Applications of adaptive feedback active noise control ... - IEEE Xplore
216
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
Applications of Adaptive Feedback Active Noise Control System Sen M. Kuo, Xuan Kong, and Woon S. Gan
Abstract—This paper presents the experimental results of using the single-channel adaptive feedback active noise control (AFANC) algorithm with an innovative setup to achieve global attenuation of industrial machine noise in settings such as large manufacturing plants. An effective solution of using active/passive techniques and three distributed error sensors is proposed. The performance of the AFANC algorithm is verified by real-time experiments using the TMS320C32 DSP to control vibratory bowl and welding power generator noises. The experiments results show that this singlechannel AFANC system can effectively reduce the noise level and is cost effective, portable, and easy for installation to control many noisy sources in large spaces. Index Terms—Acoustical configurations for active noise control (ANC), adaptive feedback ANC (AFANC), adaptive signal processing, applications of ANC, global ANC.
I. INTRODUCTION
A
COUSTIC noise problems become more serious as industrial equipment such as engines, blowers, fans, transformers, and compressors increase in number and use. Traditional passive silencers are valued for their ability to attenuate noise over a broad frequency range; however, they are relatively large, costly, and ineffective at low frequencies. Active noise control (ANC) [1]–[4] involves an electroacoustic or electromechanical system that cancels the primary (unwanted) noise based on the principle of superposition. That is, an antinoise of equal amplitude and opposite phase is generated and combined with the primary noise, resulting in the cancellation of both noises. The ANC system efficiently attenuates low frequency noise where passive methods are either ineffective or tend to be very expensive or bulky. ANC is developing rapidly because it permits improvements in noise control, often with potential benefits in size, weight, volume, and cost. Single-channel broad-band feedforward ANC systems have a single reference sensor, single secondary source, and single error sensor as illustrated in Fig. 1. The reference sensor such as . This signal a microphone measures the reference signal is then processed by the ANC system to generate the control to drive the secondary source. The error sensor signal monitors the performance of the ANC system by measuring . The error is the result of acoustic the residual error and , a filtered version of . The superposition of Manuscript received January 23, 2002. Manuscript received in final form August 19, 2002. Recommended by Associate Editor D. S. Bayard. S. M. Kuo is with the Department of Electrical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: [email protected]). X. Kong was with the Department of Electrical Engineering, Northern Illinois University, DeKalb, IL 60115 USA. He is now with NeuroMetrix, Waltham, MA 02451 USA (e-mail: [email protected]). W. S. Gan is with the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore (e-mail: [email protected]). Digital Object Identifier 10.1109/TCST.2003.809252
Fig. 1. Block diagram of single-channel broad-band feedforward ANC system.
filter is commonly referenced to as the secondary-path , and its effect must be compensated. In practical ANC systems, the secondary path includes the digital-to-analog converter, reconstruction filter, power amplifier, loudspeaker, acoustic path from loudspeaker to error microphone, error microphone, preamplifier, antialiasing filter, and analog-to-digital converter. The filtered-X least-mean-square (FXLMS) algorithm [5], which places the secondary-path model in the reference signal path to the weight update of the LMS algorithm, is generally the most effective approach. As illustrated in Fig. 1, the secondary signal is generated as (1) and are the cois the filter efficient and signal vectors, respectively, and order. The adaptive filter minimizes the instantaneous squared error using the FXLMS algorithm
where
(2) where
is the adaptive gain and (3)
is the estimated impulse response of the secIn (3), and denotes linear convolution. ondary-path filter or its source may not be availThe primary noise able during the ANC operation for applications like spatially incoherent noise generated from turbulence, noise generated from many sources and propagation paths, and induced resonance where no coherent reference signal is available. Therefore, a crucial step in such an ANC system is to estimate the primary and to use it as the reference signal for the ANC noise [6]. This paper emphasizes the practical aspects of filter the single-channel AFANC system for global noise control applications.
1063-6536/03$17.00 © 2003 IEEE
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
217
Fig. 2. Block diagram of adaptive feedback ANC system.
II. ADAPTIVE FEEDBACK ANC An AFANC system using the FXLMS algorithm is illustrated is used to synin Fig. 2, where the secondary-path estimate thesize the reference signal and to compensate for the secondary is synthesized as path. The reference signal
(a)
(4) , are the coefficients of the where th order FIR filter used to estimate the secondary path . Estimation of can be performed using the off-line modeling technique summarized in [2, Sec. 3.3.4]. If on-line modeling is required by a given application, the additive random noise technique given in [2, Sec. 7.3] can be used. The secondary is generated via signal (5)
(b) Fig. 3. Experimental setup of using single-channel adaptive feedback ANC system for global noise control. (a) Side view. (b) Top view.
, are the coefficients of where at time . The single-channel AFANC algorithm is very effective and portable since no reference sensor is required. This algorithm was implemented in real time on a TMS320C32 board to control electrical vibratory bowl (for mixing material) and welding power generator engine noises. The noise level depends on the machine load such as the amount of material in the vibratory bowl and the rotation speed of the engine. Since the step size given in (2) needs to be inversely proportional to the noise level, larger noise level variations may lead to algorithm instability for a fixed step size. To improve the robustness of the algorithm, a normalized FXLMS algorithm is used
(6) where the filtered reference signal is given by (7) and the power estimate (8)
is based on a first-order recursive filter and the constant places an upper bound on the adaptive gain when estimated power is too small. III. EXPERIMENTAL SETUP In order to use a single-channel AFANC algorithm for global control of industrial machine noise in large manufacturing plants, an innovative setup using both passive and active control was designed and built. As illustrated in Fig. 3, the noisy machine (an electrical vibratory bowl) is partially surrounded with an open-ended acrylic cylinder, since the enclosed noise sources must have openings for air intake, gas exhaust, heat dissipation, and access to the machine. The passive control uses a layer of absorbing material attached to the inside of the cylinder wall to provide the higher insertion loss (more than 20 dB) above 500 Hz and to confine the noise to radiate upward only. The secondary source (canceling loudspeaker) is hung below an umbrella-shaped dome made by acrylic. This acrylic dome is placed on the top of the noise source in order to reflect the upward noise back to the canceling loudspeaker. In this configuration, the operators can have access to the machine and the heat and/or exhaust gas generated by the machine can be dissipated through the wide gap between the dome and the
218
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
cylinder. The efficiency of the passive technique depends on the ratio of the noise wavelength to the thickness of the absorbing layers. For the longer sound wavelength (lower frequency), a thicker acoustic form is needed. As shown in Fig. 3, the canceling loudspeaker enclosed in the PVC pipe is hung under the acrylic dome with three chains. In the first experiment, a single error microphone was placed at the center of reflection from the acrylic dome, which is in between the acrylic dome and the canceling loudspeaker. While the ANC system was able to attenuate some harmonics, its global noise reduction capability was compromised by two factors: the slow convergence of the AFANC algorithm and the failure to control all harmonics. This is because of the lack of coherence between the primary and reference noise. To minimize the energy of the , it is important to obtain high coherence at residual noise frequencies for which the primary noise energy is significant. The coherence can be improved by reducing the flow velocity, using multiple sensors, and using distributed sensors. In the second experiment, a higher coherence error signal was obtained by combining the redundant information provided by three error microphones evenly distributed between the gap of the acrylic dome and cylinder where the undesired noise radiated. The three error microphones [shown in Fig. 3(b)] are attached in the middle of the chain and are face upward. To avoid adding computational complexity for using the multiple-channel AFANC system [2] with multiple analog input channels, the microphone outputs are simply mixed (averaged) to form a used by the simple single-channel AFANC algosingle rithm as shown in Fig. 2, which needs an inexpensive digital signal processor for implementation. Therefore, the ANC hardware needs only one analog input channel (including an A/D converter, an analog low-pass filter, a preamplifier), thus further reducing the cost of the system. This configuration also provides higher magnitude response at the error microphone locations in the frequency range of interest from 100 Hz to 400 Hz. This is because the average of three error signals is equivalent to low-pass filtering, thus increases the coherence at the desired low-frequency range. IV. EXPERIMENT RESULTS The experimental setup shown in Fig. 3 is located in an 800 square foot lab with many lab benches and equipment. The system utilizes a 6-in secondary loudspeaker (SuperPro PW640) and a power amplifier (QSC Mx700) to drive the canceling loudspeaker. The three error microphones (Shure Brothers SM98-A) being of cardiod type have the effect of increasing the direct to reverberant signal level, resulting in improved performance. The microphone outputs are combined and amplified using a preamplifier (Symetric SX20). A 1 1 AFANC algorithm shown in Fig. 2 was implemented on the TMS320C32 floating-point digital signal processor with two analog input–output channels with was ob16-bit converters. The secondary-path estimate tained using the off-line modeling technique [2] with estimate . Adaptive AFANC filter was of filter order of . order The performance is measured as noise reduction at the error microphone positions. The first noisy machine used for
Fig. 4. Performance of AFANC system for vibratory bowl. The dotted line represents the AFANC system was turned off, and the solid line shows that the AFANC system was turned on.
real-time experiments in this research is an electrical vibratory bowl used in some manufacturing plants for mixing material. There are strong even harmonics at frequencies 120, 240, 360, 600, 660, and 720 Hz. To improve the noise reduction at high frequencies, a layer of acoustic form was attached inside the acrylic cylinder as shown in Fig. 3. The acoustic treatment reduces the high-frequency harmonics (600, 660, and 720 Hz), but enhanced the second harmonic at 120 Hz, which can be effectively attenuated by the AFANC system. The performance of the AFANC system measured by an HP 35670A dynamic signal analyzer is shown in Fig. 4, where the dotted line shows the primary noise spectrum measured at the error microphones when the AFANC system was turned off. When the system was turned on, the primary noise was attenuated immediately, and the residual noise spectrum is shown as the solid line of Fig. 4. These two measurements are done with identical equipment settings, thus provide relative performances before and after noise control. Compared with the primary noise shown in the dotted line, we observed that the harmonic components at 120, 240, and 360 Hz are reduced significantly. The second harmonic at 120 Hz has been attenuated by more than 60 dB. During the real-time experiments, we observed that the AFANC system with three microphones converged much faster than the system that used a single microphone. The experiments were conducted in the DSP laboratory with many lab benches, chairs, and equipment that is similar in setting to manufacturing plants. The performance of the single-channel AFANC system for global noise control is measured at ten locations surrounding the experimental setup inside the lab using sound pressure level (SPL) meters. The (in unit meters) results are summarized in Table I, where is the distance of meter location from the edge of acrylic is the height of the meter from the floor. Table I dome and shows the overall noise reduction average of 11.3 dB by using the combination of passive and active noise control shown in Fig. 3. The passive noise control using a layer of
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
219
TABLE I SUMMARY OF GLOBAL NOISE REDUCTION USING SINGLE-CHANNEL AFANC SYSTEM. THE NOISE LEVEL IS MEASURED IN UNIT dB
acoustic form lined inside the acrylic cylinder has an effect of confining the noise to radiate upward and then canceling it with the AFANC system. Thus the global noise control in a large three-dimensional space can be achieved using a low-cost single-channel AFANC system. The last column of Table I shows the residual noise is about 63 dB in the whole lab except location 4, which is about 0.25 m from the floor and is very close to the noise source. It is important to note that Table I shows the performance measured by the dB meter at different locations, which indicates the averaged SPL over the wide frequency range. However, Fig. 4 shows the noise magnitude spectra measured at the error microphone by the signal analyzer in much narrower frequency range. To further test the performance of the AFANC system for different applications, a welding power generator powered by an engine was also used in experiments. This machine is usually used in the residential areas where engine noise is annoying. First the engine noises were recorded using Sony DAT when the engine was running at high idle (3700 r/min) and low idle (2200 r/min) speed. The recording microphone (SM-98A) was placed close to the outlet of the mechanical muffler of engine. A microphone windscreen was used to reduce airflow caused by the exhaust gas. The recorded noise was played in the lab using a 10-in loudspeaker, which was placed inside the acrylic cylinder as a noise source to replace the vibratory bowl shown in Fig. 3. Fig. 5 shows the noise canceling performance of the single-channel AFANC system with the 3700 r/min engine noise. A passive mechanical muffler attenuates the high-frequency harmonics, which is ineffective at low-frequency ranges. Note that the narrowband components at the first three harmonics (61, 122, and 183 Hz) were canceled out completely. This experiment shows that the single-channel AFANC algorithm with the experimental setup shown in Fig. 3 able to achieve global noise control from different noise sources. After the satisfactory performance was achieved in the lab using the recorded engine noises, the experiments were conducted on the actual welding power generator, which was
Fig. 5. Performance of AFANC algorithm for the recorded noise from the welding power generator running at 3700 r/min.
located at the loading dock outside a building. Real-time experiments were conducted for both engine speeds. The experimental setup is illustrated in Fig. 6, where two 6-in loudspeakers are placed on the top of power generator. The error microphone was placed on top of the mechanical muffler outlet. The two loudspeakers are driven by the same secondary generated by the single-channel AFANC algorithm signal shown in Fig. 2. Fig. 7 shows an example of performance when the engine was running at 2200 r/min. The dotted line shows the spectrum of the primary noise measured at the error microphone when the AFANC system was turned off. The solid line shows the residual noise spectrum measured by the error microphone when the AFANC system was turned on. It is shown that the dominant harmonics at 76, 114, 152, and 190 Hz were attenuated at the broad-band noise level. The larger size of the experimental setup
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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
Fig. 6. Experimental setup for reducing noise on the actual welding power generator.
shown in Fig. 3 is proposed to achieve global control of welding power generator noise in future research. V. CONCLUSION An innovative experimental setup to achieve global attenuation of industrial machine noise in large manufacturing plants using a single-channel AFANC system was proposed in this paper. A partial enclosed acoustic barrier is designed to reduce noise components at high-frequency ranges. To increase the coherence between the reference and error signals, three distributed error sensors are used and their outputs were mixed to obtain an error signal. The performance of the AFANC algorithm was verified by real-time experiments using the TMS320C32 board to control vibratory bowl and welding power generator noises. The experiment results showed that this single-channel AFANC system can effectively reduce the noise level at different locations in the large lab. AFANC systems use error sensors only are cost effective, portable, and
Fig. 7. Performance of AFANC on an actual welding power generator when the engine was running at 2200 r/min.
easy for installation to control many noisy sources in large spaces. REFERENCES [1] P. A. Nelson and S. J. Elliott, Active Control of Sound. San Diego, CA: Academic, 1992. [2] S. M. Kuo and D. R. Morgan, Active Noise Control Systems: Algorithms and DSP Implementations. New York: Wiley, 1996. [3] , “Active noise control: A tutorial review,” Proc. IEEE, vol. 87, pp. 943–973, June 1999. [4] S. J. Elliott, Signal Processing for Active Control. San Diego, CA: Academic Press, 2001. [5] D. R. Morgan, “An analysis of multiple correlation cancellation loops with a filter in the auxiliary path,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, pp. 454–467, Aug. 1980. [6] S. M. Kuo and D. Vijayan, “Adaptive algorithms and experimental verification of feedback active noise control systems,” Noise Control Eng. J., vol. 47, pp. 37–46, Mar.–Apr. 1994.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
Applications of Adaptive Feedback Active Noise Control System Sen M. Kuo, Xuan Kong, and Woon S. Gan
Abstract—This paper presents the experimental results of using the single-channel adaptive feedback active noise control (AFANC) algorithm with an innovative setup to achieve global attenuation of industrial machine noise in settings such as large manufacturing plants. An effective solution of using active/passive techniques and three distributed error sensors is proposed. The performance of the AFANC algorithm is verified by real-time experiments using the TMS320C32 DSP to control vibratory bowl and welding power generator noises. The experiments results show that this singlechannel AFANC system can effectively reduce the noise level and is cost effective, portable, and easy for installation to control many noisy sources in large spaces. Index Terms—Acoustical configurations for active noise control (ANC), adaptive feedback ANC (AFANC), adaptive signal processing, applications of ANC, global ANC.
I. INTRODUCTION
A
COUSTIC noise problems become more serious as industrial equipment such as engines, blowers, fans, transformers, and compressors increase in number and use. Traditional passive silencers are valued for their ability to attenuate noise over a broad frequency range; however, they are relatively large, costly, and ineffective at low frequencies. Active noise control (ANC) [1]–[4] involves an electroacoustic or electromechanical system that cancels the primary (unwanted) noise based on the principle of superposition. That is, an antinoise of equal amplitude and opposite phase is generated and combined with the primary noise, resulting in the cancellation of both noises. The ANC system efficiently attenuates low frequency noise where passive methods are either ineffective or tend to be very expensive or bulky. ANC is developing rapidly because it permits improvements in noise control, often with potential benefits in size, weight, volume, and cost. Single-channel broad-band feedforward ANC systems have a single reference sensor, single secondary source, and single error sensor as illustrated in Fig. 1. The reference sensor such as . This signal a microphone measures the reference signal is then processed by the ANC system to generate the control to drive the secondary source. The error sensor signal monitors the performance of the ANC system by measuring . The error is the result of acoustic the residual error and , a filtered version of . The superposition of Manuscript received January 23, 2002. Manuscript received in final form August 19, 2002. Recommended by Associate Editor D. S. Bayard. S. M. Kuo is with the Department of Electrical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: [email protected]). X. Kong was with the Department of Electrical Engineering, Northern Illinois University, DeKalb, IL 60115 USA. He is now with NeuroMetrix, Waltham, MA 02451 USA (e-mail: [email protected]). W. S. Gan is with the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore (e-mail: [email protected]). Digital Object Identifier 10.1109/TCST.2003.809252
Fig. 1. Block diagram of single-channel broad-band feedforward ANC system.
filter is commonly referenced to as the secondary-path , and its effect must be compensated. In practical ANC systems, the secondary path includes the digital-to-analog converter, reconstruction filter, power amplifier, loudspeaker, acoustic path from loudspeaker to error microphone, error microphone, preamplifier, antialiasing filter, and analog-to-digital converter. The filtered-X least-mean-square (FXLMS) algorithm [5], which places the secondary-path model in the reference signal path to the weight update of the LMS algorithm, is generally the most effective approach. As illustrated in Fig. 1, the secondary signal is generated as (1) and are the cois the filter efficient and signal vectors, respectively, and order. The adaptive filter minimizes the instantaneous squared error using the FXLMS algorithm
where
(2) where
is the adaptive gain and (3)
is the estimated impulse response of the secIn (3), and denotes linear convolution. ondary-path filter or its source may not be availThe primary noise able during the ANC operation for applications like spatially incoherent noise generated from turbulence, noise generated from many sources and propagation paths, and induced resonance where no coherent reference signal is available. Therefore, a crucial step in such an ANC system is to estimate the primary and to use it as the reference signal for the ANC noise [6]. This paper emphasizes the practical aspects of filter the single-channel AFANC system for global noise control applications.
1063-6536/03$17.00 © 2003 IEEE
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
217
Fig. 2. Block diagram of adaptive feedback ANC system.
II. ADAPTIVE FEEDBACK ANC An AFANC system using the FXLMS algorithm is illustrated is used to synin Fig. 2, where the secondary-path estimate thesize the reference signal and to compensate for the secondary is synthesized as path. The reference signal
(a)
(4) , are the coefficients of the where th order FIR filter used to estimate the secondary path . Estimation of can be performed using the off-line modeling technique summarized in [2, Sec. 3.3.4]. If on-line modeling is required by a given application, the additive random noise technique given in [2, Sec. 7.3] can be used. The secondary is generated via signal (5)
(b) Fig. 3. Experimental setup of using single-channel adaptive feedback ANC system for global noise control. (a) Side view. (b) Top view.
, are the coefficients of where at time . The single-channel AFANC algorithm is very effective and portable since no reference sensor is required. This algorithm was implemented in real time on a TMS320C32 board to control electrical vibratory bowl (for mixing material) and welding power generator engine noises. The noise level depends on the machine load such as the amount of material in the vibratory bowl and the rotation speed of the engine. Since the step size given in (2) needs to be inversely proportional to the noise level, larger noise level variations may lead to algorithm instability for a fixed step size. To improve the robustness of the algorithm, a normalized FXLMS algorithm is used
(6) where the filtered reference signal is given by (7) and the power estimate (8)
is based on a first-order recursive filter and the constant places an upper bound on the adaptive gain when estimated power is too small. III. EXPERIMENTAL SETUP In order to use a single-channel AFANC algorithm for global control of industrial machine noise in large manufacturing plants, an innovative setup using both passive and active control was designed and built. As illustrated in Fig. 3, the noisy machine (an electrical vibratory bowl) is partially surrounded with an open-ended acrylic cylinder, since the enclosed noise sources must have openings for air intake, gas exhaust, heat dissipation, and access to the machine. The passive control uses a layer of absorbing material attached to the inside of the cylinder wall to provide the higher insertion loss (more than 20 dB) above 500 Hz and to confine the noise to radiate upward only. The secondary source (canceling loudspeaker) is hung below an umbrella-shaped dome made by acrylic. This acrylic dome is placed on the top of the noise source in order to reflect the upward noise back to the canceling loudspeaker. In this configuration, the operators can have access to the machine and the heat and/or exhaust gas generated by the machine can be dissipated through the wide gap between the dome and the
218
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
cylinder. The efficiency of the passive technique depends on the ratio of the noise wavelength to the thickness of the absorbing layers. For the longer sound wavelength (lower frequency), a thicker acoustic form is needed. As shown in Fig. 3, the canceling loudspeaker enclosed in the PVC pipe is hung under the acrylic dome with three chains. In the first experiment, a single error microphone was placed at the center of reflection from the acrylic dome, which is in between the acrylic dome and the canceling loudspeaker. While the ANC system was able to attenuate some harmonics, its global noise reduction capability was compromised by two factors: the slow convergence of the AFANC algorithm and the failure to control all harmonics. This is because of the lack of coherence between the primary and reference noise. To minimize the energy of the , it is important to obtain high coherence at residual noise frequencies for which the primary noise energy is significant. The coherence can be improved by reducing the flow velocity, using multiple sensors, and using distributed sensors. In the second experiment, a higher coherence error signal was obtained by combining the redundant information provided by three error microphones evenly distributed between the gap of the acrylic dome and cylinder where the undesired noise radiated. The three error microphones [shown in Fig. 3(b)] are attached in the middle of the chain and are face upward. To avoid adding computational complexity for using the multiple-channel AFANC system [2] with multiple analog input channels, the microphone outputs are simply mixed (averaged) to form a used by the simple single-channel AFANC algosingle rithm as shown in Fig. 2, which needs an inexpensive digital signal processor for implementation. Therefore, the ANC hardware needs only one analog input channel (including an A/D converter, an analog low-pass filter, a preamplifier), thus further reducing the cost of the system. This configuration also provides higher magnitude response at the error microphone locations in the frequency range of interest from 100 Hz to 400 Hz. This is because the average of three error signals is equivalent to low-pass filtering, thus increases the coherence at the desired low-frequency range. IV. EXPERIMENT RESULTS The experimental setup shown in Fig. 3 is located in an 800 square foot lab with many lab benches and equipment. The system utilizes a 6-in secondary loudspeaker (SuperPro PW640) and a power amplifier (QSC Mx700) to drive the canceling loudspeaker. The three error microphones (Shure Brothers SM98-A) being of cardiod type have the effect of increasing the direct to reverberant signal level, resulting in improved performance. The microphone outputs are combined and amplified using a preamplifier (Symetric SX20). A 1 1 AFANC algorithm shown in Fig. 2 was implemented on the TMS320C32 floating-point digital signal processor with two analog input–output channels with was ob16-bit converters. The secondary-path estimate tained using the off-line modeling technique [2] with estimate . Adaptive AFANC filter was of filter order of . order The performance is measured as noise reduction at the error microphone positions. The first noisy machine used for
Fig. 4. Performance of AFANC system for vibratory bowl. The dotted line represents the AFANC system was turned off, and the solid line shows that the AFANC system was turned on.
real-time experiments in this research is an electrical vibratory bowl used in some manufacturing plants for mixing material. There are strong even harmonics at frequencies 120, 240, 360, 600, 660, and 720 Hz. To improve the noise reduction at high frequencies, a layer of acoustic form was attached inside the acrylic cylinder as shown in Fig. 3. The acoustic treatment reduces the high-frequency harmonics (600, 660, and 720 Hz), but enhanced the second harmonic at 120 Hz, which can be effectively attenuated by the AFANC system. The performance of the AFANC system measured by an HP 35670A dynamic signal analyzer is shown in Fig. 4, where the dotted line shows the primary noise spectrum measured at the error microphones when the AFANC system was turned off. When the system was turned on, the primary noise was attenuated immediately, and the residual noise spectrum is shown as the solid line of Fig. 4. These two measurements are done with identical equipment settings, thus provide relative performances before and after noise control. Compared with the primary noise shown in the dotted line, we observed that the harmonic components at 120, 240, and 360 Hz are reduced significantly. The second harmonic at 120 Hz has been attenuated by more than 60 dB. During the real-time experiments, we observed that the AFANC system with three microphones converged much faster than the system that used a single microphone. The experiments were conducted in the DSP laboratory with many lab benches, chairs, and equipment that is similar in setting to manufacturing plants. The performance of the single-channel AFANC system for global noise control is measured at ten locations surrounding the experimental setup inside the lab using sound pressure level (SPL) meters. The (in unit meters) results are summarized in Table I, where is the distance of meter location from the edge of acrylic is the height of the meter from the floor. Table I dome and shows the overall noise reduction average of 11.3 dB by using the combination of passive and active noise control shown in Fig. 3. The passive noise control using a layer of
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
219
TABLE I SUMMARY OF GLOBAL NOISE REDUCTION USING SINGLE-CHANNEL AFANC SYSTEM. THE NOISE LEVEL IS MEASURED IN UNIT dB
acoustic form lined inside the acrylic cylinder has an effect of confining the noise to radiate upward and then canceling it with the AFANC system. Thus the global noise control in a large three-dimensional space can be achieved using a low-cost single-channel AFANC system. The last column of Table I shows the residual noise is about 63 dB in the whole lab except location 4, which is about 0.25 m from the floor and is very close to the noise source. It is important to note that Table I shows the performance measured by the dB meter at different locations, which indicates the averaged SPL over the wide frequency range. However, Fig. 4 shows the noise magnitude spectra measured at the error microphone by the signal analyzer in much narrower frequency range. To further test the performance of the AFANC system for different applications, a welding power generator powered by an engine was also used in experiments. This machine is usually used in the residential areas where engine noise is annoying. First the engine noises were recorded using Sony DAT when the engine was running at high idle (3700 r/min) and low idle (2200 r/min) speed. The recording microphone (SM-98A) was placed close to the outlet of the mechanical muffler of engine. A microphone windscreen was used to reduce airflow caused by the exhaust gas. The recorded noise was played in the lab using a 10-in loudspeaker, which was placed inside the acrylic cylinder as a noise source to replace the vibratory bowl shown in Fig. 3. Fig. 5 shows the noise canceling performance of the single-channel AFANC system with the 3700 r/min engine noise. A passive mechanical muffler attenuates the high-frequency harmonics, which is ineffective at low-frequency ranges. Note that the narrowband components at the first three harmonics (61, 122, and 183 Hz) were canceled out completely. This experiment shows that the single-channel AFANC algorithm with the experimental setup shown in Fig. 3 able to achieve global noise control from different noise sources. After the satisfactory performance was achieved in the lab using the recorded engine noises, the experiments were conducted on the actual welding power generator, which was
Fig. 5. Performance of AFANC algorithm for the recorded noise from the welding power generator running at 3700 r/min.
located at the loading dock outside a building. Real-time experiments were conducted for both engine speeds. The experimental setup is illustrated in Fig. 6, where two 6-in loudspeakers are placed on the top of power generator. The error microphone was placed on top of the mechanical muffler outlet. The two loudspeakers are driven by the same secondary generated by the single-channel AFANC algorithm signal shown in Fig. 2. Fig. 7 shows an example of performance when the engine was running at 2200 r/min. The dotted line shows the spectrum of the primary noise measured at the error microphone when the AFANC system was turned off. The solid line shows the residual noise spectrum measured by the error microphone when the AFANC system was turned on. It is shown that the dominant harmonics at 76, 114, 152, and 190 Hz were attenuated at the broad-band noise level. The larger size of the experimental setup
220
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 11, NO. 2, MARCH 2003
Fig. 6. Experimental setup for reducing noise on the actual welding power generator.
shown in Fig. 3 is proposed to achieve global control of welding power generator noise in future research. V. CONCLUSION An innovative experimental setup to achieve global attenuation of industrial machine noise in large manufacturing plants using a single-channel AFANC system was proposed in this paper. A partial enclosed acoustic barrier is designed to reduce noise components at high-frequency ranges. To increase the coherence between the reference and error signals, three distributed error sensors are used and their outputs were mixed to obtain an error signal. The performance of the AFANC algorithm was verified by real-time experiments using the TMS320C32 board to control vibratory bowl and welding power generator noises. The experiment results showed that this single-channel AFANC system can effectively reduce the noise level at different locations in the large lab. AFANC systems use error sensors only are cost effective, portable, and
Fig. 7. Performance of AFANC on an actual welding power generator when the engine was running at 2200 r/min.
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