The constant growth of air traffic leads to increasing demands for the aircraft industry to manufacture airplanes
more economically and to ensure a higher level of efficiency, ecology and safety. During the last years important
improvements for fuselage structures have been achieved by application of new construction principles,
employment of sophisticated and/or alternative materials, and by improved manufacturing processes. In
particular the intensified application of fibre-reinforced plastics components is in the focus of current discussions
The main goal of an ongoing national project is to improve the existing ultrasonic test technology in such a way
that it is optimally suited for the examination of CFRP multilayer structures. The B-Scan and C-Scan results are
then used for the visualization of individual layers and the complete layer set-up.
First results of the project revealed that with carefully selected transducers and frequencies it is possible to detect defects and irregularities in the layer structure like delaminations, fibre cracking, ondulations, missing layers etc. and even to visualize the fibre orientations in the individual layers.
Many technical processes, e.g. in mechanical engineering, are causing acoustic emission. Acoustic emission (AE) consists of elastic waves, generated by stress changes in a solid. These waves can be detected at the surface of the solid by piezoelectric sensors. Classical methods to characterize acoustic emission signals include detecting and counting single events, describing their energy and frequency properties. The spreading conditions for acoustic waves in solids and the interference of a large number of AE sources lead to quasi-continuous signals from which no individual AE event can be extracted. This is also typical for wire sawing. If AE signals shall be used for online process monitoring, it is necessary to extract signal properties that are correlated with process changes. A common feature is the RMS value of the signal, which is correlated with the energy of AE and was found to be very sensitive to changing process conditions. Other features used are the peak values of the signal and the number of zero crossings. To get more information about the actual state of the observed process, parameters of the statistical distribution of short-time RMS like mean value, variation coefficient and skewness have been tested and their sensitivity to process changes have been investigated. An online monitor has been developed based on a hard- and software concept, adapted to process continuous acoustic emission data, with fast acquisition rates and signal processing.
Acoustic monitoring of technological processes requires methods that eliminate noise as much as possible. Sensor-near signal evaluation can contribute substantially. Frequently, a further necessity exists to integrate the measuring technique in the monitored structure. The solution described contains components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction, and digital communication. The core component is a digital signal processor (DSP). Digital signal processors perform the algorithms necessary for filtering, down sampling, FFT computation and correlation of spectral components particularly effective. A compact, sensor-near signal processing structure was realized. It meets the Match-X standard, which as specified by the German Association for Mechanical and Plant Engineering (VDMA) for development of micro-technical modules, which can be combined to applicaiton specific systems. The solution is based on AL2O3 ceramic components including different signal processing modules as ADC, as well as memory and power supply. An arbitrary waveform generator has been developed and combined with a power amplifier for piezoelectric transducers in a special module. A further module interfaces to these transducers. It contains a multi-channel preamplifier, some high-pass filters for analog signal processing and an ADC-driver. A Bluetooth communication chip for wireless data transmission and a DiscOnChip module are under construction. As a first application, the combustion behavior of safety-relevant contacts is monitored. A special waveform up to 5MHz is produced and sent to the monitored object. The resulting signal form is evaluated with special algorithms, which extract significant parameters of the signal, and transmitted via CAN-bus.
The economic efficiency and competitiveness of environment-friendly rail transportation depends on safety, availability and maintenance of single highly loaded structure components. Until now these components have been changed in fixed maintenance intervals irrespective of any usage related conditions. With the knowledge and evaluation of the component conditions, life cycle costs can be reduced by means of optimized maintenance and/or “fit for purpose” design. For example, rail-bound vehicle wheel sets are among the most highly stressed travelling gear components of the bogie. if such a component fails, a serious accident may occur. For this reason, a health monitoring system based on the interpretation of ultrasonic sound signatures has been developed. First, the ultrasonic waves generated by an artificial defect on the outer wheel tread of a railroad wheel towards an acoustic sensor, placed inside the hollow shaft of the railroad axis were simulated with a EFIT (Elastodynamic Finite Integration Technique). The results achieved proved that relevant signals can be found in a frequency range up to 300 kHz.
Based on this a diagnostic unit was designed and built for application under rotation conditions, which consists of a piezo-electric sensor, primary electronics, an analog-to-digital converter, a digital signal processor, a trigger unit, and a telemetric transmitter. This diagnostic unit was integrated in the hollow shaft of a railroad wheel axis, a component of a special laboratory test rig. Algorithms which allow for the rotation-synchronized processing of acoustic signals were implemented into the rotating diagnostic unit. After successfully completing a campaign for this test rig, a second test was performed inside the wheel/railroad simulation test rig of the Deutsche Bahn AG under railroad-like conditions. The data generated inside the hollow shaft of the railroad wheel axis by the diagnostic unit were telemetrically transmitted to an industrial computer. The detection of artificial defects of different sizes is shown in correlation with theoretical assumptions.