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Blind Enhancement of Harmonically Related Signals by Maximizing Skewness
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. Rubico Vibration Analysis AB.
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

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.

##### Place, publisher, year, edition, pages
Luleå tekniska universitet, 2014.
##### Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
##### National Category
Signal Processing
##### Research subject
Signal Processing
##### Identifiers
Local ID: e5906010-90eb-433e-a39f-7f3d5e682f7dISBN: 978-91-7439-917-2 (print)ISBN: 978-91-7439-918-9 (electronic)OAI: oai:DiVA.org:ltu-26461DiVA: diva2:999623
##### Note
Godkänd; 2014; 20140214 (kubova); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Aziz Kubilay Ovacikli Ämne: Signalbehandling/Signal Processing Uppsats: Blind Enhancement of Harmonically Related Signals by Maximizing Skewness Examinator: Professor Johan Carlson, Institutionen för system- och rymdteknik, Luleå tekniska universitet Diskutant: Tekn. Dr. Magnus Sandell, Toshiba Research Labs, Bristol, UK Tid: Onsdag den 4 juni 2014 kl 10.15 Plats: A1545, Luleå tekniska universitetAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-24Bibliographically approved
##### In thesis
1. Blind Adaptive Extraction of Impulsive Signatures from Sound and Vibration Signals
Open this publication in new window or tab >>Blind Adaptive Extraction of Impulsive Signatures from Sound and Vibration Signals
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

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.

##### Place, publisher, year, edition, pages
Luleå tekniska universitet, 2017
##### Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
##### National Category
Electrical Engineering, Electronic Engineering, Information Engineering
##### Research subject
Signal Processing
##### Identifiers
urn:nbn:se:ltu:diva-64982 (URN)978-91-7583-933-2 (ISBN)978-91-7583-934-9 (ISBN)
##### Public defence
2017-10-18, A109, 10:15 (English)
##### Supervisors
Available from: 2017-08-11 Created: 2017-08-09 Last updated: 2017-11-24Bibliographically approved

#### Open Access in DiVA

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Cite
Citation style
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