Two kinds of anti-audio interference active noise control (ANC) algorithms are proposed based on a conventional filtered-X least mean squares virtual microphone (FXLMS-VM) algorithm. The proposed filtered-X least mean squares virtual microphone and anti-audio interference (FXLMS-VM-AI) algorithm employs an extra audio path to compensate for the influence of the audio interference phenomenon on the ANC adaptive filter. The proposed time frequency mixed filtered-X least mean squares virtual microphone and antiaudio interference (TFM-FXLMS-VM-AI) algorithm can overcomes this audio interference effect by calculating the coefficients of the ANC adaptive filter in the frequency domain. The time domain acoustic wave field simulation method is adopted to test the performance of the proposed ANC algorithms in an audio interference environment. By analyzing the simulation results, both proposed algorithms show better convergence accuracy in terms of the adaptive filter coefficients and better audio signal performance than the conventional algorithm. The proposed FXLMS-VM-AI algorithm has a faster convergence speed for the ANC adaptive filter than the proposed TFM-FXLMS-VM-AI algorithm. The proposed TFM-FXLMS-VM-AI algorithm has a lower computational complexity than the conventional algorithm and the proposed TFM-FXLMS-VM-AI algorithm.
In actual , there exist inevitably a lot of interference from neighbor machine and noise from surrondings in mechanical vibration signal measured by sensor ,which is disadvantageous for condition monitoring and fault diagnosis. In order to eliminate the axial vibration signal in the noise, using Wavelet packet denoising method in this article, Emulating experiment s were carried out under the MATLAB software ,original signals adopted vibration impulsion signal produced by vice position of faulty bear. Separation result s confirm this method successfully ext ract original source ,efficiently removes noise.
This paper presents a hybrid damage detection method based on continuous wavelet transform (CWT) and modal parameter identification techniques for beam-like structures. First, two kinds of mode shape estimation methods, herein referred to as the quadrature peaks picking (QPP) and rational fraction polynomial (RFP) methods, are used to identify the first four mode shapes of an intact beam-like structure based on the hammer/accelerometer modal experiment. The results are compared and validated using a numerical simulation with ABAQUS software. In order to determine the damage detection effectiveness between the QPP-based method and the RFP-based method when applying the CWT technique, the first two mode shapes calculated by the QPP and RFP methods are analyzed using CWT. The experiment, performed on different damage scenarios involving beam-like structures, shows that, due to the outstanding advantage of the denoising characteristic of the RFP-based (RFP-CWT) technique, the RFP-CWT method gives a clearer indication of the damage location than the conventionally used QPP-based (QPP-CWT) method. Finally, an overall evaluation of the damage detection is outlined, as the identification results suggest that the newly proposed RFP-CWT method is accurate and reliable in terms of detection of damage locations on beam-like structures.
As one of the research hotspots in the field of pumps, cavitation detection plays an important role in equipment maintenance and cost-saving. Based on this, this paper analyzes detection methods of cavitation faults based on different signals, including vibration signals, acoustic emission signals, noise signals, and pressure pulsation signals. First, the principle of each detection method is introduced. Then, the research status of the four detection methods is summarized from the aspects of cavitation-induced signal characteristics, signal processing methods, feature extraction, intelligent algorithm identification of cavitation state, detection efficiency, and measurement point distribution position. Among these methods, we focus on the most widely used one, the vibration method. The advantages and disadvantages of various detection methods are analyzed and proposed: acoustic methods including noise and acoustic emission can detect early cavitation very well; the vibration method is usually chosen first due to its universality; the anti-interference ability of the pressure pulsation method is relatively strong. Finally, the development trend of detecting cavitation faults based on signals is given: continue to optimize the existing detection methods; intelligent algorithms such as reinforcement learning and deep reinforcement learning will be gradually integrated into the field of cavitation status identification in the future; detection systems still need to be further improved to accommodate different types of pumps; advanced sensing devices combined with advanced signal processing techniques are one of the effective means to detect cavitation in a timely manner; draw on other fault detection methods such as bearing faults and motor faults.
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