Acoustic Signal Fault Warning Method for Belt Conveyor Idler Bearings Based on St-DPMM
A fault warning method for belt conveyor idler bearings based on Student's t-distribution Dirichlet process mixture model(St-DPMM)is proposed to address the problem of complex and variable external impacts during operation of the bearings.The acoustic signal is superimposed by multiple excitation source signals,which is seriously disturbed by noise.The traditional single feature warning methods are difficult to effectively realize the fault warning.Firstly,the acoustic signal of the bearings is collected,and the time-frequency domain features are extracted to construct a high-dimensional feature space.Secondly,the St-DPMM is trained to fit the statistical distribution of acoustic signal of the bearings,and the distance between benchmark mixture model and normal state model is calculated by using KL divergence approximation method.Finally,based on 3σ criterion,the self-learning warning threshold is used to calculate the difference between benchmark model and real-time model,and the fault warning is realized by comparing with warning threshold.The test results show that the accuracy,stability and timeliness of the proposed method have significant advantages over comparative methods,and can effectively realize the fault warning of the bearings.
rolling bearingbelt conveyorearly warningDirichlet distributionsignal processing
NETL
NSTL
万方数据
