Real-time active noise control testing was undertaken in a cabin of a mining vehicle for the purpose of determining the relative effectiveness of energy density control vs sound pressure control. Both tonal and broadband noise over the frequency range 5 to 300Hz were used in the tests. Measurements before and after control were taken close to the error sensors and well away from the error sensors. As expected, higher reductions were achieved close to the error sensors, even when multiple sensors were used. When broadband noise was controlled, the levels at frequencies in the vicinity of the cavity resonances were reduced at the expense of increased levels at other frequencies so that the response spectrum became more flat. Energy density sensing resulted in greater levels of control than pressure sensing at distances further than approximately 300 mm from the error sensors

Hansen, C.

Morgans, R.

Stanef, D.

Adelaide Research &amp; Scholarship

Colin Hansen, page number 1TENTH INTERNATIONAL CONGRESS ONSOUND AND VIBRATION, ICSV10REAL TIME CONTROL OF SOUND PRESSURE ANDENERGY DENSITY IN A MINING VEHICLE CABINColin H. Hansen, Daniel A. Stanef and  Richard C. MorgansDepartment of Mechanical Engineering, University of Adelaide, SA 5005, Australiachansen@mecheng.adelaide.edu.auAbstractReal-time active noise control testing was undertaken in a cabin of a mining vehicle forthe purpose of determining the relative effectiveness of energy density control vs soundpressure control.  Both tonal and broadband noise over the frequency range 5 to 300Hzwere used in the tests.  Measurements before and after control were taken close to theerror sensors and well away from the error sensors.  As expected, higher reductions wereachieved close to the error sensors, even when multiple sensors were used. Whenbroadband noise was controlled, the levels at frequencies in the vicinity of the cavityresonances were reduced at the expense of increased levels at other frequencies so thatthe response spectrum became more flat.  Energy density sensing resulted in greaterlevels of control than pressure sensing at distances further than approximately 300 mmfrom the error sensors.1. INTRODUCTIONDrivers subjected to extended periods of exposure to low frequency noise in the cabins ofheavy machinery such as diesel-driven mining vehicles experience fatigue and highlevels of distraction. One possible approach to ameliorating the problem is to use activenoise control (ANC). Here, the effectiveness of applying an ANC system to a CaterpillarColin Hansen, page number 2mining vehicle cabin is investigated using the Causal Systems EZANC II real timecontroller.  Both energy density sensors and pressure sensors are used to provide errorsignals to the controller and both tonal and broadband noise spectra are used as theprimary noise disturbance.  Random noise excitation was first generated by the EZ-ANCII and subsequently a recording of existing cabin noise was used with the intention ofsimulating as closely as possible the actual noise environment in a typical mining vehicle.In a previous paper1, a finite element analysis undertaken on the experimental cabinwas used together with measurements of the transfer functions between control sourcesand error sensors to predict the feasibility of a number of alternative sensing strategies forachieving both global and localised noise reductions in the cabin. Results from theanalytical work indicated that ANC could produce zones of local control inside the cabinfor both energy density and sound pressure sensing strategies, with a larger “zone ofquiet” about the error sensors predicted with the energy density sensing strategy.  Thework also showed that for a single local primary acoustic source in the cabin, it was notpossible to achieve global control using other acoustic sources and error sensorsdistributed around the cabin, even if the number of control sources and error sensors wasvery large. Experimental work reported here demonstrates the relative effectiveness ofsound pressure sensing vs energy density sensing for achieving noise reductions in thelocal region near the driver’s head.2. EXPERIMENTAL ARRANGEMENT AND PROCEDUREThe experimental arrangement is shown inFigure 1.  The system used the Causal SystemsEZANC II controller as a basis. For each testthat used the sound pressure sensing technique,signals from four low-cost electret condensermicrophones located in the cabin and amplifiedvia custom built amplifiers were directed to thecontroller.  The locations of the four errorsensor microphones, which were positionedclose to the driver’s head location, are labelled“EH” and are shown in Figure 2.  Also shownin Figure 2 are the locations of the energydensity probe (near the driver’s head locationand labelled “ED”) and the location of themonitor microphone (“MED”) used formonitoring the sound pressure level at thelocation of the energy density probe. Thesound pressure reduction at this monitor microphone, measured during ANC experimentswith the energy density sensor, was used as a comparison with the average soundMINING VEHICLE CABIN4 CONTROL SPEAKERS1  ENERGY DENSITY PROBE12  MICS1 PRIMARY SPEAKERPCPULSE  DATA ACQ. UNITTEAC DATA PLAYBACK UNIT EZANC II HP ANALYSER 2x2 CHANNEL CONTROL SPEAKER AMPLIFIERS  ENERGY DENSITY UNIT 2 x 6 CHANNEL  MIC. AMPS. PRIMARY SPEAKER AMP.Figure 1. Flow diagram of experimentalarrangement.Colin Hansen, page number 3pressure reduction at the four error microphones achieved during ANC experiments usingthe sound pressure sensing strategy. side view       Plan viewFigure 2. Locations of primary source (P), control sources (C1…C4), microphoneswithin 300 mm of nearest error sensor (M300), randomly spaced microphones locatedfurther than 300 mm from the nearest error sensor (MS), error sensor microphones neardriver’s head location (EH), energy density probe (ED) and microphone at the centre ofthe energy density probe (MED).For tests using the energy density sensing strategy, the signals from the fourmicrophones of the energy density probe were pre-processed to provide three orthogonalvelocity signals and one pressure signal, which were then used as input signals to theEZANC II controller, as shown in Figure 1.The locations of the eight monitor microphones, which were used during ANCexperiments with both sensing strategies to measure the extent of the zone of quietaround the error sensors during control, are shown in Figure 2.  The monitor microphonesthat were within 300 mm of the nearest error sensor are labelled, “M300” and those thatwere randomly spaced throughout the cabin at distances larger than 300 mm from thenearest error sensor are labelled, “MS”.The loudspeakers that were driven by the EZ-ANC II outputs were located asshown in Figure 2 at positions labelled “C1….C4”. All four control speakers were usedfor every test. The primary acoustic source, identified as “P” in Figure 2, was positionedin the boot of the cabin.The first step in each test was to perform cancellation path modelling using thereference signal.  For tonal noise this was completed automatically by the ANC software,but for random noise, the cancellation path identification was performed manually byusing the “view weights” feature of the software and waiting for convergence to occur.Colin Hansen, page number 4Tonal primary disturbance tests were undertaken at 53 Hz , 65 Hz and 111 Hz withand without the first and second harmonics. However, only results for the 55 Hz tests areshown here.  In addition, tests were also conducted for band limited random noise (0-300Hz) and for a noise signal actually recorded in a typical vehicle cabin on-site.Every primary noise type was controlled first using the energy density sensingstrategy, and then the sound pressure sensing strategy.  For both strategies four errorsensors and four control sources were used, with the error sensors located in the vicinityof the driver’s head location, and the control sources in the front and back corners of thecabin as shown in Figure 2.3. RESULTSThe primary and controlled sound fields for each test are shown in Figures 3 to 5. Eachfigure comprises six graphs. The left most graphs correspond to the pressure sensingstrategy and the right most graphs correspond to the energy density sensing strategy. Thetop two graphs in each figure correspond to the sound pressure sensed by the microphoneat the energy density probe or the average pressure at the four error sensors, depending onwhether the results are from an energy density or sound pressure sensing strategy test.The centre two graphs represent the average response of four microphones at 300 mmfrom the energy density probe or nearest pressure error sensor. The bottom two graphsrepresent the energy average of the sound pressure levels measured by the fourmicrophones positioned randomly in the cabin and further than 300 mm from the nearesterror sensor.4. ANALYSISBy combining the reductions achieved for various primary disturbance frequencies, thesound pressure sensing strategy emerges as the preferred strategy for achieving higherlevels of reduction at the error sensors and at distances closer that 300 mm from the errorsensors.  Energy density sensing out-performs sound pressure sensing in terms of thelevel of reduction achieved at positions far away from the error sensors (ie. “atmicrophones spaced throughout cabin”) and also results in a smaller controlled pressuregradient in the vicinity of the error sensors. A large pressure gradient is undesirablebecause it results in large changes in perceived sound pressure level as the driver moveshis/her head around within the “zone of quiet”. Energy density sensing was found toproduce a larger and more uniform zone of quiet than sound pressure sensing; thisoutcome agrees with the findings of the FEA analysis on mining vehicle cabin noisereported previously1.As was expected, greater levels of reduction are achieved when the cabin is excitedat or near its resonance frequency of 53 Hz and these results are illustrated in Figure 3.The sum totals of reductions across the three groups of microphones tested were found toColin Hansen, page number 5be significantly higher for the 53 Hz and 65 Hz primary tone cases than the 111 Hzprimary tone case.  These latter results are not shown here due to lack of space.In some instances, when only the fundamental tone was included in the referenceand primary signals, application of ANC resulted in slight increases in the levels atfrequencies corresponding to the first and second harmonics, due to harmonic distortionof the speaker response.  For this reason the experimental work was repeated with thefirst and second harmonics included as part of the disturbance and reference signals. Thisresulted in similar levels of control of the fundamental without the observed increase inlevel at the second and third harmonics. These latter results are included in Figure 3.With random primary noise generated by the controller, sound pressure sensingachieved greater reduction over certain frequency bands within the spectrum (40 – 80 Hz,120 – 140 Hz, and 230 – 280 Hz). However, this was offset by the increase in noise levelat other frequencies. Results are shown for both sensing strategies in Figure 4.When excited with the tape recording of on-site cabin noise, both sensing strategiesachieved a significant reduction of the peak at 53 Hz and surrounding frequencies, butalso increased the noise level over the 100 Hz to 250 Hz range. These results are shownin Figure 5.It is clear from all three figures that the amount of achievable noise reductionsteadily reduces as the measurement location becomes further from the errormicrophones. This was predicted by the analytical work reported previously1.5. CONCLUSIONSBy using a four-sensor, four-control source real time ANC system, attenuation of tonaland broadband primary disturbances was achieved inside a mining vehicle cabin usingenergy density and sound pressure sensing strategies.  Reduction of tonal disturbanceswas achieved using both strategies, with sound pressure sensing outperforming itscounterpart at locations up to 300 mm away from the error sensors. When the energydensity sensing strategy was used, higher levels of attenuation were achieved at locationsin the cabin further away from the error sensors.When broadband control was implemented, reductions at frequencies at and nearthe low-frequency resonances of the acoustic cavity were achieved at the expense ofincreasing the noise level at other frequencies.  This occurred for both sensing strategies.The use of more error sensors and control sources has the potential to lead to a greaterlevel and larger zone of control in the cabin, as could alternative positioning of thecontrol system sensors and actuators.REFERENCEHansen, C.H., Stanef, D.A. and Morgans, R.C. (2002).  Active control of low frequency sound in a miningvehicle cabin.  Proc. 2002 Annual Conference of the Australian Acoustical Society, Adelaide, SouthAustralia, Nov. 13-15.Colin Hansen, page number 6Figure 3. Real time ANC results for the mining vehicle cabin, showing sound pressurelevels before and after control. Excitation consists of a 53 Hz tone and the first twoharmonics. Figures (a), (c), and (e) show the results the sound pressure sensing strategyand Figures (b), (d) and (f) show the results for the energy density sensing strategy.Figure (a) shows the average of the sound pressure sensed by the 4 error microphones;Figure (b) shows the sound pressure sensed by the microphone located adjacent to theED probe; Figures (c) and (d) show the sound pressure level averaged over the fourmonitor microphones within 300 mm of the furthest error sensor; and Figures (e) and (f)show the sound pressure level averaged over the four monitor microphones distributedthroughout the cabin at a distance of at least 300 mm from the nearest error sensor.(a) (b)(c) (d)(e) (f)Colin Hansen, page number 7Figure 4. Real time ANC results for the mining vehicle cabin, showing sound pressurelevels before and after control. Excitation consists of band limited random noise between0 and 300 Hz. Figures (a), (c), and (e) show the results the sound pressure sensingstrategy and Figures (b), (d) and (f) show the results for the energy density sensingstrategy. Figure (a) shows the average of the sound pressure sensed by the 4 errormicrophones; Figure (b) shows the sound pressure sensed by the microphone locatedadjacent to the ED probe; Figures (c) and (d) show the sound pressure level averagedover the four monitor microphones within 300 mm of the furthest error sensor; andFigures (e) and (f) show the sound pressure level averaged over the four monitormicrophones distributed throughout the cabin at a distance of at least 300 mm from thenearest error sensor.(a) (b)(c)(d)(e) (f)Colin Hansen, page number 8Figure 5. Real time ANC results for the mining vehicle cabin, showing sound pressurelevels before and after control. Excitation consists of noise recorded in a mining vehiclecabin and band limited to 300 Hz. Figures (a), (c), and (e) show the results the soundpressure sensing strategy and Figures (b), (d) and (f) show the results for the energydensity sensing strategy. Figure (a) shows the average of the sound pressure sensed bythe 4 error microphones; Figure (b) shows the sound pressure sensed by the microphonelocated adjacent to the ED probe; Figures (c) and (d) show the sound pressure levelaveraged over the four monitor microphones within 300 mm of the furthest error sensor;and Figures (e) and (f) show the sound pressure level averaged over the four monitormicrophones distributed throughout the cabin at a distance of at least 300 mm from thenearest error sensor.(a)(d)(b)(c)(e) (f)

Real time control of sound pressure and energy density in a mining vehicle cabin

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Real time control of sound pressure and energy density in a mining vehicle cabin

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Real time control of sound pressure and energy density in a mining vehicle cabin

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