Research On Vibration Detection And Feature Extraction Of Rapier Loom

Vibration detection for rapier looms is of great significance for fault diagnosis and prediction of rapier looms.Because the rapier loom is in a strong noise environment,and the fault signal is relatively weak when a fault occurs in the early stage,the existing vibration detection system has the problems of low sampling accuracy and poor antiinterference ability.At the same time,the characteristic expression of the collected vibration signal is not obvious,and the existing rapier loom vibration characteristic extraction method is difficult to meet the requirements of vibration signal characteristic extraction under the background of strong noise.Aiming at the problem that the weak fault signal of rapier loom is difficult to detect and characterize,this paper studies the vibration detection system and feature extraction method of rapier loom.The main research work is as follows:1.The vibration model of the rapier loom is established and the vibration detection and feature extraction scheme is proposed.The vibration models of the rotating shaft and the rapier are established,the fault characteristic frequencies of the main components of the rolling bearing are discussed,and the main vibration frequencies of the rapier loom are summarized.According to the obtained vibration frequency range,aiming at the deficiencies of the prior art,a research scheme of the detection system and the feature extraction method is proposed from the two aspects of the rapier loom vibration detection system and the feature extraction method.2.A feature extraction method that combines the improve complete ensemble empirical mode decomposition with adaptive noise and the adaptive piecewise hybrid stochastic resonance is proposed.In order to improve the system’s denoising ability and weak fault signal feature extraction ability,empirical mode decomposition theory and stochastic resonance theory are analyzed in this paper.Aiming at the shortcomings of spurious components and mode aliasing,the empirical mode decomposition is improved.Aiming at the saturation phenomenon of stochastic resonance and the difficulty of parameter selection,the self-adaptive,variable-scale segmental hybrid stochastic resonance is studied.Based on this,a method for extracting vibration features of rapier looms is proposed.3.The vibration detection system of the rapier loom is built and the experimental research is carried out.Combined with the on-site industrial environment of the rapier loom,the vibration signal range and precision of the rapier loom are analyzed,and the hardware platform is constructed from three aspects: sensor,conditioning module and acquisition module.Among them,the IEPE acceleration sensor is used as the core,and the second-order high-pass filter circuit of the voltage-controlled voltage source,the AD620 amplifier circuit and the LTC1596 anti-aliasing filter circuit are used to complete the conditioning module.The analog voltage acquisition circuit with AD7190 as the core is studied.Combined with the control system of the rapier loom,the sampling accuracy of the vibration detection system of the rapier loom is tested and verified to ensure that the sampling error of the system is within 0.5%.In view of the above research work,the analysis is carried out from the point of view of simulation and experiment.In terms of simulation,the feature extraction algorithm is verified by establishing the vibration and impact model of the rapier loom.By comparing the amplitudes of the characteristic frequencies,it is proved that the proposed method has the ability of filtering and feature extraction of weak fault signals;in the experiment,the vibration signal of the rapier loom is collected and analyzed by different methods.By comparing the eigenfrequency amplitude of the output signal,the difference between the eigenfrequency amplitude and the surrounding maximum noise,and the signal-to-noise ratio of the output signal under different algorithms,as well as the success rate and accuracy of the feature extraction results,it is proved that the proposed method is effective against complex field disturbances.The feature extraction of rod loom has better effect.