CN113257214A - Active noise reduction method for fan pipeline system - Google Patents
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- G10K11/17817—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the acoustic paths, e.g. estimating, calibrating or testing of transfer functions or cross-terms between the output signals and the error signals, i.e. secondary path
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Abstract
The invention discloses an active noise reduction method for a fan pipeline system, which adopts a white noise signal as a loudspeaker input signal in an off-line mode, adopts a microphone to receive the white noise transmitted by a secondary channel, and estimates parameters of the secondary channel by an ANC controller by utilizing the two signals; the method comprises the following steps that when a fan is started to work, noise is generated, a non-acoustic sensor is used for obtaining rotating speed information of the fan, and a signal generator module in an ANC controller generates a sine signal according to the rotating speed information of the fan and sends the sine signal to a loudspeaker for playing; the signal received by the error microphone, namely the error signal after the fan noise is superposed with the sinusoidal signal played by the loudspeaker, is transmitted to the ANC controller, so that the error signal is minimized. The narrow-band ANC algorithm can effectively inhibit the noise of the fan pipeline, obviously improves the subjective auditory perception of a user, and has wide application prospect and commercial value.
Description
Technical Field
The invention relates to an active noise reduction method for a fan duct system.
Background
With the continuous development of social economy, the variety and intensity of environmental noise are continuously increased, and the influence on the life and work of people is more serious. The traditional Passive Noise reduction technology (PNC) adopts sound absorption, sound insulation, a silencer and other modes to realize the suppression of target Noise, has certain effect on medium and high frequency Noise above 1000Hz, but has poor Noise reduction effect on low frequency Noise below 1000Hz, especially below 500 Hz. In order to reduce the Noise in the low frequency band, Active Noise Control (ANC) is increasingly emphasized. The active noise reduction technology is characterized in that according to the superposition principle of sound waves, a cancellation signal which has the same frequency, opposite phase and equivalent amplitude with the original noise is sent out through a secondary loudspeaker, and the original noise is greatly reduced or even eliminated.
Fan noise is a very typical noise in ambient noise. The noise reduction mode aiming at the fan noise can adopt a passive noise reduction method, the fan noise is reduced by covering sound absorption materials on the outer wall of the pipeline, optimizing the shape of fan blades and the like, and the passive noise reduction method has poor medium and low frequency noise reduction effect. The active noise reduction technology can effectively suppress the low-medium frequency noise below 1000Hz in the fan noise on the basis of not changing the structure of the fan system, and the suppression effect is very obvious. The aerodynamic noise of the fan blades is dominant, and the rotational noise caused by the rotation of the fan blades in the aerodynamic noise has a discrete frequency characteristic and is formed by a fundamental frequency and a high-order harmonic of the fundamental frequency, wherein the fundamental frequency is determined by the rotation speed of the fan and the number of the fan blades. When the fan noise passes through the regular pipeline, the discrete fundamental frequency noise is controlled, and the noise of the fan can be well reduced.
Disclosure of Invention
The invention adopts the classic FxLMS algorithm to control the discrete frequency of the fan, automatically adjusts the value adjusting method of the LMS filter according to the characteristics of the discrete frequency, optimizes the point number of the LMS filter and can achieve the purpose of efficiently eliminating the discrete frequency noise.
The invention adopts an improved self-adaptive notch FxLMS algorithm based on the FxLMS algorithm, is applied to the active noise reduction of the pipeline discrete frequency noise, and is also called as narrow-band ANC aiming at the active noise reduction of the pipeline discrete frequency.
The frequency information of the discrete frequency noise related to the fan rotating speed can be obtained by a non-acoustic sensor such as a rotating speed sensor, and can also be obtained by recording the noise information in advance and then carrying out spectrum analysis. After the frequency information is obtained, a relatively accurate narrow-band reference signal can be constructed for active control of narrow-band noise. And actively eliminating discrete frequency noise generated by the fan by adopting an adaptive notch FxLMS algorithm.
The fan-duct active noise reduction algorithm is mainly applied to application scenarios with fans and ducts, such as an air conditioning fan, an air purifier fan, a range hood and the like, in which, when the rotation speed of the fan is stable, relatively stable discrete frequency noise is generated, and although no special processing is performed on noise similar to white noise accompanied by the discrete frequency noise, when the discrete frequency noise is removed, the remaining white noise has a much better auditory effect. The algorithm of the invention can effectively reduce the noise of the fan pipeline, and can be combined with a white noise (broadband noise) reduction algorithm in the future; at present, commercial active noise reduction schemes for pipeline noise caused by fans are still few, and the algorithm has a wide application prospect.
The invention has the following beneficial effects:
the narrow-band ANC algorithm can effectively inhibit the noise of the fan pipeline, and the subjective auditory perception of a user is obviously improved. In various electrical products, the fan is used very generally, and if the noise of the fan can be effectively reduced, the comfort of the use of the electrical appliance can be greatly improved, so that the noise reduction scheme aiming at the noise of the fan pipeline has wide application prospect and commercial value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic block diagram of narrowband ANC;
FIG. 2 is a sub-path offline recognition;
FIG. 3 is an estimate of the secondary path
FIG. 4 is a block diagram of the adaptive notch FxLMS algorithm;
fig. 5 is an ANC operation schematic.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Examples
For discrete frequency noise (narrow-band noise) generated by the rotation motion of the fan, some auxiliary information can be obtained through a non-acoustic sensor such as a rotation speed sensor, so that a relatively accurate narrow-band reference signal can be constructed. Under the condition that the conditions such as the rotating speed of the fan and the like are not changed, when the conditions allow, the discrete noise can be recorded in advance, the frequency spectrum analysis is carried out, more accurate frequency information can be obtained, and more accurate narrow-band reference signals are constructed. This narrowband reference signal will be used for active control of the narrowband noise. A schematic block diagram of a narrowband ANC system based on the FxLMS algorithm is shown in fig. 1.
Off-line identification of secondary path
The secondary path generally refers to the physical path from the secondary sound source to the error microphone. The existence of the secondary path may cause a delay in a time domain between a control signal sent by the control system and a target noise signal during active noise reduction, thereby causing instability of a control process and even causing noise reduction failure. In order to eliminate the influence of the time delay of the secondary path and improve the noise reduction and convergence performance of the control system, the transfer function of the secondary path is identified in advance, which is called as secondary path identification. The secondary path identification method includes an off-line identification method and an on-line identification method.
In the application, the off-line identification of the secondary path is carried out by adding a random white noise method. The identified secondary path coefficient is set to be represented by the impulse response coefficient of the FIR digital filter, and the adaptive updating of the filter coefficient can be realized through an adaptive algorithm such as LMS (least mean square) and the like according to the set cost function, namely the identified secondary path coefficient is continuously iterated, so that the real secondary path transmission function is gradually approximated. A block diagram of the LMS algorithm-based off-line identification of the secondary path is shown in fig. 2.
The specific identification process is as follows: keeping the primary sound source not sounding, driving the secondary loudspeaker to emit random white noise, taking the random white noise as a reference signal and feeding the random white noise back to the controller, simultaneously acquiring the white noise signal by the error microphone after the white noise signal is transmitted through the real secondary channel, feeding the acquired signal back to the controller to be used as the other input of the LMS algorithm, continuously updating and iterating the weight coefficient of the LMS algorithm until the convergence of the identified error signal is small enough, and then completing the identification of the secondary channel.
The white noise emitted by the secondary speaker is generated by the system itself. An example of a 64-order path transfer function is shown in fig. 3.
Adaptive notch FxLMS algorithm
The adaptive notch FxLMS algorithm is improved on the basis of the classic FxLMS algorithm and is suitable for an adaptive control algorithm for controlling periodic target noise (discrete frequency noise). The algorithm is simple in structure, obvious in narrow-band noise suppression effect and often used for active control of engine order noise. A block diagram of an active noise reduction algorithm for single frequency component noise is shown in FIG. 4, in which a signal generator synthesizes a sinusoidal signal x according to the frequency f of the target noise signal to be removed1(n) and a cosine signal x2(n) mixing x1(n) and x2(n) are respectively multiplied by w1(n) and w2(n) two weight coefficients constituting the output signal y (n), y (n) the signal y after transmission through the real secondary path S (z)s(n) obtaining an error signal e (n) after the cancellation with the original noise d (n), sending the error signal e (n) to an ANC controller, and continuously adjusting the weight coefficient w by the ANC controller according to the minimum mean square error criterion1(n) and w2(n) minimizing the value of e (n), i.e. converging.
y(n)＝w1(n)x1(n)+w2(n)x2(n)
According to the minimum criterion of mean square error, the more-line formula of the sine component weight coefficient and the cosine component weight coefficient can be obtained as follows:
xf1(n) and xf2(n) are respectively estimated sub-paths
The filtered sine component reference signal and the cosine component reference signal.
The traditional FxLMS algorithm based on the adaptive notch method is simple to implement, has low operation complexity, and has a good effect in software simulation, but because the actual application environment is complex, many situations cannot be completely simulated through software simulation, and the situation of poor noise reduction effect often occurs by adopting the adaptive algorithm. Mainly because of the weight coefficient w1(n) and w2And (n) is only equivalent to a 1-order FIR filter, the self-adaptive capacity is limited, and in a complex actual environment, because the self-adaptive capacity is limited, the algorithm is often not converged, so that the system cannot reduce noise, and howling may occur.
For this case, the weight coefficient w of the adaptive notch FxLMS algorithm1(n) and w2(n) increasing from one order to a plurality of orders
And
the order is N, the size of the order N is determined according to the actual situation of the system and can be 4-128, and the highest order can be 128 under the permission of the DSP computing capacity. Multiplying the weight coefficient by the corresponding weight coefficient after increasing the range of variation of the order of the weight coefficient
And
are also increased to
And
the same number of points, in the formula for calculating y (n),
"+" indicates convolution.
Mu value adjusting mode of adaptive notch FxLMS algorithm value
Since the order of the weight coefficient is adjusted from 1 order to N order, the adjustment mode of the parameter μ for controlling the weight coefficient adjustment of the adaptive filter has a great influence on whether the system converges. So a corresponding adjustment is also made to modify mu from a fixed value to a variable value.
μminIs the minimum μ value, μ, that can cause the system to converge when the μ value is fixedmaxIs the maximum μ value that can converge the system when the μ value is fixed. The mu is updated in such a way, so that the convergence speed of the algorithm can be ensured to be high at the beginning, and the algorithm can be converged to the minimum value. In order to increase the flexibility of algorithm implementation and reduce the operation complexity of the algorithm, the operation is processed in a frame division manner, the length (number of points) of each frame is FL, the frame length can be 8 to 64, and the order N is usually greater than the frame length FL. Typical values are N-64 and FL-16.
By using the algorithm, the noise reduction effect can reach 20-25 dB aiming at the discrete frequency noise of 200-800 Hz.
The fan duct noise generally comprises narrow-band noise related to the rotating speed of the fan and wide-band noise related to the duct (which can be approximately regarded as white noise), and the narrow-band noise in the fan duct noise is effectively suppressed by adopting a narrow-band algorithm. The technical scheme adopted by the invention is as follows. Fig. 5 is an ANC operation schematic diagram.
The algorithm realizes steps S01-S03:
in step S01, in an off-line manner, a white noise signal is used as a speaker input signal, a microphone is used to receive the white noise transmitted through the secondary channel, the ANC controller estimates parameters of the secondary channel by using the two signals, the principle of the secondary channel refers to fig. 2, and the identification result of the secondary channel is shown in fig. 3;
in step S02, the fan is turned on to generate noise, the non-acoustic sensor is used to obtain the information of the fan speed, and the signal generator module in the ANC controller generates a sinusoidal signal according to the information of the fan speed and sends the sinusoidal signal to the speaker for playing;
in step S03, the signal received by the error microphone (microphone) is the error signal after the fan noise is superimposed (cancelled) with the sinusoidal signal played by the speaker, and this error signal is transmitted to the ANC controller for adjusting the phase and amplitude of the sinusoidal signal played by the speaker, so as to minimize the error signal.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An active noise reduction method for a fan duct system, comprising the steps of:
s1, in an off-line mode, adopting a white noise signal as a loudspeaker input signal, adopting a microphone to receive the white noise transmitted by a secondary channel, and estimating parameters of the secondary channel by the ANC controller by utilizing the two signals;
s2, noise is generated when the fan is started, the rotation speed information of the fan is obtained by using the non-acoustic sensor, and a signal generator module in the ANC controller generates a sinusoidal signal according to the rotation speed information of the fan and sends the sinusoidal signal to a loudspeaker for playing;
s3, the signal received by the error microphone is the error signal after the fan noise is superimposed on the sinusoidal signal played by the speaker, and this error signal is transmitted to the ANC controller for adjusting the phase and amplitude of the sinusoidal signal played by the speaker, so as to minimize the error signal.
2. The active noise reduction method for fan duct systems of claim 1, wherein the identified secondary path coefficients set in S1 are represented by impulse response coefficients of an FIR digital filter, and adaptive updating of the filter coefficients is achieved by an adaptive algorithm according to the set cost function, i.e. continuous iteration of the identified secondary path coefficients, and then a step-by-step approximation of the true secondary path transfer function is performed.
3. The active noise reduction method for the fan duct system as claimed in claim 2, wherein the primary sound source is kept silent in S1, the secondary speaker is driven to emit random white noise, and the random white noise is used as a reference signal and fed back to the controller, and the white noise signal is collected by the error microphone after passing through the real secondary path, and the collected signal is fed back to the controller as another input of the LMS algorithm, and the weight coefficient of the LMS algorithm is continuously updated and iterated until the identified error signal converges to be small enough, at which time the identification of the secondary path is completed.
4. The active noise reduction method for fan duct system of claim 2, wherein the adaptive algorithm in S1 is a weight coefficient w of an adaptive notch FxLMS algorithm1(n) and w2(n) increasing from one order to a plurality of orders
And
the order is N, the size of the order N is determined according to the actual condition of the system and can be 4 to 128, and the highest order can be 128 under the permission of the DSP computing capacity; multiplying the weight coefficient by the corresponding weight coefficient after increasing the range of variation of the order of the weight coefficient
And
are also increased to
And
the same number of points, in the formula for calculating y (n),
"+" indicates convolution.
5. The active noise reduction method for a fan duct system of claim 4,
the mu value of the adaptive notch FxLMS algorithm value is adjusted in a way that the mu is modified from a fixed value to a variable value.
6. The active noise reduction method for a fan duct system of claim 5,
in order to increase the flexibility of algorithm implementation and reduce the operation complexity of the algorithm, the operation is processed in a frame division manner, the length (number of points) of each frame is FL, the frame length can be 8 to 64, and the order N is usually greater than the frame length FL.
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| CN115188358A (en) * | 2022-07-21 | 2022-10-14 | 广东浦尔顿科技有限公司 | Noise processing method of direct current charging module |
| CN116241493A (en) * | 2021-12-08 | 2023-06-09 | 宏碁股份有限公司 | Electronic system with heat dissipation and wind pressure compensation feedforward type active noise control function |
| WO2023103168A1 (en) * | 2021-12-06 | 2023-06-15 | 南京航空航天大学 | Active helicopter noise suppression apparatus integrating acoustic array and in-blade control |
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