An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Long-finned pilot whale population diversity and structure in Atlantic waters assessed through biogeochemical and genetic markers

Acknowledgements. Cetacean samples were collected under the auspices of stranding monitoring programs run by the Sociedade Portuguesa de Vida Selvagem, the Coordinadora para o Estudio dos Mamíferos Mariños (supported by the regional government Xunta de Galicia), the UK Cetacean Strandings Investigation Programme and the Scottish Agriculture College Veterinary Science Division (jointly funded by Defra and the Devolved Governments of Scotland and Wales), the Marine Mammals Research Group of the Institute of Marine Research (Norway), the Museum of Natural History of the Faroe Islands and the International Fund for Animal Welfare Marine Mammal Rescue and Research Program (USA). The authors thank all the members of these institutions and organizations for their assistance with data and sample collection. S.S.M., P.M.F. and M.F. were supported by PhD grants from the Fundação para a Ciência e Tecnologia (POPH/FSE ref SFRH/BD/ 38735/ 2007, SFRH/BD/36766/2007 and SFRH/BD/30240/ 2006, respectively). A.L. was supported by a postdoctoral grant from the Fundação para a Ciência e Tecnologia (ref SFRH/BPD/82407/2011). The work related to strandings and tissue collection in Portugal was partially supported by the SafeSea project EEAGrants PT 0039 (supported by Iceland, Liechtenstein and Norway through the EEA Financial Mechanism), the MarPro project Life09 NAT/PT/000038 (funded by the European Union program LIFE+) and the project CetSenti FCT RECI/AAG-GLO/0470/2012 (FCOMP- 01-0124-FEDER-027472) (funded by the program COMPETE and the Fundação para a Ciência e Tecnologia). G.J.P. thanks the University of Aveiro and Caixa Geral de Depósitos (Portugal) for financial support. The authors acknowledge the assistance of the chemical analysts at Marine Scotland Science with the fatty acid analysis.

Long-finned pilot whale population diversity and structure in Atlantic waters assessed through biogeochemical and genetic markers

Acknowledgements. Cetacean samples were collected under the auspices of stranding monitoring programs run by the Sociedade Portuguesa de Vida Selvagem, the Coordinadora para o Estudio dos Mamíferos Mariños (supported by the regional government Xunta de Galicia), the UK Cetacean Strandings Investigation Programme and the Scottish Agriculture College Veterinary Science Division (jointly funded by Defra and the Devolved Governments of Scotland and Wales), the Marine Mammals Research Group of the Institute of Marine Research (Norway), the Museum of Natural History of the Faroe Islands and the International Fund for Animal Welfare Marine Mammal Rescue and Research Program (USA). The authors thank all the members of these institutions and organizations for their assistance with data and sample collection. S.S.M., P.M.F. and M.F. were supported by PhD grants from the Fundação para a Ciência e Tecnologia (POPH/FSE ref SFRH/BD/ 38735/ 2007, SFRH/BD/36766/2007 and SFRH/BD/30240/ 2006, respectively). A.L. was supported by a postdoctoral grant from the Fundação para a Ciência e Tecnologia (ref SFRH/BPD/82407/2011). The work related to strandings and tissue collection in Portugal was partially supported by the SafeSea project EEAGrants PT 0039 (supported by Iceland, Liechtenstein and Norway through the EEA Financial Mechanism), the MarPro project Life09 NAT/PT/000038 (funded by the European Union program LIFE+) and the project CetSenti FCT RECI/AAG-GLO/0470/2012 (FCOMP- 01-0124-FEDER-027472) (funded by the program COMPETE and the Fundação para a Ciência e Tecnologia). G.J.P. thanks the University of Aveiro and Caixa Geral de Depósitos (Portugal) for financial support. The authors acknowledge the assistance of the chemical analysts at Marine Scotland Science with the fatty acid analysis.

Molecular hydrogen (H2) mixing ratio and stable isotopic composition (dD) at the Cabauw tall tower in the Netherlands (2008-2012)

Measurements of the stable isotopic composition (dD(H2) or dD) of atmospheric molecular hydrogen (H2) are a useful addition to mixing ratio (X(H2)) measurements for understanding the atmospheric H2 cycle. dD datasets published so far consist mostly of observations at background locations. We complement these with observations from the Cabauw tall tower at the CESAR site, situated in a densely populated region of the Netherlands. Our measurements show a large anthropogenic influence on the local H2 cycle, with frequently occurring pollution events that are characterized by X(H2) values that reach up to 1 ppm and low dD values. An isotopic source signature analysis yields an apparent source signature below -400 per mil, which is much more D-depleted than the fossil fuel combustion source signature commonly used in H2 budget studies. Two diurnal cycles that were sampled at a suburban site near London also show a more D-depleted source signature (-340 per mil), though not as extremely depleted as at Cabauw. The source signature of the Northwest European vehicle fleet may have shifted to somewhat lower values due to changes in vehicle technology and driving conditions. Even so, the surprisingly depleted apparent source signature at Cabauw requires additional explanation; microbial H2 production seems the most likely cause. The Cabauw tower site also allowed us to sample vertical profiles. We found no decrease in (H2) at lower sampling levels (20 and 60m) with respect to higher sampling levels (120 and 200m). There was a significant shift to lower median dD values at the lower levels. This confirms the limited role of soil uptake around Cabauw, and again points to microbial H2 production during an extended growing season, as well as to possible differences in average fossil fuel combustion source signature between the different footprint areas of the sampling levels. So, although knowledge of the background cycle of H2 has improved over the last decade, surprising features come to light when a non-background location is studied, revealing remaining gaps in our understanding.

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

Determining epithelial contribution to in vivo mesenchymal tumour expression signature using species-specific microarray profiling analysis of xenografts

Gene expression profiling using microarrays and xenograft transplants of human cancer cell lines are both popular tools to investigate human cancer. However, the undefined degree of cross hybridization between the mouse and human genomes hinders the use of microarrays to characterize gene expression of both the host and the cancer cell within the xenograft. Since an increasingly recognized aspect of cancer is the host response (or cancer-stroma interaction), we describe here a bioinformatic manipulation of the Affymetrix profiling that allows interrogation of the gene expression of both the mouse host and the human tumour. Evidence of microenvironmental regulation of epithelial mesenchymal transition of the tumour component in vivo is resolved against a background of mesenchymal gene expression. This tool could allow deeper insight to the mechanism of action of anti-cancer drugs, as typically novel drug efficacy is being tested in xenograft systems.

Phylogenetic analysis of the family Thermaceae with an emphasis on signature position and secondary structure of 16S rRNA

The sequences of the 16S rRNA genes from 38 strains of the family Thermaceae were compared by alignment analysis. The genus-specific and species-specific base substitutions or base deletions (signature positions) were found in three hypervariable regions (in the helices 6, 10 and 17). The differentiation of secondary structures of the high variable regions in the 5' end (38-497) containing several signature positions further supported the concept. Based on the comparisons of the secondary structures in the segments of 16S rRNAs, a key to the species of the family Thermaceae was proposed. (C) 2003 Published by Elsevier Science B.V. on behalf of the Federation of European Microbiological Societies.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

Theoretical Analysis of Aggregation in Block-Copolymer Films: The Optical Signature

We focus this work on the theoretical investigation of the block-copolymer poly [oxyoctyleneoxy-(2,6-dimethoxy-1,4phenylene-1,2-ethinylene-phenanthrene-2,4diyl) named as LaPPS19, recently proposed for optoelectronic applications. We used for that a variety of methods, from molecular mechanics to quantum semiempirical techniques (AMI, ZINDO/S-CIS). Our results show that as expected isolated LaPPS19 chains present relevant electron localization over the phenanthrene group. We found, however, that LaPPS19 could assemble in a pi-stacked form, leading to impressive interchain interaction; the stacking induces electronic delocalization between neighbor chains and introduces new states below the phenanthrene-related absorption; these results allowed us to associate the red-shift of the absorption edge, seen in the experimental results, to spontaneous pi-stack aggregation of the chains. (C) 2009 Wiley Periodicals, Inc. Int J Quantum Chem 110: 885-892, 2010

Approccio network-based alla discriminant analysis mediante HPC per la ricerca di signature ottimali in dati ad alta dimensionalità

Il presente lavoro di tesi si inserisce nell’ambito della classificazione di dati ad alta dimensionalità, sviluppando un algoritmo basato sul metodo della Discriminant Analysis. Esso classifica i campioni attraverso le variabili prese a coppie formando un network a partire da quelle che hanno una performance sufficientemente elevata. Successivamente, l’algoritmo si avvale di proprietà topologiche dei network (in particolare la ricerca di subnetwork e misure di centralità di singoli nodi) per ottenere varie signature (sottoinsiemi delle variabili iniziali) con performance ottimali di classificazione e caratterizzate da una bassa dimensionalità (dell’ordine di 101, inferiore di almeno un fattore 103 rispetto alle variabili di partenza nei problemi trattati). Per fare ciò, l’algoritmo comprende una parte di definizione del network e un’altra di selezione e riduzione della signature, calcolando ad ogni passaggio la nuova capacità di classificazione operando test di cross-validazione (k-fold o leave- one-out). Considerato l’alto numero di variabili coinvolte nei problemi trattati – dell’ordine di 104 – l’algoritmo è stato necessariamente implementato su High-Performance Computer, con lo sviluppo in parallelo delle parti più onerose del codice C++, nella fattispecie il calcolo vero e proprio del di- scriminante e il sorting finale dei risultati. L’applicazione qui studiata è a dati high-throughput in ambito genetico, riguardanti l’espressione genica a livello cellulare, settore in cui i database frequentemente sono costituiti da un numero elevato di variabili (104 −105) a fronte di un basso numero di campioni (101 −102). In campo medico-clinico, la determinazione di signature a bassa dimensionalità per la discriminazione e classificazione di campioni (e.g. sano/malato, responder/not-responder, ecc.) è un problema di fondamentale importanza, ad esempio per la messa a punto di strategie terapeutiche personalizzate per specifici sottogruppi di pazienti attraverso la realizzazione di kit diagnostici per l’analisi di profili di espressione applicabili su larga scala. L’analisi effettuata in questa tesi su vari tipi di dati reali mostra che il metodo proposto, anche in confronto ad altri metodi esistenti basati o me- no sull’approccio a network, fornisce performance ottime, tenendo conto del fatto che il metodo produce signature con elevate performance di classifica- zione e contemporaneamente mantenendo molto ridotto il numero di variabili utilizzate per questo scopo.