Iowa Crop Progress & Condition, March 5, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, April 18, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, April 11, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, April 25, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, May 2, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, May 9, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Governor Branstad's 2015 Condition of the State Address, January 13, 2015

Speech by Governor Branstad

Iowa Crop Progress & Condition, May 15, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

Iowa Crop Progress & Condition, May 23, 2016

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report

On the Condition Monitoring of Induction machines

The research of condition monitoring of electric motors has been wide for several decades. The research and development at universities and in industry has provided means for the predictive condition monitoring. Many different devices and systems are developed and are widely used in industry, transportation and in civil engineering. In addition, many methods are developed and reported in scientific arenas in order to improve existing methods for the automatic analysis of faults. The methods, however, are not widely used as a part of condition monitoring systems. The main reasons are, firstly, that many methods are presented in scientific papers but their performance in different conditions is not evaluated, secondly, the methods include parameters that are so case specific that the implementation of a systemusing such methods would be far from straightforward. In this thesis, some of these methods are evaluated theoretically and tested with simulations and with a drive in a laboratory. A new automatic analysis method for the bearing fault detection is introduced. In the first part of this work the generation of the bearing fault originating signal is explained and its influence into the stator current is concerned with qualitative and quantitative estimation. The verification of the feasibility of the stator current measurement as a bearing fault indicatoris experimentally tested with the running 15 kW induction motor. The second part of this work concentrates on the bearing fault analysis using the vibration measurement signal. The performance of the micromachined silicon accelerometer chip in conjunction with the envelope spectrum analysis of the cyclic bearing faultis experimentally tested. Furthermore, different methods for the creation of feature extractors for the bearing fault classification are researched and an automatic fault classifier using multivariate statistical discrimination and fuzzy logic is introduced. It is often important that the on-line condition monitoring system is integrated with the industrial communications infrastructure. Two types of a sensor solutions are tested in the thesis: the first one is a sensor withcalculation capacity for example for the production of the envelope spectra; the other one can collect the measurement data in memory and another device can read the data via field bus. The data communications requirements highly depend onthe type of the sensor solution selected. If the data is already analysed in the sensor the data communications are needed only for the results but in the other case, all measurement data need to be transferred. The complexity of the classification method can be great if the data is analysed at the management level computer, but if the analysis is made in sensor itself, the analyses must be simple due to the restricted calculation and memory capacity.

Control hardware and Condition Monitoring System for Intersector Weld/Cut Robot

Työ sisältää ohjaislaitteiston vertailun ja valinnan rinnakkaisrakenteista robottia varten sekä kunnonvalvontajärjestelmän periaatteiden laadinnan kyseistä robottia varten. Ohjauslaitteisto sisältää teollisuustietokoneen sekä kenttäväylän. Sekä tietokoneesta että väylästä on teoriaosuus ja yksityiskohtaisempi valintaosuus. Teoriaosuudessa selitetään tarkemmin laitteiden toimintaperiaatteista. Valintaosuudessa kerrotaanmiksi jokin tietty laite on valittu käytettäväksi robotin ohjauksessa. Kunnonvalvontateoria ja rinnakkaisrakenteisen robotin kunnonvalvonnan keinot ovat työn toinen osa. Teoriaosa sisältää yleisluonteisen selvityksen vikaantumisesta ja valvonnasta. Erikoisrobotin kunnonvalvonnan keinot esitetään työssä tietyssä järjestyksessä. Ensin esitetään mahdolliset vikatilanteet. Toisessa kohdassa havainnollistetaan vikojen havaitseminen.

Design of a Condition Monitoring System for a Glass Wool Production Line

Työn tarkoituksena oli suunnitella kunnonvalvontajärjestelmä kahdelle lasivillan tuotantolinjalle. Suunnitteluprosessin lisäksi työssä on esitelty erilaisia kunnonvalvontamenetelmiä. Työn alussa on kerrottu erilaisista kunnonvalvontamenetelmistä, joilla voidaan seurata erilaisten laitteiden ja koneiden toimintakuntoa.Erityisesti työssä on tarkasteltu teollisuudessa yleistyviä kunnonvalvonnan värähtelymittauksia. Työssä suunniteltu kunnonvalvontajärjestelmä perustuu viiteen eri menetelmään, jotka ovat värähtelymittaus, lämpötilanmittaus lämpökameralla, lämpötilanmittaus kannettavalla mittarilla, kuuntelu elektronisella stetoskoopilla ja pyörivien osien kunnontarkkailu stroboskoopilla. Kunnonvalvontajärjestelmän suunnittelu on tehty useassa eri vaiheessa. Ensin työssä on kartoitettu tuotannon kannalta tärkeimmät laitteet ja niiden mahdolliset vikaantumistavat. Seuraavaksi on valittu sopivat kunnonvalvontamenetelmät ja tehty mittaussuunnitelma, jossa on esitetty eri laitteille suoritettavat mittaukset ja mittausten aikavälit.Lopuksi työssä on esitelty muutama esimerkkitapaus kunnonvalvontamenetelmien käytöstä sekä kerrottu mahdollisista tulevaisuuden kehitysmahdollisuuksista.

Independent sources of condition dependency and multiple pathways determine a composite trait: lessons from carotenoid-based plumage colouration.

Many colour ornaments are composite traits consisting of at least four components, which themselves may be more complex, determined by independent evolutionary pathways, and potentially being under different environmental control. To date, little evidence exists that several different components of colour elaboration are condition dependent and no direct evidence exists that different ornamental components are affected by different sources of variation. For example, in carotenoid-based plumage colouration, one of the best-known condition-dependent ornaments, colour elaboration stems from both condition-dependent pigment concentration and structural components. Some environmental flexibility of these components has been suggested, but specifically which and how they are affected remains unknown. Here, we tested whether multiple colour components may be condition dependent, by using a comprehensive 3 × 2 experimental design, in which we carotenoid supplemented and immune challenged great tit nestlings (Parus major) and quantified effects on different components of colouration. Plumage colouration was affected by an interaction between carotenoid availability and immune challenge. Path analyses showed that carotenoid supplementation increased plumage saturation via feather carotenoid concentration and via mechanisms unrelated to carotenoid deposition, while immune challenge affected feather length, but not carotenoid concentration. Thus, independent condition-dependent pathways, affected by different sources of variation, determine colour elaboration. This provides opportunities for the evolution of multiple signals within components of ornamental traits. This finding indicates that the selective forces shaping the evolution of different components of a composite trait and the trait's signal content may be more complex than believed so far, and that holistic approaches are required for drawing comprehensive evolutionary conclusions.