Fatigue crack growth in a lap joint specimen extracted from a retired aircraft fuselage was monitored using bonded
continuous acoustic emission sensors. The specimen lasted nearly 350,000 cycles of tension-tension cyclic loading.
During this period a large number of acoustic emission signals were collected. Two distinct classes of events were
observed during this test. The first group of events consist of low amplitude, long rise time and long duration events
which could be attributed to fretting between various surfaces. The second group of events had short rise time and short
duration and is thought to be from fatigue cracks. This interpretation is based on the waveform characteristics observed
during this test and patterns seen in acoustic emission signals from known fatigue cracks in previous studies. Based on
this assumption the crack growth process appear to have initiated after 200,000 cycles of fatigue load and accelerated
during the final 20,000 cycles. The final fracture of the specimen occurred in the grip area and indications of this
impending failure were evident in the acoustic emission data. In addition, acoustic emission data also suggest fatigue
crack growth in an area inaccessible for visual examination.
Recently a new structural health monitoring system that employs a "continuous acoustic emission sensor" and an embeddable local processor has been proposed. The development of a processor that integrates the functions of signal conditioning, feature extraction, data storage, and digital communication is currently in progress. A prototype of this local processor chip has been developed. The integration of a continuous sensor with an embeddable local processor can potentially enable an inexpensive method of monitoring large and complex structures using acoustic emission signals. Such a system can reduce the cost, complexity, and weight of the required instrumentation. It is potentially scalable to large and complex structures and could be integrated into the structural material.
The success of the acoustic emission based structural health monitoring technique depends on its ability to discriminate between valid acoustic emission signals and ambient noise. In addition, the technique should be able to identify the damage mode from the acoustic emission waveforms. This paper focuses on the use of acoustic emission technique for the identification of failure modes in composite materials. Three types of failure modes in glass fabric epoxy composite laminates are considered. These are two types of delamination growth and transverse crack growth. Wavelet analysis is used to extract time frequency information from the acoustic emission signals. Different features of the waveform including the frequency components, Symmetric and Antisymmetric components, and amplitudes are used to classify the signals and identify the failure modes. The laboratory tests indicate that it is possible to distinguish the individual failure modes under consideration. It was also possible to filter out spurious AE signals that originate from extraneous sources using an appropriate choice of sensors and frequency components. An attempt is made to relate the rate of damage growth with the detected acoustic emission signal parameters.
Fatigue crack growth during the service of aging aircrafts has become an important issue and the monitoring of such cracks in hot spots is desirable. A structural health monitoring system using an acoustic emission technique under development for monitoring safety of such structures is described in this paper. A “continuous sensor” formed by connecting multiple sensor nodes in series arrangement to form a single channel sensor is proposed to monitor acoustic emission signals. This paper describes the work in progress on developing sensors, instrumentation, and measurement technique applicable to on-board monitoring of fatigue cracks in 7075-T6 aluminum lap joints. The traditional AE sensors as well as bonded nodes of continuous sensors described above were used to monitor acoustic emission signals emanating from crack growth in aluminum 7075 T6 specimens. It was possible to differentiate the signals due to crack growth from noise signals arising from fretting as well as RF pickup. The sensitivity of the bonded sensor under development was comparable to commercial high sensitivity resonant frequency AE sensors. The relationship between acoustic emission parameters and the crack growth rate in the aluminum specimens is examined.
A new approach for the Health Monitoring of structural systems is described in this paper. This technique is based on detecting the acoustic emission signals from damage progression in structures using an array of sensory nodes. Two different sensor configurations that could be used for monitoring wide areas on a structure are discussed. An reliable and cost effective health monitoring system can be an enabling technology for the widespread use of newly discovered high performance materials and design concepts in structural applications. Without a reliable health monitoring system, the lack of service experience and the susceptibility of new classes of materials to unexpected and unknown failure modes will likely delay their acceptance into actual structures. The proposed sensory system mimics biological neurons in its architecture and such an architecture can reduces the cost and complexity of the monitoring system. It is potentially scalable to large and complex structures and could be integrated into the structural materials. The paper summarizes recent work related to this sensory system and provides some new results.