Structural health monitoring systems are increasingly used for comprehensive fatigue tests and surveillance of large scale
structures. In this paper we describe the development and validation of a wireless system for SHM application based on
The system is based on a wireless sensor network and focuses especially on low power measurement, signal processing
and communication. The sensor nodes were realized by compact, sensor near signal processing structures containing
components for analog preprocessing of acoustic signals, their digitization and network communication. The core
component is a digital microprocessor ARM Cortex-M3 von STMicroelectronics, which performs the basic algorithms
necessary for data acquisition synchronization and filtering.
The system provides network discovery and multi-hop and self-healing mechanisms. If the distance between two
communicating devices is too big for direct radio transmission, packets are routed over intermediate devices
The system represents a low-power and low-cost active structural health monitoring solution. As a first application, the
system was installed on a CFRP structure.
To operate wind turbines safely and efficiently, condition monitoring for the main components are of increasing
importance. Especially the lack of access to offshore installations increases inspection and maintenance costs.
The current work at Fraunhofer IZFP Dresden in the field of monitoring of wind turbines is focused on the development
of a condition monitoring system for rotor blades.
A special focus lies on the application of optical technologies for communication and power supply. It is not possible to
introduce electrical conductors into the rotor blade since it might cause tremendous damages by lightning.
The monitoring concept is based on a combination of low frequency integral vibration monitoring and acoustic
monitoring techniques in the frequency range between 10 and 100 kHz using guided waves. A joint application of
acousto ultrasonics and acoustic emission techniques will be presented. Challenges and solutions of such a field test like
sensor application, data handling and gathering as well as temperature variation are described.
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.
Conventionally, modal monitoring of Wind Turbine Rotor Blades is primarily based on the evaluation of
eigenfrequencies. Beyond this, combining a sensor network with the Operational Modal Analysis (OMA)
method, mode shape and parallely a local component are utilized here. In addition it is expected that the
damping, which is also determined by the OMA method, will give a lead on damage development at the rotor
already at an early stage. Modal monitoring by means of measurement is combined with FEM simulation and
with the comparison of results obtained from measurement and simulation. Moreover, this will establish a
connection between the engineer and the design data of a rotor blade, which also are based on FEM analyzes.
A further significant increase regarding error resolution is possible by combining the global modal methods with
locally sensitive monitoring methods, based on guided elastic waves. These assume plate-like structures through
which elastic waves propagate in the low-frequency ultrasonic range (10 - 100 kHz) in certain modes. These
different wave modes interact distinctively with inner structural damages such as web fractures and
delaminations. It is differentiated between piezoelectrically excited waves (acousto ultrasonics), and waves
produced by energy released at fractures, delamination etc. (acoustic emission). Applying a moderate number of
sensors, the combination of both methods can allow an effective monitoring of the global structure.
For comprehensive fatigue tests and surveillance of large scale structures, a vibration monitoring system working
in the Hz and sub Hz frequency range was realized and tested. The system is based on a wireless sensor network
and focuses especially on the realization of a low power measurement, signal processing and communication.
Regarding the development, we met the challenge of synchronizing the wireless connected sensor nodes with
The sensor nodes ware realized by compact, sensor near signal processing structures containing components for
analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network
communication. The core component is a digital micro controller which performs the basic algorithms necessary
for the data acquisition synchronization and the filtering. As a first application, the system was installed in a
rotor blade of a wind power turbine in order to monitor the Eigen modes over a longer period of time. Currently
the sensor nodes are battery powered.
The paper presents guided elastic waves and their identification and damage interaction in a CFRP plate. After the
excitation of a fiber transducer, different elastic waves emerge in a plate. By using specially developed 3D laser
scanning software it was possible to specify the different wave modes. These wave modes have been described
concerning their propagating velocities and different motion components. The interaction of different wave modes with
introduced impact damage (7J) is shown. In some experiments, it was proven that impact locations can be derived from
the detected Lamb waves. This work is continued to develop structural health monitoring systems (SHM) for selected
aircraft components (e. g. stringer elements, panels).
In the European project SAFE PIPES guided elastic waves in the frequency range between 100 and 250 kHz, generated
and detected by appropriate transducer arrays, are used to monitor the structural integrity of industrial piping systems by
comparing the actual state of the pipe with a predefined reference state. In the present paper, theoretical, numerical, and
experimental investigations are combined to study guided wave propagation and wave interaction with relevant defects
in detail. Based on these findings, a guided wave based multi-channel SHM system is designed and applied for
monitoring of crack-like defects in steel pipes. The first results reveal that guided wave based SHM in the kHz
frequency regime has great potential for online monitoring of piping systems. It is able to combine imaging techniques
with long range detection capabilities and therefore closes the gap between high-frequency NDE on the one hand and
low-frequency vibration analysis on the other hand.
Proc. SPIE. 6530, Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring 2007
KEYWORDS: Ferroelectric materials, Digital signal processing, Sensors, Composites, Structured optical fibers, Transducers, Signal processing, Structural health monitoring, Data communications, Acoustics
The presented paper describes a condition monitoring for Aircraft structures based on the evaluation of acoustical Lamb waves. Methods for effective sensor near signal processing are required to detect wave modes and to reduce noise as much as possible. Frequently, a further necessity exists to integrate the measuring technique into the monitored structure. To meet these requirements, sensor near units for signal processing have to be developed, which can be connected as nodes within a network.
A compact, sensor near signal processing structure has been realized containing components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network communication. The core component is a digital signal processor (DSP), which performs the basic algorithms necessary for filtering, down sampling, mode selection and correlation of spectral components particularly effective.
As a first application, impact detection and characterization of delaminations were realized for a fiber composite plate. Starting from the simulation of wave propagation, characteristic signal parameters were determined. In some experiments, it could be proven that impact locations and delaminations can be derived from the detected Lamb waves. This work is continued to develop special structural health monitoring systems (SHM) for selected aircraft components (e. g. stringer elements, panels).
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.
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.
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.