Research On Mechanical Fault Acoustic Emission Signal Analysis Based On Local Mean Decomposition

With the rapid development of modern industry, several of large rotating machinery inindustrial production plays a decisive role. Because of all kinds of fault defects, equipmentmaintenance and improper operation error will affect the stable operation of machinery andequipment, pose a threat to the safety of production, and even resulting in seriouseconomic losses, so the monitoring and diagnosis of mechanical equipment fault has beengradually paid attention to.Acoustic emission in mechanical fault diagnosis field is a kind of health monitoringand diagnosis technology, it is a kind of dynamic detection technology, the energy sourcefor the detected object itself. Acoustic emission technology of object to be measured closedegree is not high, and is not sensitive to the measured object shape, so it can detect andevaluate the whole structure. Therefore, acoustic emission technique for early mechanicalfaults real-time monitoring and diagnosis is very important.In this paper, it uses the adaptive local mean decomposition of time-frequencyanalysis (LMD) method of gear fault signal feature extraction of acoustic emission. Localmean decomposition method can take signal component decomposed into the productfunction PF components. Compared with normal gear and the local mean fault gear of AEsignal, it can analyze the time-frequency characteristics of the fault, and through thecorrelation analysis, the representative fault feature component. According to the marginalspectrum reflects the failure frequency and the gear fault frequency values are compared toverify the theory, it can be effective to monitoring and diagnosis of mechanical fault localmean decomposition method based on acoustic emission. Therefore, local meandecomposition method to extract gear wear plays an important role in the signalcharacteristics and the gear fault monitoring and diagnosis of acoustic emission.