figure 1). The CROWS data acquisition 

system includes the CROWS system (weapons platform), described below; an eDAQ digital 

signal collector; sensor modules, described details in appendix A; tri-axial accelerometers; and a 

personal computer. The weapons platform was the unit under test monitored via the 

accelerometers. The accelerometers received the vibration signals at four locations—elevation 

control unit, pedestal assembly, gun/actuator assembly, and sensor units. These locations are

described in detail in sections 2.2. 

Figure 1. CROWS data acquisition system.

Data collected during this set of tests involved various sensors and data acquisition hardware and 

software. Vibration within the system was the primary parameter of interest. Various voltages, 

currents, and temperatures were recorded to supplement the vibration data. Vibration data were 

acquired through the use of four tri-axial accelerometers (Vibra-metrics model 3000). The data 

acquisition system that provided the collection and storage was the SoMat eDAQ-lite System. 

Unless otherwise noted, all data acquisition hardware and software were products of HBM, Inc. 

The eDAQ-lite was controlled by a laptop computer, a Dell Latitude D620, using the program 

TCE v3.11.0, build 227. The software used to upload data to the laptop computer and parse it

was Infield v2.1.0. Power for and signal conditioning of each accelerometer was provided by 

EICP-B modules.

Weapon Platform

eDAQ Data Acquisition

Processing data

Tri-axial

accelerometer
4

Data were collected by sampling the output of the sensors while the weapons system 

continuously cycled through a set of programmed movement operations (figure 2). In figure 2, 

the lines shown should be viewed as the trace of the gun muzzle in three-dimensional (3-D)

space. As can be seen, the movement incorporated both rotations in azimuth and elevation, as 

well as a combined rotation in azimuth and elevation. 

Figure 2. Movement of one cycle of the weapons system.

For ARL reference purposes, we arbitrarily assigned an orientation to the system that can be 

described as the perspective of an observer at the ―rear‖ of the system looking outward toward 

the ―front.‖ Specifically, the front is the normal firing direction of the gun with the SU and 

elevation unit on the ―right‖ and the ammo box on the ―left.‖ A further delineation between 

―front‖ and ―rear‖ is that the power and control cabling enters the main compartment of the 

system from the ―rear.‖

2.2 Accelerometer Mounting

Each of the four subassemblies had one accelerometer attached to it. Figures 3–7 show the 

general placement of accelerometers on system. Details of the mounting locations are as 

follows: 

1. SU: The attachment is at the junction of the SU and pedestal assembly, specifically on a 

flange in the closest possible proximity to the SU harmonic drive gearing (figure 3). The 

attachment was accomplished by inserting an ARL-fabricated extension at one of the 

flange mounting points. This extension placed the accelerometer ~1⁄2 in away from the 

center of the screw mounting point. Accelerometer axis orientation: X is up, Y is rear, and 

Z is right. 

90 degree rotation in Azimuth

~30 degree

rotation in

Elevation

~30 degree 

rotation in

Elevation

Starting point
5

Figure 3. Accelerometer mounting position on the SU.

2. Gun/Actuator Assembly: The attachment is at the mounting location of the linear actuator to 

the gun cradle, specifically by replacing the upper right mounting screw (one of four 

mounting screws) with a custom ARL thread adaptor bolt (8-32 to 10-32) (figure 4). The 

first number of the bolt is the largest (major) diameter of the bolt and the second number is 

the number of threads per inch. For example, the major diameter in unified threads is 

defined as (0.060 in + 0.013 in × (numbered diameter)). So #8 has a major diameter of 

0.164 in. The accelerometer mounts directly to the 10-32 thread of this adaptor placing it 

inline with the original mounting screw. The goal of this accelerometer was to pick up 

vibrations in the cradle. Accelerometer axis orientation: X is rear, Y is down, and Z is 

right.

Figure 4. Accelerometer mounting position on the Gun/Actuator Assembly.

3. Pedestal Assembly: The attachment is at the bottom front right 1/4-28 (1/4 in major 

diameter with 28 threads per inch) hole in the lower compartment ―front‖ cover plate 
6

(facing the plate) (figure 5). This location is one of eight heavy-up armor attachment 

points on this plate. The accelerometer was attached using a custom ARL thread adaptor 

bolt (1/4-28 to 10-32). Accelerometer axis orientation: X is up, Y is right, and Z is front.

Figure 5. Accelerometer mounting position on the pedestal assembly.

4. Elevation Control Unit: The attachment is at the top right 10-32 existing hole in the bell 

housing of the elevation drive motor (figure 6). This was a direct attachment, thus no 

adaptors were necessary. This location was chosen because it produced the highest 

amplitude signal upon elevation in earlier testing. Accelerometer axis orientation: X is 

left, Y is rear, and Z is up.

Figure 6. Accelerometer mounting position on the elevation control unit.
7

3. Inverse Normalized Energy Algorithm

Figure 7 shows a flowchart of the Inverse Normalized Energy Algorithm. The algorithm starts 

off with the input vibration data collected with an acquisition system, as described in section 2. 

Since the accelerometer had a very slow frequency response and unwanted noise sources due to 

other parts of the CROWS systems, the one-dimensional de-noising techniques (MATLAB 

Wavelet Toolbox, User’s Guide, version 3) were used to extract out the unwanted noises and 

unnecessary responses. Many de-noised techniques have been introduced in open literature 

(Daubechies, 1992; Donoho, 1995; Mallat, 1989) and many routines have been developed in the 

MATLAB toolbox. The effect of each de-noising technique has minimum impact on the result. 

In this report, the ―symlet‖ wavelet package method at the level 5, one of the built-in functions in 

the MATLAB Wavelet Toolbox, was used to de-noise signals. The de-noised signals were then 

subtracted by the input data to obtain the residual data. The energy spectrum of each data file 

was computed. The frequency spectrum covers from zero to half of the sampling frequency, in 

this case, 20 KHz. Each spectrum was examined at different bands. It turned out the band was 

selected when the energy spectrum changed. For this particular case of the mechanical coupling 

on the CROWS, we selected a frequency band of 7.1–7.2 KHz. The maximum spectral 

component was extracted out for each file at the selected frequency band. Then, we calculated

the inverse normalized energy: the inverse ratio of the normalized energy of the baseline, in 

which all bolts were tightened, versus the cases when bolts were loosened at different levels of 

torque. 

Figure 7. Flowchart of Inverse Normalized Energy Algorithm.
8

The values of the ratios are the feature values for the fault diagnostic. The trend of the fault 

diagnostic profile shows the progress of loosening bolts as the torque of the bolts on the ECM 

were decreased from totally tightened to loosened hand tight. Mathematically, the feature values, 

FV, are calculated as follows:

   

   

 2

 2

1

FV( ) where 1, 2, 3, 4

 |

 |

Max

Max

B

B

B

B

b f

f f

i f

f f

i i

FFT R

FFT R





       

     

(1)

where, FFT is Fast Fourier Transform with the FFT size of the length of the data record. The 

vertical line ( | ) in equation 1 means that the 



operand is evaluated at the certain condition. 

fB is the frequency band of interest, in this case, fB = [7.1–7.2 KHz]. f is a set of frequency 

[0–20 KHz]. Rb is the residual signal (unit in g (gravity)) of the baseline data. Ri is the residual 

signal (unit in g) of the data at the level of torque i.

4. Results

We recorded data from the tri-axial accelerometer mounted on the ECM. While the 

accelerometer has three different axes, we chose to use only one axis (the y-axis) for the analysis 

for this report. Since the analysis for other axes was similar to that of the y-axis, it was not 

included in this report. 

The bolts of the ECM were deliberately changed at five different levels. Figure 8 shows the 

―raw‖ data of the CROWS, amplitude (g) versus time (s). At the first level, all bolts were 

completely tightened to ~54 foot-lb (ft-lb), shown as Baseline. At the second level, the torque of 

the bolts was decreased to 34 ft-lb, shown as Run #1. At the third level, the bolts were loosened 

at 25 ft-lb, shown as Run #2. At the fourth level, the bolts were loosened at the torque of hand 

tightened and less than 25 ft-lb, shown as Run #3. At the last level, the bolts were completely 

loosened, shown as Run #4. The length of each data file was recorded for 30 s and the data were 

sampled at 20 KHz. Each data file was recorded for a complete full cycle, as shown in figure 2. 
9

Figure 8. Raw data.

Figure 9 shows the de-noised signals of the raw data via 1-D de-noising technique using the 

symlet wavelet at the level 5 of the decomposition. As shown in figure 9, the unwanted noises 

and other signals were extracted. Since the collected data included a combination of signal and 

noise, de-noising was used to remove the unwanted noise and thus improve the signal-to-noise 

ratio and also remove the low frequency modulation of the accelerometers.

0 5 10 15 20 25 30

-2

0

2

Baseline

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #1

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #2

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #3

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #4

Time (sec)

Amplitude (g)
10

Figure 9. De-noised signals.

Once the signals are de-noised, the residual signals, as shown in figure 10, were computed by 

subtracting the de-noised signals (figure 9) from the raw data (figure 8). These residual signals 

were then used to calculate the feature values. Figure 11 shows the energy spectra of the baseline 

and the individual runs. For figure 11, we selected the frequency band of interest, 7.1 to 7.2 KHz, 

to compute the inverse normalized spectra using equation 1. Other frequency bands were also 

used for the computation; however, the results for those frequency bands were not significantly

different for different runs. The feature value plot is shown in figure 12. As the torque for the 

tightened bolts decreased, the inverse normalized energy (or feature) values increased. This 

indicates that the inverse normalized energy algorithm can detect the fault of loosened bolts on 

the ECM of the CROWS. 

0 5 10 15 20 25 30

-2

0

2

Baseline

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #1

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #2

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #3

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #4

Time (sec)

Amplitude (g)
11

Figure 10. Residual signals.

0 5 10 15 20 25 30

-2

0

2

Baseline

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #1

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #2

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #3

Time (sec)

Amplitude (g)

0 5 10 15 20 25 30

-2

0

2

Run #4

Time (sec)

Amplitude (g)
12

Figure 11. Energy spectra.

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8

0

1

2

x 105 Baseline

Frequency (kHz)

Magnitude

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8

0

1

2

x 105 Run #1

Frequency (kHz)

Magnitude

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8

0

1

2

x 105 Run #2

Frequency (kHz)

Magnitude

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8

0

1

2

x 105 Run #3

Frequency (kHz)

Magnitude

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8

0

1

2

x 105 Run #4

Frequency (kHz)

Magnitude
13

Figure 12. Feature values.

5. Conclusion

We have developed an innovated method for diagnosing and predicting loosened bolts on the 

ECM of a CROWS. The methods uses an algorithm based on features using a wavelet family 

package for fault detection and trend prognosis on mechanical coupling in the CROWS. In 

particular, the method computes the inverse normalized energy via de-noising techniques using 

the symlet wavelet package. In this effort, we applied the algorithm to the experimental data. The 

results indicated that the algorithm was robust—it was able to detect loosened bolts on the 

CROWS as well as on other mechanical systems. 
14

6. References

1. Ly, Canh; Tom, Kwok; Bayba, Andrew; Washington, Derwin. Fault Diagnosis and Trend 

Prognosis on Mechanical Coupling for the Common Remotely Operated Weapon Station 

(CROWS). RDECOM Mobility/Logistics Technology Focus Team Reliability Workshop, 

September 2009.

2. Ly, Canh; Tom, Kwok; Byington, Carl; Patrick, Romano; Vachtsevanos, George J. Fault 

Diagnosis and Failure Prognosis for Engineering Systems: A Global Perspective. IEEE 

Conference of Automated System Engineering, Bangalore, India, August 2009.

3. Mathlab Wavelet Toolbox, User’s Guide, Version 3.

4. Donoho, D. L. De-noising by Soft-thresholding. IEEE Trans. on Inf. Theory 1995, 42 (3), 

613–627.

5. Daubechies, I. Ten Lectures on Wavelets, CBMS-NSF conference series in applied 

mathematics. SIAM Ed., 1992.

6. Mallat, S. A Theory for Multiresolution Signal Decomposition: The Wavelet 

Representation. IEEE Pattern Anal. and Machine Intell. 1989, 11 (7), 674–693.
15

Appendix A. Instrumentation Specifications

The following is a short description of the hardware and sensors used during this data collection.

A-1 SoMat eDAQ-lite

The SoMat eDAQ-lite (figure A-1) is a standalone data acquisition system. The system is built of 

several data aquisition layers. The configuration of the SoMat eDAQ-lite used for this study is 

composed of the base processor layer (ELCPU), a battery layer (ELBAT-2), seven simultaneous 

high level layers (ELHLS), and a digital input/output layer (ELDIO). The ELHLS layers contain 

the channels used for data collection in this series of experiments; each provides four

simultaneously sampled high-level differential analog inputs through independent connectors.

Physical

Size: 6.875 in W x 5.625 in L x 8.75 in H 

Temperature: –20 to 65 °C

Connectors: SoMat 6-Pin M8

Enclosure

Sealed System: Machined aluminum case and sealed gaskets protect the system from 

humidity, corrosion, water spray, and dust.

Vibration Tested: Swept sine tested from 5 to 2000 Hz at 20 G’s

Modular: Up to eight layers can be custom configured pending testing requirements. 

Four screws are all that is required to disassemble a system. 

System

Input Power: 

ELCPU: 10-18 VDC

Fuses: 10A, automotive mini-blade

Internal backup battery

Sample Rates:

Master Clock Rates:100 kHz = 0.1 Hz to 100 kHz

98.3 kHz = 0.1 Hz to 98,304 Hz 
16

Communications

Ethernet: 100 BaseT

Serial: RS232 up to 115,200 baud 

Memory

2 GB Internal Flash

64 MB Internal DRAM

Figure A-1. SoMat eDAQ-lite.

A-2 ICP-Type Signal Conditioning Module (EICP-B)

This module (figure A-2) supplies a regulated 4-mA current source to an Integral Electronics 

Piezoelectric (IEPE) transducer. Constant current biased transducers require AC coupling for the 

output signal. The ICP-Type Signal Conditioning Module uses a 1 microfarad–1MΩ coupling 

network, which forms a 0.159-Hz high pass input filter. That is, a sinusoidal input signal at 

0.159 Hz will be attenuated by 3 dB, (output signal at 70.7% of input). Frequencies below 

0.159 Hz will be attenuated more, higher frequencies less.

Physical

Size: 0.75 in W x 3.25 in L x 0.75 in H (1.905 cm W x 8.255 cm L x 1.905 cm H)

Weight: 0.08 lbs (0.03 kg)

Temperature: –20° to 65° C

Connectors: SoMat 6-Pin M8 and BNC
17

Inputs

Input Power: 24 VDC at 4 mA

Load: 1 MΩ 

Figure A-2. EICP-B.

A-3 SoMat Thermocouple SMART Module (SMITC)

The SMITC (figure A-3) provides isolated thermocouple conditioning with software selectable 

linearization tables for J-, K-, T-, and E-type calibrations. The module has universal cold 

junction compensation and is fully isolated to 500 V.

Figure A-3. SMITC.

A-4 K Type Thermocouple

The K Type Thermocouple has the following specifications:

• Surface mount with self-adhesive backing

• Better than 0.3 s response time

• 2 m (72 in) color-coded perfluoroalkoxy (PFA) insulated leads 

• Rated to 175 °C (350 °F) long term

A-5 Vibra-Metrics Model 3000 Tri-axial Accelerometer

The Vibra-metrics model 3000 tri-axial accelerometer (figure A-4) has the following 

specifications:

Sensitivity: at 100 Hz, ±5% 10 mV/g, 1.02 mV/(m/s2)

Frequency Range:

• ±5% 3 Hz, 0.7 kHz

• ±10% 2 Hz, 0.8 kHz
18

• ±3 dB 1 Hz, 0.10 kHz

Turn-on Time: 2% of final bias <5 s

Amplitude Range: at 72 °F ±500 g

Mounted Resonance: >25 kHz

Transverse Sensitivity: <5%

Figure A-4. Vibra-Metrics model 3000.

A-6 Electrical

Noise (typical):

• Broadband 2.5 Hz to 25 kHz 1500 μg (rms)

• Spectral 10 Hz 100 μg/√Hz

• 100 Hz 25 μg/√Hz

• 1000 Hz 10 μg/√Hz

Output Impedance: 1000 



Isolation: >100 Meg 



Noise Rejection: (EMI/RFI) >52 dB

Bias Volts (nominal): 7 VDC

Power Requirements: 15–30 VDC

Current Regulating Diode: 1–6 mA 

A-7. Environmental

Temperature Range: –40 to 250 °F / –40 to 121 °C

Base Strain: <0.005 g peak/μstrain
19

Shock Limit: 5000 g

A-8 Physical

Physical Dimensions: 

• 8 in h x 0.8 in w x 1.05 in d

• 2 cm x 2 cm x 2.7 cm

Weight: 0.35 oz/10 g

Case Material: Titanium

Mounting: 10-32 removable stud

Mounting Torque: 20 in-lb (2.2 N-m)

Connector: Side 4-pin
20

List of Symbols, Abbreviations, and Acronyms

ARL U.S. Army Research Laboratory

CBM/PHM Condition-Based Maintenance and Prognostics and Health Management

CROWS Common Remote Operated Weapons Station

ECM elevation control motor

FFT Fast Fourier Transform

P&D prognostics and diagnostics

PFA perfluoroalkoxy 

SU Sensor Unit
21

NO. OF

COPIES ORGANIZATION

1 ADMNSTR

ELEC DEFNS TECHL INFO CTR

ATTN DTIC OCP

8725 JOHN J KINGMAN RD STE 0944

FT BELVOIR VA 22060-6218

1 CD OFC OF THE SECY OF DEFNS

ATTN ODDRE (R&AT)

THE PENTAGON

WASHINGTON DC 20301-3080

1 US ARMY RSRCH DEV AND ENGRG 

CMND

ARMAMENT RSRCH DEV & ENGRG 

CTR 

ARMAMENT ENGRG & TECHNLGY 

CTR

ATTN AMSRD AAR AEF T J MATTS

BLDG 305

ABERDEEN PROVING GROUND MD 

21005-5001

1 PM TIMS, PROFILER (MMS-P) 

AN/TMQ-52

ATTN B GRIFFIES 

BUILDING 563

FT MONMOUTH NJ 07703

1 US ARMY INFO SYS ENGRG CMND

ATTN AMSEL IE TD A RIVERA

FT HUACHUCA AZ 85613-5300

1 COMMANDER

US ARMY RDECOM

ATTN AMSRD AMR 

W C MCCORKLE

5400 FOWLER RD

REDSTONE ARSENAL AL 35898-5000

2 US ARMY RDECOM-ARDEC

ATTN RDAR WSF A P JONNSON

ATTN RDAR WSF A G GARCIA

BLDG 91

PICATINNY NJ 07806

2 US ARMY RDECOM-ARDEC

ATTN RDAR WSF A S GEARHART

BLDG 91 ATS

PICATINNY NJ 07806

NO. OF

COPIES ORGANIZATION

1 US ARMY RDECOM-ARDEC

ATTN RDAR WSF A D MARSTON

BLDG 61S

PICATINNY ARSENAL NJ 07806-5000

1 US GOVERNMENT PRINT OFF

DEPOSITORY RECEIVING SECTION

ATTN MAIL STOP IDAD J TATE

732 NORTH CAPITOL ST NW

WASHINGTON DC 20402

1 US ARMY RSRCH LAB

ATTN RDRL CIM G T LANDFRIED

BLDG 4600

ABERDEEN PROVING GROUND MD 

21005-5066

8 US ARMY RSRCH LAB

ATTN IMNE ALC HRR 

MAIL & RECORDS MGMT

ATTN RDRL CIM L TECHL LIB

ATTN RDRL CIM P TECHL PUB

ATTN RDRL SER E A BAYBA

ATTN RDRL SER E C LY

ATTN RDRL SER E 

D WASHINGTON

ATTN RDRL SER E K TOM

ATTN RDRL SER E R DEL ROSARIO

ADELPHI MD 20783-1197

TOTAL: 21 (1 ELEC, 1CD, 19 HCS)
22

INTENTIONALLY LEFT BLANK.