Prism signal processing for sensor condition monitoring

2016

SHM systems have been under development for decades. Many technologies have bee n considered during these years (ultrasounds, optic fiber, Eddy current, electromagne tic, imaging, impedance measurements,...) and many solutions for fault detection algorithms, types of sensors, techniques for conducting the tests, etc. have also been introduced. Even though the SHM technology is almost mature, just a few SHM experimental systems are frequently used. SHM systems usually focus on structures with no limitation on volume r weight. The SHM systems are still under test in applications for aircraft and wind generators, where the weight and volume is critical. One of the most applied technologies in SHM is the monitoring through surface ultrasonic waves or Lamb waves. The main advantages of this technique are the low power consumption of the sensors and the capability to cover large areas using few se nsors. The ultrasound technique for SHM consists of a system composed of several modules: si...

TECHNISCHE HOCHSCHULE INGOLSTADT, 2016

Structural health monitoring (SHM), an emerging technique developed from Non Destructive Testing(NDT), combines advanced sensor technology with intelligent algorithms to interrogate the structural ‘health’ conditions. SHM performs the real-time and on-line damage detection with the help of in-situ sensors. The potential benefits of implementing SHM in automotive sector includes improving reliability, reducing life-cycle costs and high safety factor to improve the design of automotive components. For these purposes, a car has been fitted with measuring equipment’s: strain gauge and accelerometer to record the measuring drives in the present work. The aim of the work is to investigate the measured strain signals, suggestions for improvement and optimization identified based on the theories of signal conditioning and signal analysis and computationally implemented for a SHM system. The thesis work majorly deals with signal monitoring, damage monitoring and fatigue life monitoring in a SHM system. The measured strain signal is generated from hardware embedded platform and it deals with several practical issues. The measured signal has a disturbance superimposed on the good data. This disturbance can mask some important fatigue features of the stress or strain signals. One of the goal in this study includes an implementation and evaluation methods for noise suppression and drift removal of the signal. Hence several digital filters are designed and applied for the signals. Filter efficiency was checked, compared and recommended with a focus on setting-up a processed signal for next signal analysis step with amplitude based methods. A car is subjected to a large number of highly variable loads, but fatigue life prediction is typically based on constant amplitude fatigue behaviour. Therefore counting method was used for describing variable amplitude signals as a collection of constant amplitude cycles with predominantly using Rainflow analysis. The rainflow matrix and load spectrum are obtained for vehicle structure signals. An assessment of the remaining service life of a car structure is carried out by a simplified version of the linear damage accumulation hypothesis according to Palmgren-Miner rules and derivation of S-N curve which relates the stresses to the number of cycles to failure.

ABSTRACT

IFAC Proceedings Volumes, 1995

Several methods for analysis of acoustic emission signals are presented. They are mainly oriented to detection of changes in noisy signals and characterization of higher amplitud discrete pulses or bursts. The aim was to relate changes and events with failure, crack or wear in materials, being the final goal to obtain automatic means of detecting such changes and/or events. Performance evaluation was made using both simulated and laboratory test signals. The methods being presented are the following: a) Application of the Hopfield Neural Network (NN) model for classifying faults in pipes and detecting wear of a bearing. b) Application of the Kohonnen and Back Propagation Neural Network model for the same problem. c) Application of Kalman filtering to determine time ocurrence of bursts. d) Application of a bank of Kalman filters (KF) for failure detection in pipes. e) Study of amplitude distribution of signals for detecting changes in their shape. f) Application of the entropy distance to measure differences between signals.

Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 2004

Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.

Review of Scientific Instruments, 2013

IOP Conference Series: Materials Science and Engineering, Volume 138, Number 1, 2016

Damage detection is the basis of the damage identification task in Structural Health Monitoring. A good damage detection process can ensure the adequate work of a SHM System because allows to know early information about the presence of a damage in a structure under evaluation. However this process is based on the premise that all sensors are well installed and they are working properly, however, it is not true all the time. Problems such as debonding, cuts and the use of the sensors under different environmental and operational conditions result in changes in the vibrational response and a bad functioning in the SHM system. As a contribution to evaluate the state of the sensors in a SHM system, this paper describes a methodology for sensor fault detection in a piezoelectric active system. The methodology involves the use of PCA for multivariate analysis and some damage indices as pattern recognition technique and is tested in a blade from a wind turbine where different scenarios are evaluated including sensor cuts and debonding.

2020

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focus...

2016

Online monitoring systems demand an adequate operation of sensor system used to acquire structural state measurements. If a damaged sensor record is incorporated in the diagnosis algorithm, it could be generate uncertainties and generate unsuitable alarms. Thus, appropriate operation of sensor system is a critical requirement in order to obtain a high reliability for structural damage diagnosis algorithms. In this work a data-driven procedure is studied in order to mitigate the faulty sensor effect in a monitoring system. The studied method takes advantage of piezo-diagnostics approach, where piezoelectric devices are attached to the surface of the monitored structure to produce guided waves. Thus, piezoelectric measurements are analyzed by applying principal component analysis and cross-correlation, in order to detect abnormal behaviors. In this sense, the squared prediction error Q and Hotelling squared statistical indices are used to observe a typical behaviour caused by sensor p...

Sensors, 2014

† This paper includes materials from Pyayt et al. An approach for real-time levee health monitoring using signal processing methods.