Fusion And Diagnosis Technology Research Of Non-contact Multi-sensor Acoustic Signals For Rolling Bearing Fault

Rolling bearing is a basic element in various of industrial fields who plays aimportant role in all kinds of mechanical equipments. There are many Rolling bearingfault diagnosis methods, for example infrared axis temperature detection method,vibration signal analysis method, lubrication oil analysis method and so on. Thesemethods have their own characteristics which can be used to judge the types of rollingbearing common faults effectively, but they all can't diagnose the rolling bearing earlyfaults effectively because of their own characteristics and the limitation of applicationenvironment. In addition, the rolling bearing in service is often being in moving statewhose monitoring of running state is the technical problem in various of industrialfields. Aiming at improving rolling bearing faults diagnosis technology and laying afoundation for rolling bearing condition monitoring, this paper studies the rollingbearing fault diagnosis methods based on the periodic non-contact acoustic emissionsignals to guarantee that the operation of mechanical equipments are safe and stable.The non-contact multi-sensor acoustic emission testing test-bed for rollingbearing fault was established. The fault acoustic emission signals of the rollingbearings with rolling element fault, inner ring fault, outer ring fault respectively werecollected in different rotational speeds and different moving speeds. The morphologyfilter technology was studied. According to the characteristics of all kinds of rollingbearing fault acoustic emission signals, the suitable structure element was found. Andthen with this structure element, the test data in each group was treated bymorphology filter technology. After that the interference information likes lowfrequency noise, high frequency noise and so on was rejected, this process eliminatedthe obstacle for the following data analysis.Aiming at the problems of overlap and incomplete information when the signalswere collected with multi-sensor, according to the time difference of acousticemission impact signals and the geometric relation of sensors array, the discriminationformula and the fusion algorithm of the same sound source signals were established.With this method the experimental signals of each group were processed asidentification and fusion. The results show that the similar degree of the fusion signalsis better than each same sound source signal compared with the fault source signals.The rolling bearing faults diagnosis method based on periodicity acoustic emission impact count and the rolling bearing faults diagnosis method based on thecharacteristic parameters of the periodicity acoustic emission signals are put forward.The former diagnoses rolling bearing faults using the corresponding relation betweenthe quantity of rolling bearing fault fusion signals and the acoustic emissioncumulative impact count. The latter diagnoses rolling bearing faults by calculating thewaveform characteristic parameters of rolling bearing fault periodicity signals. Thediagnosis results show that the former has a higher accuracy in a few period test andsome errors in multi period test, but these errors are in a range which can be endured;because the processing targets are the periodicity acoustic signals, the interference ofindividual non fault source signal is eliminated so that the latter has a higher accuracy.Above two methods all can diagnose and distinguish the types of the rolling bearingearly faults.