Reference-modulated noise injection for online secondary path modeling in active noise control under strong background noise

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

All-pass filtered x least mean square algorithm for narrowband active noise control

Online secondary path modeling algorithm without auxiliary noise for narrowband active noise control

The similarity between the identification of secondary path in feedforward active noise control and the channel identification in stereo echo cancellation is revealed in this paper. Accordingly, the convergence behavior of the ANC system can be analyzed by investigating the joint auto-correlation matrix of the reference and the filtered reference signal. It is proved that the straightforward secondary path modeling can be carried out without the injection of any additive noise as long as the control filter is of a sufficiently long length. Furthermore, by taking advantage of the time-varying characteristic of the control filter, effective modeling of the secondary path can even be achieved without any restriction on the control filter length.

Underground magnetic resonance sounding (MRS) low signal-noise ratio (SNR) weak signal is hard to manage with traditional de-noising method such as fitting and filter.In view of this, this article makes use of both transient electromagnetic method (TEM) data and MRS data obtained from integrated detecting equipment.The goal is to extract weak MRS signal based on TEM under strong background noise.At first, underground electrical distribution can be acquired from TEM data, which is processed by equivalent conductive plane method.Then, the initial model of moisture content distribution was constructed on geological information and 3D MRS forwarding was made with finite element method.At last, the correlation coefficient series and deviations between forwarding data and measured data were calculated.The initial model would be corrected until the result is satisfying.Model calculation shows that this method can effectively extract the weak nuclear magnetic signals in tunnel under strong background noise.

An efficient online secondary path estimation for feedback active noise control systems

This paper proposes a new modular design for implementation of active noise control (ANC) systems with online secondary path modeling. This design comprises of four similar modules to reduce the unwanted primary noise. One of these modules performs as the main adaptive control filter that uses an FxLMS algorithm to converge the tap-weights. Two other modules work as the adaptive modeling filters that use an LMS algorithm to model the secondary path filter. The last module works as a digital filter to produce the antinoise output signal. This innovative modular implementation of the ANC system results in a fast design and fast convergence with capability to expand the four adaptive filters and data bus when better noise reduction for ANC system is desired.

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

<abstract> <p>Rolling bear is a major critical component of rotating machinery, as its working condition affects the performance of the equipment. As a result, the condition monitoring and fault diagnosis of bearings get more and more attention. However, the strong background noise makes it difficult to extract the bearing fault features exactly. Furthermore, regular gradient disappearance and overfit appear in traditional network model training. Therefore, taking the printing press bearings as the research object, an intelligent fault diagnosis method based on strong background noise is proposed. This method integrates frequency slice wavelet transform (FSWT), deformable convolution and residual neural network together, and realizes the high-precision fault diagnosis of the printing press bearings. First, FSWT is used to preprocess the original vibration signal to obtain bearing fault features in the time and frequency domain, reconstruct the signal in any frequency band and describe local features accurately. Second, the ResNet is selected as the base network, and the two-dimensional time-frequency diagrams (TFD) obtained by preprocessing are used as input. For the model that has a poor ability to extract subtle features under strong background noise, the deformable convolution layer is introduced to reconstruct the convolution layer of ResNet, called deformable convolution residual neural network (DC-ResNet). Finally, the effectiveness of this method is verified by using the data sets collected under experimental conditions and actual working conditions for fault diagnosis of the printing press. The results show that the DC-ResNet can classify different bearing faults under strong background noise, and the accuracy and stability are greatly improved, which the accuracy meets 93.90%. The intelligent fault diagnosis with high-precision of printing press bearings under complex working conditions is realized by the proposed method.</p> </abstract>

In many applications of active noise control (ANC), an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to adjust the secondary path estimation. Comparative simulation results shown in this paper indicate effectiveness of the proposed method.KeywordsActive noise controlfeedforwardFxLMSonline secondary path modelling

Active control for vehicle interior noise based on DWT-FxLMS algorithm using a piezoelectric feedback system

Weak signal detection has garnered considerable attention in numerous research fields, especially weak signal detection under strong noise, which is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficients is proposed for detecting weak exponential damped sinusoidal signals. This method has a wide variety of signal processing applications. The innovation of our method lies in selecting the most informative singular values of K rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signals via the autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. Numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, and indicate that the presented method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared to stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also offer significant advantages. Thus, it is beneficial for theoretical analysis and engineering applications.

Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise

In acoustic environments with strong background noise and interferences, the generalized sidelobe canceller (GSC) suffers from signal distortion and reduced amount of noise suppression since the blocking matrix cannot produce pure noise reference due to unavoidable direction of arrival (DOA) mismatch. In this paper, a generalized sidelobe canceller robust to DOA mismatch is proposed. It employs eigenanalysis to reconstruct the interference-plus-noise covariance matrix with the possible DOA region of signal of interest (SOI). The reconstructed matrix is provided for the MaxSINR beamforming and subsequent blocking matrix construction. The designed blocking matrix can minimize the signal leakage meanwhile maximize the noise and interference power for further noise and interference suppression. Numerical simulation results show that compared with the alternatives, the proposed GSC has better interference suppression capability and lower SOI distortion in situations without precise DOA of SOI.

An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.