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• 1.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Rubico Vibration Analysis AB. Rubico Vibration Analysis AB.
Material Impulse Response Estimation from Overlapping Ultrasound Echoes Using a Compressed Sensing Technique2017In: IEEE International Ultrasonics Symposium, IUS, IEEE Computer Society, 2017, article id 8091788Conference paper (Refereed)

When investigating thin materials with pulse echo ultrasound, multiple reflections (reverberations) from the layer(s) will overlap. It is therefore difficult to deduce information about speed of sound, thickness, density, etc. from the raw data. In order to extract this information, the overlapping pulses must be either decoupled or we must find some model of the material sample describing the wave propagation. It is, however, often reasonable to assume that the the number of reflections is small relative to the number of samples in the record signal of interest. In other words, the system describing the reverberations is sparse. In this paper we investigate, with simulations and with experiments on a 4.8 and 2.2 mm thick glass plate, respectively, how the framework of compressed sensing can be adopted in order to retrieve the impulse response of the material specimen

• 2.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Rubico AB.
Blind Adaptive Extraction of Impulsive Signatures from Sound and Vibration Signals2017Doctoral thesis, comprehensive summary (Other academic)

The two questions in science why" and how" are hereby answered in the context of statistical signal processing applied to vibration analysis and ultrasonic testing for fault detection and characterization in critical materials such as rolling bearings and thin layered media. Both materials are of interest in industrial processes. Therefore, assuring the best operating conditions on rolling bearings and product quality in thin layered materials is important.

The methods defended in this thesis are for retrieval of the impulsive signals arising from such equipments and materials, representing either faults or responses to an excitation. As the measurements collected via sensors usually consist of signals masked by some unknown systems and noise, retrieving the information-rich portion is often challenging. By exploiting the statistical characteristics due to their natural structure, a linear system is designed to recover the signals of interest in different scenarios. Suppressing the undesired components while enhancing the impulsive events by iteratively adapting a filter is the primary approach here. Signal recovery is accomplished by optimizing objectives (skewness and $\ell_1$-norm) quantifying the presumed characteristics, rising the question of objective surface topology and probability of ill convergence. To attack these, mathematical proofs, experimental evidences and comprehensive discussions are presented in the contributions each aiming to answer a specific question.

The aim in the theoretical study is to fill a gap in signal processing by providing analytical and numerical results especially on \emph{skewness} surface characteristics on a signal model (periodic impulses) build on harmonically related sinusoids. With understanding the inner workings and the conditions to suffice, the same approach is applied to different class of signals in ultrasonic testing, such as aperiodic finite energy signals (material impulse response) and a very short duration impulse as an excitation. A similar optimization approach aiming to enhance another attribute, \emph{sparseness}, is experimented numerically on the aforementioned signals as a case study. To summarize, two different objectives each quantifying a certain characteristic are optimized to recover signals carrying valuable information buried in noisy vibration and ultrasonic measurements.

Considering the fact that a research is qualified as successful if it creates more questions than it answers and lets ideas flourish creating scientific value, the presented work aims to achieve this in statistical signal processing. Analytical derivations assisted with experiments form the basis for observations, discussions and further questions to be studied and directed on similar phenomena arising from different sources in nature.

• 3.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. Rubico Vibration Analysis AB.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Skewness as an objective function for vibration analysis of rolling element bearings2014In: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013: Trieste, Italy, 4-6 Sept. 2013, Piscataway, NJ: IEEE Communications Society, 2014, p. 462-466Conference paper (Refereed)

The scale invariant third order moment, skewness, is analysed as an objective function to an adaptive gradient ascent algorithm. The purpose is to achieve a spectrum at the filter output that can enable identification of possible bearing defect signatures which are impulsive and periodic. Harmonically related sinusoids are used to represent such signatures and to build a signal model allowing characterization of the objective surface of skewness, providing insight to its convergent behaviour. The results are supported with an experiment from an industry setting. Robustness of the proposed algorithm is demonstrated by examining the frequency spectrum resulting from the signal model.

• 4.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. Rubico Vibration Analysis AB.
Blind Enhancement of Harmonically Related Signals by Maximizing Skewness2014Licentiate thesis, comprehensive summary (Other academic)

Rolling element bearings are used in rotating machinery in various industry branches. Their health status must be monitored continuously in order to establish proper operational conditions in a production process. Numerous approaches, which can be investigated under the subject of Condition Based Maintenance", have been studied within mechanical engineering and signal processing to be able to detect and classify possible faults on rolling bearings.Periodic impulsive signals can emerge from defected bearings within rotating machinery. As the signal is distorted by an unknown transfer function, noise and severe interference, the challenge becomes to reduce these effects as much as possible to extract valuable and reliable information about the rolling bearings' health status. Without any observation of the source signal, a scale-invariant higher order moment, skewness, can be used as a tool to characterize statistical properties to enhance the desired signal. It is the impulsiveness, thus asymmetry of the signal that will be promoted. To assess the performance of skewness, a signal model that consists of harmonically related sinusoids representing an impulsive source is built. Depending on such a model, surface characteristics of skewness are investigated. In relation to harmonic content, the ability of skewness in discovering such harmonic relation is studied. It has been observed that the optimization process converges to a setting where all harmonics are preserved, while any component that does not possess such a harmonic relation is suppressed. In the case of multiple mutually inharmonic source signals with harmonic support, it is shown that skewness maximization results in a setting where only the harmonic set with highest skewness remains. Finally, experimental examples are provided to support theoretical findings.

• 5.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Rubico Vibration Analysis AB.
Blind pulse compression through skewness maximization on overlapping echoes from thin layers2016In: IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016, Piscataway, NJ: IEEE conference proceedings, 2016, article id 7728571Conference paper (Refereed)

Pulse compression on overlapping echoes without knowledge of the pulse shape, transducer and propagation path impulse response is examined to provide valuable information about the sample structure in ultrasonic testing. A comparison against previous research is presented on two different levels of overlap severity with simulated signals. By exploiting the knowledge on the statistical characteristics of the signal of interest, an appropriate measure of merit, such as skewness, is maximized to promote impulsive occurrences to both extract the excitation signal and to enhance the impulse response of a material under test.

• 6.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. Rubico Vibration Analysis AB.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Rubico AB, Luleå. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Impulse Response Extraction and Parametric Modelling of Reverberating Ultrasonic Echoes from Thin Layers2015In: 2015 IEEE International Ultrasonics Symposium (IUS 2015): Taipei, 21-24 Oct. 2015, Piscataway, NJ: IEEE Communications Society, 2015, article id 7329331Conference paper (Refereed)

Enhacement of material impulse response buried in reverberating ultrasonic echoes from thin layered materials can be exploited in order to be able to detect possible flaws. One of the methods presented in this study is to enhance the impulse response of a material by training an adaptive filter that promotes and appropriate statistical characteristic such as asymmetry. The other approach is to employ a parametric linear model of reverberations that utilizes Maximum Likelihood Estimation on its parameters, to later suppress the reverberations and reveal possible flaws. Both approaches are investigated and shown to succeeed under certain conditions and supported with experiments.

• 7.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. Rubico Vibration Analysis AB.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Uncovering harmonic content via skewness maximization: a Fourier analysis2014In: Proceedings of the 22nd European Signal Processing Conference (EUSIPCO 2014): Lisbon, Portugal, 1-5 Sept. 2014, Piscataway, NJ: IEEE Communications Society, 2014, p. 481-485, article id 6952135Conference paper (Refereed)

Blind adaptation with appropriate objective function results in enhancement of signal of interest. Skewness is chosen as a measure of impulsiveness for blind adaptation to enhance impacting sources arising from defective rolling bearings. Such impacting sources can be modelled with harmonically related sinusoids which leads to discovery of harmonic content with unknown fundamental frequency by skewness maximization. Interfering components that do not possess harmonic relation are simultaneously suppressed with proposed method. An experimental example on rolling bearing fault detection is given to illustrate the ability of skewness maximization in uncovering harmonic content.

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Cite
Citation style
• apa
• harvard1
• ieee
• modern-language-association-8th-edition
• vancouver
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