Afroz Zaman

Glenn Research Center, Cleveland, Ohio

Matthew Bauch

Carnegie Mellon University, Pittsburgh, Pennsylvania

Daniel Raible

Glenn Research Center, Cleveland, Ohio

Embedded Acoustic Sensor Array for Engine Fan 

Noise Source Diagnostic Test: Feasibility of Noise 

Telemetry Via Wireless Smart Sensors 

NASA/TM—2011-217017

July 2011
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Afroz Zaman

Glenn Research Center, Cleveland, Ohio

Matthew Bauch

Carnegie Mellon University, Pittsburgh, Pennsylvania

Daniel Raible

Glenn Research Center, Cleveland, Ohio

Embedded Acoustic Sensor Array for Engine Fan 

Noise Source Diagnostic Test: Feasibility of Noise 

Telemetry Via Wireless Smart Sensors 

NASA/TM—2011-217017

July 2011

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135
Acknowledgments

This work was supported by the Subsonic Fixed Wing (SFW) project under NASA’s Fundamental Aeronautics Program.

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at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2011-217017 1 

Embedded Acoustic Sensor Array for Engine Fan Noise 

Source Diagnostic Test: Feasibility of Noise Telemetry 

Via Wireless Smart Sensors 

Afroz Zaman 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Matthew Bauch 

Carnegie Mellon University 

Pittsburgh, Pennsylvania 15213 

Daniel Raible 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

Aircraft engines have evolved into a highly complex system 

to meet ever-increasing demands. The evolution of engine 

technologies has primarily been driven by fuel efficiency, 

reliability, as well as engine noise concerns. One of the 

sources of engine noise is pressure fluctuations that are induced 

on the stator vanes. These local pressure fluctuations, once 

produced, propagate and coalesce with the pressure waves 

originating elsewhere on the stator to form a spinning pressure 

pattern. Depending on the duct geometry, air flow, and frequency of fluctuations, these spinning pressure patterns are 

self-sustaining and result in noise which eventually radiate to 

the far-field from engine. To investigate the nature of vane 

pressure fluctuations and the resulting engine noise, unsteady 

pressure signatures from an array of embedded acoustic sensors are recorded as a part of vane noise source diagnostics. 

Output time signatures from these sensors are routed to a 

control and data processing station adding complexity to the 

system and cable loss to the measured signal. “Smart” wireless 

sensors have data processing capability at the sensor locations 

which further increases the potential of wireless sensors. 

Smart sensors can process measured data locally and transmit 

only the important information through wireless communication. The aim of this wireless noise telemetry task was to 

demonstrate a single acoustic sensor wireless link for unsteady 

pressure measurement, and thus, establish the feasibility of 

distributed smart sensors scheme for aircraft engine vane 

surface unsteady pressure data transmission and characterization. 

Introduction 

Wherever an array of sensors is implemented, wireless 

communication appears more appealing. Wireless sensors 

convert analog signals to digital streams prior to RF transmission, 

while wired systems route analog signals to a processing 

station for signal conversion. So, digital conversion at the wireless 

sensor node eliminates possible signal degradation during analog 

signal transmission through long cables. A wireless sensor 

system promises reliable data acquisition and transmission for 

a large number of sensors embedded on large structures. 

A smart sensor or mote is inherently different than a traditional sensor as defined by three characteristic features: onboard computational ability by means of a microprocessor, the 

ability to store sensed data prior to processing and store 

process instructions and numerical algorithms in onboard 

memory, thus, acts truly wireless both from data-acquisition 

and from power supply perspective. The wireless feature of a 

smart sensor requires that the node is either self-powered or 

battery powered and has an onboard radio to transfer 

processed data to another access node point. 

Smart sensors, however, have some limitations. Depending 

on application, sensor sampling speed may be too slow, clocks 

on different sensor nodes may not always be synchronized, as 

well as limited memory storage capacity and slower than PC 

processor speed often are the major drawbacks. Moreover, 

battery power imposes limitations on many aspects of smart 

sensors. Any task consuming large amounts of power becomes 

impractical on a battery-operated smart sensor node. Any 

system employing smart sensors needs to overcome these 

limitations through deliberate system design (Table 1). 

TABLE 1.—INTEL IMOTE2 RADIO FEATURES 

Features Value 

Processor, variable speed (PXA27x) 

Clock speed 13 to 416 MHz 

Active power (mW) 44 at 13 MHz, 570 at 416 MHz 

Radio ChipCon 2420 802.15.4 

Flash memory 32 M 

RAM (bytes) 256 K + 32 M external 

Non-volatile storage 32 M 

Size 48 × 36 × 7 mm 
NASA/TM—2011-217017 2 

Intel’s Imote2 smart sensor platform does not provide the 

sensing capability but comes with a few available sensor interface boards, digital I/O ports, a low power processor and 

802.15.4 radio with a built-in 2.4 GHz antenna for processing 

data intensive applications (Fig. 1). Other wireless sensor 

platforms, such as the MicaZ, or TelosB Motes have focused 

on ultra low power performance and low data-rates. Although 

Imote2 is well suited to high bit-rate applications, it does not 

come with ultrasonic signal sensing interface boards. To integrate a microphone sensor with a commercial Imote2 platform 

requires a custom-developed sensor interface board (Ref. 1). 

Figure 1.—Top, bottom, and vertically stacked 

view of Imote2 smart sensor platform. 

Engine Noise Telemetry Concept 

Development 

This section briefly covers engine fan noise source diagnostics problem, the rationale behind “wireless” telemetry of 

noise data, then reviews Intel’s Imote2 wireless radio platform 

and its Basic Sensor Board and highlights the motivation 

behind developing a flexible sensor board to specifically 

address the measurement needs in Subsonic Fixed Wing 

(SFW) fan noise reduction problem. The design concept of a 

SFW acoustic sensor board to interface with the Imote2 wireless radio module is presented subsequently. SFW acoustic 

sensor board was designed to meet the bandwidth requirement 

imposed during wind tunnel measurement of noise level from 

1/5th to 1/10th scale model test engines. Bandwidth requirement for measurement of acoustic noise signals from 1/5th to 

1/10th scale model test engines is inversely proportional to 

their engine scale model factors. Thus, to simulate test data 

covering audible noise signal bandwidth of 2 to 20 kHz from a 

full scale engine, a 1/5th scale model test engine signal bandwidth has to range from 10 to 100 kHz, thus requiring a 

Nyquist frequency of over 200 kHz setting analog to digital 

(ADC) signal sampling rate at less than every 5 μsec. 

Engine Vane Noise Source Measurement 

Problem Statement 

To meet stringent aircraft noise reduction standards in pursuit of quieter aircraft engines for civil aviation, a comprehensive wind tunnel test called engine noise Source Diagnostics 

Test (SDT) was conducted at GRC in order to understand the 

basic noise generation mechanism and, specifically, the 

sources of noise within a turbofan engine (Ref. 2) (Fig. 2). As 

part of noise source diagnostic test, pressure fluctuation time 

histories from vane embedded microphones at discrete locations inside a stator vane for various flow conditions was 

measured inside GRC wind tunnel. 

Figure 2.—A turbofan engine cutaway view.
NASA/TM—2011-217017 3 

Figure 3.—Engine outlet guide vane (stator) and fan 

(rotor) blades in perspective view.

Figure 4.—An engine under test in GRC wind tunnel.

 

Figure 5.—A stator vane with embedded pressure transducers. 

Engine rotor-stator interaction noise is primarily generated 

by the pressure fluctuations that are induced on the stator 

vanes due to the impingement of high speed air forced by the 

rotor fan. These local pressure fluctuations, once produced, 

coalesce with pressure waves from elsewhere on the stator and 

form a spinning pressure pattern. Under right duct geometry, 

mean flow characteristics, and fluctuation frequency, these 

pressure patterns self-sustain and result in a propagating mode, 

called duct mode that eventually radiate to the far-field from 

engine exhaust. Most of the time, the modes decay in the fan 

duct and do not make to the far field. The aggregate contributions from all the duct modes create pressure field that is 

called the rotor-stator interaction noise. 

This explanation holds for periodic vane fluctuations that 

result in discrete frequency interaction tones. However, for 

broadband vane pressure fluctuations, this explanation breaks 

down due to random amplitude/phase characteristics of vanepressure fluctuations. To explain vane pressure fluctuations 

and rotor-stator interaction noise correlation, surface distributions of vane fluctuating pressures were measured as part of 

the Fan Noise Source Diagnostic Test (SDT) conducted in the 

NASA Glenn Research Center’s 9- by 15-Foot Acoustic Wind 

Tunnel (Ref. 2). The SDT was on a model 22-in. fan of a 

modern turbofan engine (Figs. 3 and 4) consisting of 22 blades 

with design tip speed of 1215 ft/sec. Stator vane pressure time 

fluctuations were recorded using embedded pressure transducers along vane chord and span lines (Fig. 5). For each test 

condition, ~10 sec of data were recorded at 128 kHz sampling 

rate. Each test result set provided sound pressure spectral 

composition at each vane location point. As the number of 

transducers on the stators increases and as transducer locations 

on the rotor tips is considered, an alternate wireless data 

acquisition scheme was sought. 

Wireless Noise Telemetry Task

An alternate data acquisition and processing scheme 

mimicking the emerging wireless sensor networks was 

adopted. The efforts at Antenna and Optical Systems Technology Branch at GRC included wireless telemetry link feasibility demonstration for vane source noise detection using 

Imote2 smart sensor motes and three noise sources including a 

microphone sensor. Nyquist sampling rate for noise signal 2 to 

20 kHz asks for A/D ~50 kHz (20 μsec). The sensor signal 

through a power conditioning circuit on a “custom” interface 

board would modulate a 2.5 GHz RF carrier with digitized 

pressure data and import modulated data through the SPI 

port of Intel’s smart sensor, Imote2 mote radio, for transmission. Received signal at the base station mote is sent to 

a host PC for signal processing and for displaying spectral 

components. 

Kulite Pressure Sensor 

Pressure sensors produce electric output proportional to the 

applied pressure. Kulite pressure sensors (Fig. 6) are piezoresistive transducers with response from near steady state to ultrasonic frequencies at 5 V and 200 Ω output impedance (Fig. 6). 

Device sensitivity is measured by (mV/psi). Because of the 

extremely small sensing element size (1.6 by 1.6 by 0.5 mm), 

 
NASA/TM—2011-217017 4 

Figure 6.—Kulite pressure sensor. 

Figure 7.—Imote2’s “basic” sensor interface board. 

Kulite pressure sensors offer more packaging flexibility than 

any other technology for wind tunnel tests, flight tests, and 

other measurements of turbulent flow. Kulite sensors are also 

used in wind tunnel testing of automotive parts. 

Imote2’s “Basic” Sensor Interface Board 

Sensor boards (Fig. 7), which can interface directly with 

Imote2 platform and operate through available open source 

software, exist for a variety of basic sensing applications such 

as light, temperature, pressure, humidity, and acoustic (sounder)

sensors, and are compatible with most commercial sensing 

application needs for low frequency varying phenomena. Onboard memory, A/D speed and resolution of these commercial 

boards do not support SFW sensor data acquisition needs at 

~100 kHz sampling rate. 

SFW “Custom” Sensor Interface Board 

An alternate SFW sensor board concept for Imote2 were 

laid out that provides flexible user-selectable sampling rates 

and anti-aliasing filtering capabilities. Human hearing range of 

2 to 20 kHz is considered with a focus around a 5 kHz ‘ductmode’ (sampling rate, <20 μsec for a 1/5th scale engine testing). To avoid potential signal errors, avoiding sample-rate 

fluctuation is critical, especially in the higher frequency range, 

requiring microseconds sampling resolution. While simply 

interfacing a Kulite transducer with a high-quality A/D could 

address the sampling rate issues, a programmable signal conditioner was needed because of the flexibility it would offer in 

addressing anti-aliasing and signal processing concerns. 

Embedded acoustic pressure sensors provide the ability to 

measure small-amplitude vane local pressure variations; therefore, sensor interface board must provide signal sampling 

resolution and noise characteristics. Several factors contribute 

to the quality of the measured digital signal output: the sensitivity of the given sensor, noise floor of the sensor and other 

electrical components, and the resolution at which the noise 

signal is sampled (A/D resolution) which dictates the smallest 

quantifiable measured increment. 

The heart of the SFW acoustic sensor interface board is the 

Quickfilter QF4A512, a 4-channel A/D and programmable 

signal conditioner with user-selectable sampling rates and 

programmable digital filters (Ref. 3). The board interfaces a 

Kulite analog pressure sensor with the Imote2 via SPI I/O. A 

block diagram of the components of the SFW sensor board is 

given in Figure 8. Each component piece is discussed in the 

subsequent sections. 

Figure 8.—Block diagram of SFW acoustic sensor interface board to Imote2 wireless platform. 
NASA/TM—2011-217017 5 

Low-Pass Filter 

Output impedance of the microphone sensor is fed to a lowpass filter and power conditioning circuit which acts as an 

initial slow roll-off anti-aliasing filter and an impedance 

matching device (Fig. 9). 

The SFW acoustic power conditioning interface for noise 

measurement was used in the wireless link ‘feasibility demonstration’. The circuit components were operational amplifiers, 

resistors, ceramic capacitors, potentiometer and battery power 

to drive the microphone, as such, were only suitable for tabletop demonstration. Though this was sufficient for a single, 

proof of concept link demonstration, a power conditioner 

circuit board has been fabricated to be miniaturized as a sensor 

interface board suitable for vertical integration to the Imote2 

sensor platform. 

QuickFilter Development Kit, QF4A512-DK 

The heart of the digital link development kit (Ref. 4) is the 

QF4A512 programmable signal conditioner with a 4-channel, 

16-bit, Analog-to-Digital Converter (ADC). 

Each of the 4 channels offers selectable gain, analog antialiasing filter, selectable sampling frequencies and individually 

programmable digital Finite Impulse Response (FIR) filters. 

The QF4A512 performs oversampling, filtering and decimation 

to achieve two goals (Fig. 10): improve output resolution by 

oversampling and thereby decreasing the quantization error 

noise and elimination of noise energy above the Nyquist frequency through a digital decimation filter. The gain, sampling 

rate and user desired FIR filter are designed through software 

provided by QuickFilter, by selecting specific FIR filter type, 

sampling rate and filter characteristics in the FIR Editor shown 

in the screen shot below. The filter is assigned to the channel 

and is exported to Imote2 header file when the sensing application is loaded onto the Imote2. 

Imote2 mote possesses 32MB SRAM capable of supporting 

50 kHz ADC sampling speed needed for SFW acoustic data. 

With 16-bit 4 channel-ADC, 32 MB SRAM would be able to 

handle 9 to 10 secs of acoustic time series signal. To cover 

2 Hz to 20 kHz audible range, the biggest challenge was to 

achieve a 50 kHz (in μsecs) signal sampling rate through an 

ADC and higher power consumption capability (signal gain) 

from an interface board than can be measured by any commercial smart sensor board. For the current feasibility demonstration, we used a QF4A512 based custom sensor board, ISM 

400, developed for structural vibration measurement (Ref. 5). 

First 3 channels of ISM 400 ADC were dedicated to acceleration sensing along x-y-z axes; the 4th ADC channel was utilized in this single link demonstration. 

Figure 9.—(a) Low-pass filter characteristics and (b) SFW 

power conditioning circuit. 

Figure 10.—Filter design screen and actual filter output 

from QF4A512. 

Software Development 

Programming for wireless motes is done in nesC language 

in TinyOS operating system to respond to commands (‘wake 

up’, ‘data acquisition’) issued by an application running on a 

PC that was developed in Java, and delivered wirelessly by a 

base station gateway mote. Some of the critical tasks done 

were software development for controlling the sensor board 

circuitry, modifying codes for driver control functions of the 

ADC, and designing filter components for signal processing 

optimization. 

Some of the challenges were: programming in TinyOS to 

be able to import sensor data to the Imote2 radio’s overhead, 

transmit/receive serial data, multiplex, de-multiplex, and display parallel pressure data output spectrums. 

 
NASA/TM—2011-217017 6 

Single Sensor Telemetry Link Feasibility 

Demonstration 

The first set of single telemetry link feasibility demonstration was made using the SFW power conditioner and a custom 

sensor board, ISM400, with one of four ADC channels available while the other three were dedicated to the three axes of an 

on-board accelerometer sensor for vibration sensing. The 

signal sources were—simulated, random noise, and from a 

microphone. The table-top transmit-receive feasibility link set 

up is given in Figures 11 and 12 and the results are given in 

Figures 13 to 15. 

Figure 11.—Table-top link set up with transmit Imote2 to the 

left; receive base station Imote2 connected to a computer. 

Figure 12.—Closeup view of the microphone connected to a transmit Imote2 that is placed away from a base 

station receive Imote2 connected to a laptop. 

Figure 13.—1.6 kHz function generator wave and received signal spectrum at the base station. 

 
NASA/TM—2011-217017 7 

Figure 14.—White noise from PC and received signal spectrum at the base station. 

Figure 15.—440 Hz instrument tuner audio and received signal spectrum at the base station. 
NASA/TM—2011-217017 8 

Concluding Remarks 

Intel’s Imote2 smart sensor radios provide enhanced computation and communication resources that make embedded 

acoustic sensor array data telemetry possible. This study has 

explored the development of a versatile Imote2 sensor interface board capable of onboard signal processing and suitable 

for SFW acoustic data telemetry applications. The components 

of the microphone sensor interface board have been selected to 

allow low-noise, high resolution, high-speed (μsecs) data 

acquisition necessary for successful implementation of multiple sensor links. Figures 13 to 15 summarize the results of 

three separate wireless link feasibility demonstrations that 

were conducted using the table-top set up of Figure 11. Acoustic signal time signatures for the case of an ideal signal, a 

random noise signal, and an audio microphone signal and the 

corresponding spectral distributions of signals received at the 

base station were recorded and displayed for various separation distances. These single-link demonstration results prove 

the feasibility of smart sensors for wireless data acquisition 

and processing in acoustic noise source detection problems. 

References 

1. “Structural health monitoring sensor development for the Imote2 

platform,” by J. Rice and B. Spencer, Proc. SPIE Smart Structures/NDE 2008, San Diego, CA. 

2. “Fan Noise Source Diagnostic Test—Vane Unsteady Pressure 

Results,” by E. Envia, NASA/TM—2002-211808, June 2002. 

3. “QuickFilter Programmable Signal Conditioner,” 

http://www.quickfiltertech.com/html/qf4a512-psc.php

4. “QuickFilter QF4A512–DK Development Kit,” 

http://www.quickfiltertech.com/files/QF4A512-DK.pdf

5. “ISM400, Multimetric Imote2 Sensor Board,” 

http://shm.cs.uiuc.edu/files/ISM400_Datasheet.pdf
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Embedded Acoustic Sensor Array for Engine Fan Noise Source Diagnostic Test: Feasibility 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

Aircraft engines have evolved into a highly complex system to meet ever-increasing demands. The evolution of engine technologies has 

primarily been driven by fuel efficiency, reliability, as well as engine noise concerns. One of the sources of engine noise is pressure 

fluctuations that are induced on the stator vanes. These local pressure fluctuations, once produced, propagate and coalesce with the pressure 

waves originating elsewhere on the stator to form a spinning pressure pattern. Depending on the duct geometry, air flow, and frequency of 

fluctuations, these spinning pressure patterns are self-sustaining and result in noise which eventually radiate to the far-field from engine. To 

investigate the nature of vane pressure fluctuations and the resulting engine noise, unsteady pressure signatures from an array of embedded 

acoustic sensors are recorded as a part of vane noise source diagnostics. Output time signatures from these sensors are routed to a control 

and data processing station adding complexity to the system and cable loss to the measured signal. “Smart” wireless sensors have data 

processing capability at the sensor locations which further increases the potential of wireless sensors. Smart sensors can process measured 

data locally and transmit only the important information through wireless communication. The aim of this wireless noise telemetry task was 

to demonstrate a single acoustic sensor wireless link for unsteady pressure measurement, and thus, establish the feasibility of distributed 

smart sensors scheme for aircraft engine vane surface unsteady pressure data transmission and characterization. 

15. SUBJECT TERMS

Fan noise; Rotor stator interaction; Wireless; Sensor 

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