| With the continuous progress of science and technology,electromechanical equipment has become increasingly complex,which developing productive forces and meeting people’s growing material demand,also creating some urgent problems: complex electromechanical equipment has a high failure rate.The traditional electromechanical equipment diagnosis system based on internal sensing information has the disadvantage of local monitoring,high cost,low intelligence and poor portability.The sound of electromechanical equipment can characterize the health status of the equipment and play an important role in the fault diagnosis of the equipment.The use of sound source localization technology can locate the specific location of the fault,which greatly accelerate the positioning of the fault and the initial analysis of the fault.At present,the methods of fault diagnosis by using inspection robots are mostly based on machine vision,and their ability to independently analyze the capability faults is relatively weak.Based on these considerations above,this paper emphasizes the study of the key technology of integrating the sound source localization technology into inspection robots to diagnose the electromechanical equipment failure.In this paper,firstly,according to different dimensions of sound features have different contribution to identify the sound signal,combine Correlation Coefficient and Mel Frequency Cepstrum Coefficients(MFCC)and propose an improved Mel Frequency Cepstrum Coefficients(CCMFCC).The experimental results indicate that this method can better reflect the sound characteristics of electromechanical equipment and raise the working condition recognition rate.Secondly,this paper analyzed the influence of signal-to-noise ratio on the accuracy of time-delay estimation and studied the influence of time delay estimation error on the location result.The generalized cross-correlation time delay estimation is improved.A generalized cross-correlation time delay estimation based on wavelet packet decomposition and correlation coefficient is proposed and utilize Fourier-interpolation to solve the problem that time delay estimation needs high signal-to-noise ratio(SNR)and sampling rate,and raise the accuracy and noise immunity of time delay estimation.And then,we deduce the location formula of 4-element T-shaped array sound source localization algorithm,and analyze the range and error of localization.In view of the insufficiency,we propose a 7-element 12 groups of T-microphone array model.The horizontal positioning range of this model can reach 0 ~ 360 degrees,which can realize the all-directional positioning of the sound source.The localization accuracy is not affected by the sound source orientation.Compared with the T-shaped array,the range and accuracy of localization are obviously improved.Finally,we integrate the sound source localization into the mobile robot,set up the hardware and software platform.Performance tests carried out in a typical electromechanical equipment(bench drill)when different modes.The experimental results indicate that utilize 7-element 12 groups of T-microphone array model,the CCMFCC acoustic feature extraction method and the generalized cross-correlation algorithmcan which based on wavelet packet decomposition and correlation coefficient can realize the fault diagnosis of electromechanical equipment well. |
