Bearing Fault Diagnosis Based on Depth Autocoding and Frequency Domain Correlation Kurtosis
In the same state,when various faults first appear,the characteristics of the signals collected in the loud noise environment are very similar,leading to the decline of diagnostic accuracy.Based on depth coding and frequency domain correlation kurtosis(FCKT),the intelligent classification of bearing operating conditions is proposed.Under the premise of enhanced features,the data length is significantly reduced,and the recognition efficiency and accuracy of the algorithm are enhanced.The results show that,based on the gradual expansion of the value range of the maximum offset points,the discrimination accuracy presents a trend of continuous improvement.When the value is 340,the discrimination can be 100％,and has a strong stability.Compared with time domain index,frequency domain index has higher discrimination accuracy.When FCKT index is taken as the sample,it has the strongest stability and the highest accuracy.After the end of FCKT calculation,the deep self-coding intelligent differentiation is implemented,and the running time is reduced by 46.32％,which can reduce the running time to a large extent.
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