Fans and blowers are used in many applications. For example, they have been used for blowing hot air away from power generators to cool down the generators. In some situations, the noise created by the fans or blowers can be very annoying to engineers working nearby. It is well known that long term exposure to noisy environment may have negative impact to people's hearing. Moreover, people tend to get tired more easily in noisy environment.
1. Past Approaches to Fan Noise Reduction
There are some approaches to fan noise reduction. Some of them require a redesign of the fans. Others have been proven to only work for computers. All of them may not be directly applicable to legacy fans or blowers in civilian and military systems. An ideal solution should be a low cost and portable active noise cancellation system that can be used in many diverse scenarios. The near field behavior of fan noise is complicated. However, at far field, the fan noise pattern is regular, which is similar to a spherical wave. The far field is defined as the square of the fan diameter divided by the sound wavelength. For a fan having a diameter of 1 ft., the distance to far field is about 1 ft. for a frequency of 1 kHz. One challenge is that the fan noise may consist of a band of frequencies, making it harder to suppress even at far field.
One prior active noise reduction system is disclosed in U.S. Pat. No. 9,117,457, issued on Aug. 25, 2015, by C. Kwan and J. Zhou, “Compact Plug-In Noise Cancellation Device,” which is useful for speech enhancement of cell phones and stethoscopes, but not as efficient when applied to fan noise reduction.
2. Proposed Active Noise Reduction Approach
The present invention proposes a novel and high-performance system to cancel fan or blower noise. The goal is to significantly reduce the noise at far field, which is more than 0.3 meter (1 ft.) for a fan size of 1 ft. in diameter and a noise frequency of 1 kHz. First, the present invention proposes to utilize 2 microphones: one to pick up the fan noise and the other one to pick up the noisy signal at far field. Second, the present invention proposes a portable loudspeaker that can be easily placed near the fan. The loudspeaker broadcasts omni-directional anti-phase signals to reduce the noise at far field. The present invention should perform well as the loudspeaker and the fan will look like point sources from the far field. Third, a real-time Digital Signal Processor (DSP) or Field Programmable Gate Array (FPGA) with fast adaptive filter is used to process the 2 microphone signals and generate the anti-phase signal. The adaptive filter uses the second microphone (fan noise) as a reference to generate an out-of-phase signal, which can then suppress the far field noise.
The key advantages of the present invention are briefly summarized as follows:
Details of the proposed system and software algorithm will be described below.
One embodiment of the present invention is to provide a portable system, which can effectively reduce fan or blower noise at far field.
Another embodiment of the present invention is to perform active noise reduction without modifying the fans and blowers.
Another embodiment of the present invention is to use a loudspeaker to generate anti-phase signals which can cancel the fan/blower noise at far field. The loudspeaker should be placed near the fan/blower so that both the loudspeaker and the fan will become a point source from far field.
Another embodiment of the present invention is to use two microphones. One for picking up the noise at far field, and the other one for picking up fan noise near the fan.
Another embodiment of the present invention is that the active noise reduction algorithms can be implemented in a Digital Signal Processor (DSP) and a Field Programmable Gate Array (FPGA).
Overall Active Noise Reduction System Architecture
As shown in
Active Noise Reduction at Far Field
As shown in
Mathematically, the far field condition is related to the size of the fan (D), wavelength of sound (λ), and distance (z) by
Assuming a sound speed of 300 m/s and a fan diameter of 0.3 meter, the values of D2/λ will be 0.15 meter for f=500 Hz, 0.3 meter for f=1,000 Hz, and 0.6 meter for f=2,000 Hz. So, at 1 meter away, the sound field will be uniform and hence it should be easier to suppress.
Real-Time Adaptive Noise Reduction Algorithm
The signal flow in a typical active noise reduction system is shown in
The following paragraphs summarize the principle of three adaptive algorithms and simulation results. It should be noted that the simulation results were for a different application scenario where a small quiet zone is created by active noise cancellation. Although the application scenario is different from fan noise reduction, the simulations clearly demonstrate the performance of the adaptive algorithms and is adaptable to fan noise reduction.
A. Filtered X-LMS
In active noise control (see
The FX-LMS algorithm can be summarized as follows:
1. Input the reference signal x(n) from the Mic 2 and the error signal e(n) from Microphone 1, all from the input ports;
2. Compute the anti-noise y(n);
3. Output the anti-noise y(n) to the output port to drive the canceling loudspeaker;
4. Compute the filtered X version of x′(n);
5. Update the coefficients of adaptive filter W(z); and
6. Repeat the procedure for the next iteration.
Note that the total number of memory locations required for this algorithm is 2(N+M) plus some parameters.
The FX-LMS is implemented by performing extensive simulation studies. The following parameters are used: filter learning rate=0.01, frame size=512, and sampling rate 8 kHz. The narrowband results are shown in
Attenuation=15.91 dB for narrow band signal
Attenuation=7.65 dB for NASA noise file which contains actual noise in the International Space Station.
B. Filtered U-LMS
In practice, the control signal from the loudspeaker may be picked up by the reference mic and a positive feedback loop may occur. To avoid the positive feedback, a Filtered U-LMS (FU-LMS) algorithm was proposed in an article by, S. M. Kuo and D. R. Morgan, “Active Noise Control: A Tutorial,” Proc. of the IEEE, Vol. 87, No. 6, June 1999.
The FU-LMS as shown in
a. Input the reference signal x(n) and the error signal e(n) from the input ports;
b. Compute the anti-noise y(n);
c. Output the anti-noise y(n) to the output port to drive the canceling loudspeaker;
d. Perform the filtered U operation;
e. Update the coefficients of the adaptive filters A(z) and B(z); and
f. Repeat the algorithm for the next iteration.
The following parameters were used: adaptation rate=0.01, frame size=512, and sampling rate 8 kHz. The narrowband results are shown in
Attenuation=14.41 dB for narrow band signal
Attenuation=6.93 dB for NASA noise file
C. FD-FX-LMS-BS
The present invention utilizes a frequency-domain adaptive filter (FD-FX-LMS-BS) as shown in the dotted block in
The Narrowband results are shown in
Attenuation=14.36 dB for narrow band signal
Attenuation=10.21 dB for NASA file
It will be apparent to those skilled in the art that various modifications and variations can be made to the system and method of the present disclosure without departing from the scope or spirit of the disclosure. It should be perceived that the illustrated embodiments are only preferred examples of describing the invention and should not be taken as limiting the scope of the invention.
Number | Name | Date | Kind |
---|---|---|---|
5337366 | Eguchi | Aug 1994 | A |
9117457 | Kwan | Aug 2015 | B2 |
20100014685 | Wurm | Jan 2010 | A1 |
20100172511 | Togawa | Jul 2010 | A1 |
20160163304 | Lee | Jun 2016 | A1 |
20160163305 | Lee | Jun 2016 | A1 |
20160372106 | Hanazono | Dec 2016 | A1 |
20170276398 | Hanazono | Sep 2017 | A1 |
Entry |
---|
S. M. Kuo et al., Design of Active Noise Control Systems With the TMS320 Family, 1996. |
S. M. Kuo and D. R. Morgan, Active Noise Control: A Tutorial, Proceedings of the IEEE, vol. 87, No. 6, Jun. 1999. |
C. Kwan, J. Zhou, J. Qiao, G. Liu, and B. Ayhan, “A High Performance Approach to Local Active Noise Reduction,” IEEE Conference on Decision and Control, Las Vegas, Dec. 2016. |
J. Zhou, B. Ayhan, C. Kwan, and O. S. Sands, “A High Performance Approach to Minimizing Interactions between Inbound and Outbound Signals in Helmet,” SPIE Conference on Defense, Security, and Applications, Baltimore, 2012. |
Number | Date | Country | |
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20180151171 A1 | May 2018 | US |