Study on Mechanical Fault Early Warning Based on IGMM and Interval Statistics
Because complex machinery has bad working conditions and complex structure,the early fault warning method of the SF method often causes false and missing alarm events.A mechanical fault early warning method based on IGMM and interval statistics is proposed.Firstly,this method maps mechanical vibration signals to high-dimensional feature space and divides the interval.Then,the probability statistical model is established by using IGMM to estimate the frequency distribution of high-dimensional feature space in each interval under the condition of mechanical health;the frequency distribution of high-dimensional feature space in each interval under the real-time mechanical state is calculated.Finally,fault early warning is re-alized by calculating the distance between the two frequency distributions and comparing it with the warning threshold obtained by self-learning.The application results show that the proposed method has high accuracy and timeliness.
Fault Early WarningInfinite Gaussian Mixture ModelMechanical Equipment
NETL
NSTL
万方数据
维普
