Radio-frequency-based measurement methods for bearing current analysis in induction motors

The most common reason for a low-voltage induction motor breakdown is a bearing failure. Along with the increasing popularity of modern frequency converters, bearing failures have become the most important motor fault type. Conditions in which bearing currents are likely to occur are generated as a side effect of fast du/dt switching transients. Once present, different types of bearing currents can accelerate the mechanical wear of bearings by causing deformation of metal parts in the bearing and degradation of the lubricating oil properties.The bearing current phenomena are well known, and several bearing current measurement and mitigation methods have been proposed. Nevertheless, in order to develop more feasible methods to measure and mitigate bearing currents, better knowledge of the phenomena is required. When mechanical wear is caused by bearing currents, the resulting aging impact has to be monitored and dealt with. Moreover, because of the stepwise aging mechanism, periodically executed condition monitoring measurements have been found ineffective. Thus, there is a need for feasible bearing current measurement methods that can be applied in parallel with the normal operation of series production drive systems. In order to reach the objectives of feasibility and applicability, nonintrusive measurement methods are preferred. In this doctoral dissertation, the characteristics and conditions of bearings that are related to the occurrence of different kinds of bearing currents are studied. Further, the study introduces some nonintrusive radio-frequency-signal-based approaches to detect and measure parameters that are associated with the accelerated bearing wear caused by bearing currents.

Oikosulkumoottorin ennaltaehkäisevä kunnonvalvonta resistanssimittausten avulla

Työssä tarkasteltiin sähköisiä tarkastusmenetelmiä oikosulkumoottorin ennaltaehkäisevälle kunnonvalvonnalle sekä näiden menetelmien toimivuutta eri vikojen havaitsemiseen. Työ to-teutettiin Porvoon Energian Tolkkisten voimalaitoksella ja se toimii samalla tarkastusohjeena työssä esitetyille tarkastusmenetelmille. Käytetyillä tarkastusmenetelmillä kyettiin havaitse-maan osa vioista ja niitä voidaan käyttää osana ennaltaehkäisevää kunnonvalvontaa. Kaikkia vikoja ei kuitenkaan työssä esitetyillä menetelmillä voitu havaita.

Yhdyskuntajätteen mekaanisen erottelun erotustehokkuus

EU:n jätehierarkia asettaa jätteenkäsittelyssä materiaalien hyötykäytön energiahyötykäytön edelle. EU on asettanut korkeat tavoitteet jätteenkierrätykseen, 50 painoprosenttia kotitalousjätteestä on ohjattava kierrätykseen vuoteen 2020 mennessä. Suomessa kaatopaikoista on pyritty eroon lisäämällä jätteenpolttokapasiteettia. Jätteiden hyödyntämisen osalta tilanne Suomessa on hyvä, mutta kierrätystavoitteiden täyttyminen nykyisillä toimilla vaikuttaa epätodennäköiseltä. Tässä työssä selvitetään mitä mekaanisia jätteen erottelumenetelmiä maailmalla on käytössä ja kuinka tehokkaita ne ovat. Työn tavoitteena on tutkia voitaisiinko kierrätystä Suomessa tehostaa yhdyskuntajätteen mekaanisella käsittelyllä. Kirjallisuusselvityksen lisäksi työssä on simuloitu mekaanisia erotteluketjuja ja verrattu niillä saatuja tuloksia Suomen syntypaikkalajittelun tasoon. Tämän tutkimuksen perusteella, mikään yksittäinen mekaaninen erottelumenetelmä ei riittävän tehokas erottelemaan kierrätettäviä materiaaleja yhdyskuntajätteestä. Mekaanisia erottelumenetelmiä tulee yhdistää lajittelulinjastoiksi, joiden optimoiminen on monen tekijän summa. Lajittelulinjaston suunnitteluun vaikuttavat muun muassa lähtömateriaalin laatu ja lopputuotteiden käyttötarkoitukset. Yhdyskuntajätteen sisältämä biojäte likaa herkästi muut jätteet ja vaikeuttaa mekaanisesti eroteltujen jätejakeiden uudelleenkäyttöä. Biojätteen poistaminen muiden jätteiden joukosta olisi ensiarvoisen tärkeää mekaanisen erotuksen tehokkuuden kannalta. Mekaaniset erotteluketjut poistavat tehokkaasti biojätettä ja metalleja, mutta lasin ja kuitujen osalta erotusketjujen tehokkuudet jäävät alhaisiksi. Muovien osalta mekaaninen erottelu voi parhaimmillaan ollaan erittäin tehokasta, toisaalta vaatimukset lähtömateriaalin laadulle ovat suuret. Muovien osalta syntypaikkalajittelun ja mekaanisen erottelun yhtäaikainen tehostaminen voisi tarjota ratkaisun kierrätysasteen nostamiseen.

Pyörivien sähkökäyttöjen ennakoiva kunnossapito reaaliaikaisen värähtelymittauksen avulla

Teollisuuden pyörivien sähkökäyttöjen ennakoivaan kunnossapitoon investoidaan jatkuvasti enemmän ja enemmän, jotta käytönaikaisia sähkökäyttöjen rikkoontumisia saataisiin ehkäistyksi. Lisäksi huoltojen ajoittamista halutaan optimoida siten, että suunniteltujen tuotantoseisokkien aikana saataisiin tehdyksi oleelliset ja tarpeelliset huoltotoimenpiteet. Pyörivien sähkökäyttöjen kunnonvalvontamenetelmiä ja -tapoja on erilaisia ja yhtenä vaihtoehtona voisi olla reaaliaikainen värähtelymittaus. Tässä diplomityössä tarkastellaan kunnossapidon käsitteitä, värähtelymittaukseen liittyviä suureita ja menetelmiä, sekä reaaliaikaisen värähtelymittauksen hyödyntämistä teollisuudessa.

Sähkökemiallisten mittausmenetelmien hyödyntäminen optimaalisessa kunnossapidossa

Sähkökemiallisia korroosionopeuden mittausmenetelmiä, yhdistettynä rakennema-teriaalien ohenemaseurantaan, on edelleen harvoin hyödynnetty toimilaitteiden ja rakenteiden elinkaarihallintaan liittyvässä kunnossapidossa. Tässä diplomityössä selvitetään sähkökemiallisten korroosiomittausten käyttöä optimaalisen kunnossapidon apuvälineenä. Työssä selvitetään erityisesti sähkö-kemiallisten mittausjärjestelmien soveltuvuutta voimalaitosten tulistinalueen kor-roosion sekä kylmäpään happo- ja vesikastepisteen mittaamiseen ja kriittisen kas-tepistelämpötilan määrittämiseen. Lisäksi selvitetään mittausten soveltuvuut-ta voimalaitosten polttoainehallinnan optimointiin sekä teknis-taloudellisuuteen perustuvaan prosessin säätämiseen. / Electrochemical corrosion rate measuring methods have been used years in indus-trial maintenance research. Still electrochemical corrosion rate measurement methods are rarely used in the industrial maintenance work related to the life cycle of equipment and structures. This work will study the suitability and use of electrochemical corrosion meas-urements in the optimum maintenance. The work will specifically address the suitability of electrochemical measuring systems for power plants super heater and the cold end corrosion monitoring. Study focus on acid and water dew point monitoring and the determination of the critical dew point temperature. In addi-tion, study the suitability of measurements of power plant fuel management as well as techno-economics based on the process of adjustment.

Developing asset management in district heating and cooling business in a large energy company

The purpose of this Master´s Thesis is to develop asset management and its practices in case company. District heating and cooling systems operated by case company around Finland, Sweden, Poland and the Baltics form an enormous-sized asset base where some parts are starting to reach their end of life-cycles. Large-sized asset renewal actions are under discussion and maintenance spending is increasing. Financially justified decisions in changing business environment are needed. Asset management is one of the most important concepts for production organization which operates with capital-intensive production assets. Organizations profitability is highly dependent on assets´ performance. Such assets, like district heating and cooling systems, should be utilized as efficiently as possible within their life-cycles but also maintained and renewed optimally. In this qualitative thesis, empirical interview study was conducted to describe the current situation on how the assets are managed in the case company and to examine the readiness to implement a new, risk-based solution. Asset management revealed to be a very well-known concept. From proposed risk-based asset management point of view, several key observations were made. It was seen as a suitable solution, but further development will be needed. Based on the need and findings, several key processes and frameworks were created and also tested with a case study. Assets` condition monitoring should be improved, which would have a positive impact on event probability assessment. Risk acceptance is also a thing to be discussed further. When the evaluation becomes fluent in single investment cases, portfolio-level expansion should be considered and started. As a result, thesis proposes a solution how risk-based asset management could be performed practically in a capital-intensive case company in order to optimize the maintenance spending in a long run. Created practical framework is made universal: similar principles can be applied into multiple cases in case company but also in other energy companies. Risk-based asset management`s benefits could be utilized best in portfolio-level optimization where the capital would be invested to the most important objects from total risk point of view. Eventually, such approach would allow case company to optimize capital spending in a situation where funds are not adequate to cover all the mandatory needs and prioritization between the investment alternatives will truly be needed.

Possibilities and challenges in utilization of information provided by Industrial Internet applications

Global digitalization has affected also industrial sector. A trend called Industrial Internet has been present for some years and established relatively steady position in businesses. Industrial Internet is also referred with the terminology Industry 4.0 and in consumer businesses IoT (Internet of Things). Eventually, trend consists of many traditionally proven technologies and concepts, such as condition monitoring, remote services, predictive maintenance and Internet customer portals. All these technologies and information related to them are estimated to change the rules of business in industrial sector. This may result even a new industrial revolution. This research has its focus on Industrial Internet products, services and applications. The study analyses four case companies and their digital service offerings. According to this analysis the comparison of these services is done to find out if there is still space for companies to gain competitive advantage through differentiation with these state of the art solutions. One of the case companies, Case Company Ltd., is working as a primary case company and a subscriber of this particular research. The research and results are analyzed primarily from this company’s perspective and need. In empirical part, the research clarifies how Case Company Ltd. has allocated its development resources through last five years. These allocations in certain categories are then compared to other case companies’ current customer offering and conclusions are made how the approach of different companies differ from each other. Existing theoretical knowledge of Industrial Internet is about to find its shape. In this research we take a look how the case company analysis and findings correlate with the existing knowledge and literature of the topic.

Electrophysiological and behavioural correlates of facial identity and expression perception deficits following a closed head injury /

A large variety of social signals, such as facial expression and body language, are conveyed in everyday interactions and an accurate perception and interpretation of these social cues is necessary in order for reciprocal social interactions to take place successfully and efficiently. The present study was conducted to determine whether impairments in social functioning that are commonly observed following a closed head injury, could at least be partially attributable to disruption in the ability to appreciate social cues. More specifically, an attempt was made to determine whether face processing deficits following a closed head injury (CHI) coincide with changes in electrophysiological responsivity to the presentation of facial stimuli. A number of event-related potentials (ERPs) that have been linked specifically to various aspects of visual processing were examined. These included the N170, an index of structural encoding ability, the N400, an index of the ability to detect differences in serially presented stimuli, and the Late Positivity (LP), an index of the sensitivity to affective content in visually-presented stimuli. Electrophysiological responses were recorded while participants with and without a closed head injury were presented with pairs of faces delivered in a rapid sequence and asked to compare them on the basis of whether they matched with respect to identity or emotion. Other behavioural measures of identity and emotion recognition were also employed, along with a small battery of standard neuropsychological tests used to determine general levels of cognitive impairment. Participants in the CHI group were impaired in a number of cognitive domains that are commonly affected following a brain injury. These impairments included reduced efficiency in various aspects of encoding verbal information into memory, general slower rate of information processing, decreased sensitivity to smell, and greater difficulty in the regulation of emotion and a limited awareness of this impairment. Impairments in face and emotion processing were clearly evident in the CHI group. However, despite these impairments in face processing, there were no significant differences between groups in the electrophysiological components examined. The only exception was a trend indicating delayed N170 peak latencies in the CHI group (p = .09), which may reflect inefficient structural encoding processes. In addition, group differences were noted in the region of the N100, thought to reflect very early selective attention. It is possible, then, that facial expression and identity processing deficits following CHI are secondary to (or exacerbated by) an underlying disruption of very early attentional processes. Alternately the difficulty may arise in the later cognitive stages involved in the interpretation of the relevant visual information. However, the present data do not allow these alternatives to be distinguished. Nonetheless, it was clearly evident that individuals with CHI are more likely than controls to make face processing errors, particularly for the more difficult to discriminate negative emotions. Those working with individuals who have sustained a head injury should be alerted to this potential source of social monitoring difficulties which is often observed as part of the sequelae following a CHI.

Analyse en composantes indépendantes avec une matrice de mélange éparse

L'analyse en composantes indépendantes (ACI) est une méthode d'analyse statistique qui consiste à exprimer les données observées (mélanges de sources) en une transformation linéaire de variables latentes (sources) supposées non gaussiennes et mutuellement indépendantes. Dans certaines applications, on suppose que les mélanges de sources peuvent être groupés de façon à ce que ceux appartenant au même groupe soient fonction des mêmes sources. Ceci implique que les coefficients de chacune des colonnes de la matrice de mélange peuvent être regroupés selon ces mêmes groupes et que tous les coefficients de certains de ces groupes soient nuls. En d'autres mots, on suppose que la matrice de mélange est éparse par groupe. Cette hypothèse facilite l'interprétation et améliore la précision du modèle d'ACI. Dans cette optique, nous proposons de résoudre le problème d'ACI avec une matrice de mélange éparse par groupe à l'aide d'une méthode basée sur le LASSO par groupe adaptatif, lequel pénalise la norme 1 des groupes de coefficients avec des poids adaptatifs. Dans ce mémoire, nous soulignons l'utilité de notre méthode lors d'applications en imagerie cérébrale, plus précisément en imagerie par résonance magnétique. Lors de simulations, nous illustrons par un exemple l'efficacité de notre méthode à réduire vers zéro les groupes de coefficients non-significatifs au sein de la matrice de mélange. Nous montrons aussi que la précision de la méthode proposée est supérieure à celle de l'estimateur du maximum de la vraisemblance pénalisée par le LASSO adaptatif dans le cas où la matrice de mélange est éparse par groupe.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Statistics of avalanches in martensitic transformations, II. Modelling

Using a scaling assumption, we propose a phenomenological model aimed to describe the joint probability distribution of two magnitudes A and T characterizing the spatial and temporal scales of a set of avalanches. The model also describes the correlation function of a sequence of such avalanches. As an example we study the joint distribution of amplitudes and durations of the acoustic emission signals observed in martensitic transformations [Vives et al., preceding paper, Phys. Rev. B 52, 12 644 (1995)].

Kinetics of martensitic transitions in Cu-Al-Mn under thermal cycling: Analysis at multiple length scales

In this paper we study the evolution of the kinetic features of the martensitic transition in a Cu-Al-Mn single crystal under thermal cycling. The use of several experimental techniques including optical microscopy, calorimetry, and acoustic emission, has enabled us to perform an analysis at multiple scales. In particular, we have focused on the analysis of avalanche events (associated with the nucleation and growth of martensitic domains), which occur during the transition. There are significant differences between the kinetics at large and small length scales. On the one hand, at small length scales, small avalanche events tend to sum to give new larger events in subsequent loops. On the other hand, at large length scales the large domains tend to split into smaller ones on thermal cycling. We suggest that such different behavior is the necessary ingredient that leads the system to the final critical state corresponding to a power-law distribution of avalanches.

A semi-blind reception scheme based on ICA for multiuser MIMO-STBC system

When the orthogonal space-time block code (STBC), or the Alamouti code, is applied on a multiple-input multiple-output (MIMO) communications system, the optimum reception can be achieved by a simple signal decoupling at the receiver. The performance, however, deteriorates significantly in presence of co-channel interference (CCI) from other users. In this paper, such CCI problem is overcome by applying the independent component analysis (ICA), a blind source separation algorithm. This is based on the fact that, if the transmission data from every transmit antenna are mutually independent, they can be effectively separated at the receiver with the principle of the blind source separation. Then equivalently, the CCI is suppressed. Although they are not required by the ICA algorithm itself, a small number of training data are necessary to eliminate the phase and order ambiguities at the ICA outputs, leading to a semi-blind approach. Numerical simulation is also shown to verify the proposed ICA approach in the multiuser MIMO system.

Automatic artefact removal from event-related potentials via clustering

This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.