Research On Damage Monitoring Of7N01Aluminum Alloy Using Acoustic Emission In Strong Noise Environment

With the development of high-speed railways, aluminum is widely used in trainstructures, such as the traction beams, corbels and other components. When the trainruns at high speed, fatigue damage will be caused by alternating load in thealuminum structure, which endanger the safe operation of the train. Currently,non-destructive methods need to stop the train and test the body of the train off-line,so they are unable to monitor the body parts during operating condition. Comparedwith conventional non-destructive testing methods, acoustic emission technology issensitive to active defects, and can detect the overall structures fast and efficiently,but the application in trains is still in its infancy. Due to the lack of awareness of AEcharacteristics of aluminum alloy and noise signals in running trains, it is urgent toresearch acoustic emission signals from damage of aluminum and extract them fromnoisy environment when acoustic emission technique used in trains.For the problems of acoustic emission monitoring used in trains, a new in-situmicroscopic observation method was proposed in this paper, which can monitor thematerial damage process and capture damage images continuously, and it willprovide technical assistance to the analysis of acoustic emission signals.Acoustic emission and the in-situ microscopic observation system were used tomonitor aluminum base metal and weld during static damage process. The time,frequency and time-frequency domain characteristics of acoustic emission signalswere analyzed to establish the relationship between the characteristics and staticdamage behavior. The acoustic emission sources were identified by the fractureanalysis. The results show that acoustic emission energy, peak and centroidfrequency were effective indicators to monitor the crack initiation of aluminum.Fatigue cracks are easy to cause by alternating loads in aluminum structureduring the service process of trains, and fatigue crack growth rate is the base data toassess damage degree of aluminum structures. Acoustic emission features of crackinitiation and propagation in fatigue damage process of the aluminum base metaland weld were studied under laboratory conditions, and fatigue processes weremonitored by acoustic emission and in-situ microscopic observation system. Thesources of acoustic emission were analyzed by fatigue fracture. The results showthat fatigue damage of aluminum has three different acoustic emission stages, whichcorresponds to the three stages of fatigue crack growth respectively. In the fatiguecrack initiation stage, number of fatigue cycles corresponding to the change ofacoustic emission features was earlier than the number when micro-crack was firstdiscovered by the in-situ microscopic observation system. Therefore the acoustic emission features can be used as indicators of fatigue crack initiation. Relationshipbetween the acoustic emission count rate and fatigue crack growth rate wasestablished. The fatigue crack growth rate can be calculated through the monitoredacoustic emission features and the established relationships. Thus the health statusof aluminum structures was assessed, and the problem of difficult measurement forthe stress intensity factor in actual structure was avoided.In order to analyse the noise features in the moving train, a small in-siteloading system was developed to load aluminum alloy in a running train, whichsimulated acoustic emission signals in the service process of the train. Dataacquisition system was used to get real-time acoustic emission signals under noisyenvironments, which were the basic data provided for subsequent signal analysisand processing. Types of noise signals were analyzed through comparative analysisof acoustic emission signals obtained on the static and operational train, whenaluminum is not loaded and loaded. For the collected mix signal that contain noiseand acoustic emission signals from crack propagation, the time difference methodwas used to separate two signal sets of noise and acoustic emission signals fromcrack propagation. The characteristics of noise signals in time and frequency domain,time-frequency domain were analyzed, then the features of the noise and AE signalsfrom crack propagation in the above range were extracted. The extracted featurevalues were optimized to get the best feature subset using the Euclidean distanceevaluation criteria. On this basis, BP neural network classifier was constructed toidentify samples of noise and acoustic emission signals from crack propagation,which effectively separated the noise signals, thus lay a foundation for the acousticemission technique to monitor the aluminum structures used in trains.