Food Industry 4.0 readiness in Hungary

In terms of production value, the food industry is the third-largest in Hungary, the first in Hungary in terms of the number of employees, and the first in Europe in the processing industry, as well as a significant user of resources. The research examined the state of art of digitalization readiness, focusing on I4.0 technologies, which supports the management to operate more efficiently the enterprise and to make better decisions. So the focus was on integrated enterprise information systems, management support systems, business intelligence systems, industry 4.0 technologies, and issues related to their application. The analysis based on an online questionnaire survey the request sent to 4.600 enterprises, the response rate was 5% which was representative of the branches of production, covered the Hungarian food and beverage manufacturing sectors in 2019. The companies were asked the most critical technologies in development, going towards Industry 4.0. The research tools were LimeSurvey, Mailing List Server, Excel, Power BI (Desktop, Publishing Server to distribute the results). The used analysing methods were making calculations, pivot tables, models, dasboards. We found that a significant portion of businesses, 78 %, use mobile devices in the manufacturing process. The three most relevant digital technologies are geolocating (GPS, GNSS), cloud computing, and sensor technology. The current level of digitalization and integration cannot be said to be high, but respondents are very optimistic about expectations. Improvements are expected in all areas in the next 2-3 years in terms of digitalisation and integration. Vertical integration involves, first and foremost, cooperation with partners in the supply chain. Horizontal integration means close, real-time connectivity and collaboration within the company. Unfortunately, between 6% and 15% of SMEs (approximately 9% on average) and large enterprises, 36% have a digital strategy. According to the survey, the sector needs significant improvement and creating a digitalization strategy.In terms of production value, the food industry is the third-largest in Hungary, the first in Hungary in terms of the number of employees, and the first in Europe in the processing industry, as well as a significant user of resources. The research examined the state of art of digitalization readiness, focusing on I4.0 technologies, which supports the management to operate more efficiently the enterprise and to make better decisions. So the focus was on integrated enterprise information systems, management support systems, business intelligence systems, industry 4.0 technologies, and issues related to their application. The analysis based on an online questionnaire survey the request sent to 4.600 enterprises, the response rate was 5% which was representative of the branches of production, covered the Hungarian food and beverage manufacturing sectors in 2019. The companies were asked the most critical technologies in development, going towards Industry 4.0. The research tools were LimeSurvey, Mailing List Server, Excel, Power BI (Desktop, Publishing Server to distribute the results). The used analysing methods were making calculations, pivot tables, models, dasboards. We found that a significant portion of businesses, 78 %, use mobile devices in the manufacturing process. The three most relevant digital technologies are geolocating (GPS, GNSS), cloud computing, and sensor technology. The current level of digitalization and integration cannot be said to be high, but respondents are very optimistic about expectations. Improvements are expected in all areas in the next 2-3 years in terms of digitalisation and integration. Vertical integration involves, first and foremost, cooperation with partners in the supply chain. Horizontal integration means close, real-time connectivity and collaboration within the company. Unfortunately, between 6% and 15% of SMEs (approximately 9% on average) and large enterprises, 36% have a digital strategy. According to the survey, the sector needs significant improvement and creating a digitalization strategy

REVISIÓN DE LOS MÉTODOS DE DETECCIÓN DE FALLAS EN MOTORES SÍNCRONOS DE IMANES PERMANENTES CON APLICACIONES PARA INDUSTRIA 4.0 (REVIEW OF FAULT DETECTION METHODS FOR PERMANENT MAGNET SYNCHRONOUS MACHINES WITH APPLICATIONS FOR INDUSTRY 4.0)

ResumenEn este trabajo se presenta el estado del arte de la investigación existente en la metodología para el diagnóstico de fallas en motores síncronos de imanes permanentes (PMSM, por sus siglas en inglés) que tienen aplicación en los sistemas de industria 4.0. Los PMSM están incluidos en un conjunto de sistemas que deben tener la capacidad de diagnosticar su estado de operación y tomar decisiones para mantener la integridad de sus elementos en operación, evitando mantenimientos correctivos y paros de producción. Por tanto, se revisan trabajos de investigación, enfatizando aquellos de los últimos 10 años. En ellos se presentan las diferentes metodologías para el diagnóstico de fallas, tipos de fallas, algoritmos y elementos necesarios  para los PMSM. Con base en el análisis, queda manifiesta la gran relevancia del PMSM y el estudio de sus fallas para la industria 4.0.Palabras clave: Diagnóstico de Fallas, Métodos de Detección, PMSM.AbstractThis paper presents the state of the art of the existing research in the methodology for the diagnosis of faults in permanent magnet synchronous motors (PMSM) with application in industry 4.0 systems. The PMSM are included in a set of systems that must have the ability to diagnose their own operating status and make decisions to maintain the integrity of their elements in operation, avoiding corrective maintenance and production stoppages. Therefore, research works are reviewed, emphasizing those of the last 10 years. Different methodologies for the diagnosis of faults, types of faults, algorithms and elements necessary for this type of electric machine are presented. Based on the analysis, it is evident the great relevance of the PMSM and the study of its faults for the industry 4.0.Keywords: Fault Diagnosis, Detection Methods, PMSM

OPC UA -Tietomallin toteuttaminen PackML-standardin mukaisesti

Tässä kandidaatintyössä on tarkoituksena selvittää, miten luodaan minimitoteutus OPC UA tietomallista PackML-standardin mukaisesti. Vaikka OPC UA ja PackML ovat käsitteinä olleet olemassa melko pitkään, on ensimmäinen ja toistaiseksi ainoa spesifikaatio PackML:n toteuttamisesta OPC UA:han julkaistu vasta keväällä 2018. Ajatus PackML:n toteuttamisesta OPC UA:n avulla teollisuuden järjestelmissä on erittäin käyttökelpoinen, sillä siitä olisi paljon hyötyä järjestelmän käyttäjille. Tämän työn toteutus selkeyttää PackML:n käytännön toteuttamista ja tekee siitä helpommin lähestyttävän, kun saadaan selkeämpi käsitys toteutuksen rakenteesta. Työssä selvitettiin aluksi syitä siihen, miksi aiheen mukaista tietomallia ollaan tekemässä. Aluksi selvitettiin Industry 4.0:n konseptia ja sen vaatimuksia, jonka jälkeen selvitettiin hieman, miten automaatiojärjestelmien sisältämää dataa voidaan käyttää hyödyksi. Seuraavaksi tutkittiin OPC UA:ta, joka on käyttökelpoinen ratkaisu Industry 4.0:n esittämiin vaatimuksiin ja datan välittämiseen. OPC UA:n jälkeen tutkittiin, miten PackML toimii, ja miten se on yhdistettävissä OPC UA:han. Lopuksi luotiin teorian pohjalta tarkotuksenmukainen tietomalli Siemensin OPC UA modeling editorilla ja avattiin tietomallin rakennetta, sekä todettiin tietorakenteelle mahdollisia kehitysnäkökulmia. Tuloksena saatiin haluttu PackML-standardin mukaan rakennettu OPC UA-tietomallin toteutus, joka sisältää vain toiminnan kannalta pakolliset ratkaisut. Lähdemateriaalina käytettiin OPC Foundationin, OMACin ja Siemensin julkaisemia spesifikaatioita aiheeseen liittyen, sekä aiheeseen liittyvää kirjallisuutta ja julkaisuja. Lisäksi OPC Foundationin ja OMACin yhteistyökumppanien aiheeseen liittyviä verkkojulkaisuja käytettiin apuna

Managing the restoration of membranes in reverse osmosis desalination using a digital twin

This thesis studies degradation and restoration policies for a pressure vessel in a reverse osmosis (RO) desalination plant. In the study context, biofouling is the primary cause of the degradation of the RO membrane elements, amplified by seasonal algal blooms. This research developed a decision support system (DSS) for evaluating membrane restoration strategy. The engine of the DSS is a digital twin (DT), a virtual representation of wear (degradation) and restoration of membrane elements in a RO pressure vessel. The basis of the DT is a mathematical model that describes an RO pressure vessel as a novel multi-component system in which the hidden wear-states of individual elements (components) are quantified, and elements can be swapped or replaced. This contrasts with the contemporary presentation of a membrane system as a single system in the literature. The parameters of the model are estimated using statistical methods. The research approach is described in the context of a case study on the Carlsbad Desalination Plant in California. Results show a good fit between the observed and the modelled wear-states. Competing policies are compared based on risk, cost, downtime, and the number of stoppages. Projections indicate that a significant cost-saving can be achieved while not compromising the integrity of the plant. Alternative policies 11 and 12 showed better wear management than the current policy 10 of the maintenance company while reducing costs between $0.7 to$1.7 million for the next five years.The research in the thesis contributes toward maintenance modelling. New models of multivariate degradation and imperfect repair are presented. The research makes an important contribution to desalination and water treatment engineering, providing a unique membrane maintenance management approach currently absent from the literature. The thesis also contributes to the maintenance theory. It proposes a general approach for applying a decision support system (DSS) for maintenance requirements analysis, involving a digital twin (DT) for wear and repair projections when wear is stochastic, and repair effects are not immediately apparent. The essential elements of a DSS are discussed. This research encourages a dialogue between researchers of maintenance theory and modelling and practitioners of maintenance planning about decision support systems and digital twins that not only project the when but also evaluate the what in maintenance strategy. The presented concept of a DSS driven by a DT for maintenance requirement analysis has valuable practical implications, and the thesis, in discussing this concept, makes an essential contribution to the discussion about Industry 4.0, digital twins, and maintenance