Remote Condition Monitoring and Asset Management in Offshore Power Systems

Condition monitoring systems for physical assets are constantly becoming more and more common in the industrial sector. At the same time an increasing portion of asset monitoring systems are being remotely supported. As global competitors are actively developing solutions for condition monitoring and condition-based maintenance, which it enables, Wärtsilä too feels the pressure to provide customers with more sophisticated condition-based maintenance solutions. The main aim of this thesis study is to consider Wärtsilä remote condition monitoring solutions and how they relate to similar solutions from other suppliers and end customers’ needs, in the context of offshore assets. A theoretical study is also included in the thesis, where the concepts of condition monitoring, condition-based maintenance, maintenance management and physical asset management are introduced.

The importance of control monitoring systems in wind parks maintenance

The objective of every wind energy producer is to reduce operational costs associated to the production as a way to increase profits. One other issue that must be looked carefully is the equipment maintenance. Increase the availability of wind turbines by reducing the downtime associated to failures is a good strategy to achieve the main goal of increase profits. As a way to help in the definition of the best maintenance strategies, condition monitoring systems (CMS) have an important role to play. Informatics tools to make the condition monitoring of the wind turbines were developed and are now being installed as a way to help producers reducing the operational costs. There are a lot of developed systems to do the monitoring of a wind turbine or the whole wind park, in this paper will be made an overview of the most important systems.

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.

Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project (http://www.maxbeproject.eu/), an EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monitoring systems are integrated into a platform. Also extensive laboratory tests were performed to correlate the in situ measurements to the status of the axle bearing life. With the MAXBE project concept it will be possible: to contribute to detect at an early stage axle bearing failures; to create conditions for the operational and technical integration of axle bearing monitoring and maintenance in different European railway networks; to contribute to the standardization of the requirements for the axle bearing monitoring, diagnosis and maintenance. Demonstration of the developed condition monitoring systems was performed in Portugal in the Northern Railway Line with freight and passenger traffic with a maximum speed of 220 km/h, in Belgium in a tram line and in the UK. Still within the project, a tool for optimal maintenance scheduling and a smart diagnostic tool were developed. This paper presents a synthesis of the most relevant results attained in the project. The successful of the project and the developed solutions have positive impact on the reliability, availability, maintainability and safety of rolling stock and infrastructure with main focus on the axle bearing health.

Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Design and implementation of a vibrational condition monitoring system

Industrial companies, particularly those with induction motors and gearboxes as integral components of their systems, are utilizing Condition Monitoring (CM) systems more frequently in order to discover the need for maintenance in advance, as traditional maintenance only performs tasks when a failure has been identified. Utilizing a CM system is essential to boost productivity and minimize long-term failures that result in financial loss. The more exact and practical the CM system, the better the data analysis, which adds to a more precise maintenance forecast. This thesis project is a cooperation with PEI Vibration Monitoring s.r.l. to design and construct a low-cost vibrational condition monitoring system to check the health of induction motors and gearboxes automatically. Moreover, according to the company's request, such a system should have specs comparable to NI 9234, one of the company's standard Data Acquisition (DAQ) boards, but at a significantly cheaper price. Additionally, PEI VM Company has supplied all hardware and electronic components. The suggested CM system is capable of highprecision autonomous monitoring of induction motors and gearboxes, and it consists of a Raspberry Pi 3B and MCC 172 DAQ board.

Low-cost vibration sensors: tendencies and applications in condition monitoring of machines and structures

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Planning smart cities using wireless low energy monitoring systems

Thesis to obtain the Master Degree in Electronics and Telecommunications Engineering

Effect of Video Camera-Based Remote Roadway Condition Monitoring on Snow Removal-Related Maintenance Operations. 2014

Remote monitoring through the use of cameras is widely utilized for traffic operation, but has not been utilized widely for roadway maintenance operations. The Utah Department of Transportation (UDOT) has implemented a new remote monitoring system, referred to as a Cloud-enabled Remote Video Streaming (CRVS) camera system for snow removal-related maintenance operations in the winter. The purpose of this study was to evaluate the effectiveness of the use of the CRVS camera system in snow removal-related maintenance operations. This study was conducted in two parts: opinion surveys of maintenance station supervisors and an analysis on snow removal-related maintenance costs. The responses to the opinion surveys mostly displayed positive reviews of the use of the CRVS cameras. On a scale of 1 (least effective) to 5 (most effective), the average overall effectiveness given by the station supervisors was 4.3. An expedition trip for this study was defined as a trip that was made to just check the roadways if snow-removal was necessary. The average of the responses received from surveys was calculated to be a 33 percent reduction in expedition trips. For the second part of this study, an analysis was performed on the snow removal-related maintenance cost data provided by UDOT to see if the installation of a CRVS camera had an effect in reducing expedition trips. This expedition cost comparison was performed for 10 sets of maintenance stations within Utah. It was difficult to make any definitive inferences from the comparison of expedition costs over the years for which precipitation and expedition cost data were available; hence a statistical analysis was performed using the Mixed Model ANOVA. This analysis resulted in an average of 14 percent higher ratio of expedition costs at maintenance stations with a CRVS camera before the installation of the camera compared to the ratio of expedition costs after the installation of the camera. This difference was not proven to be statistically significant at the 95 percent confident level, but indicated that the installation of CRVS cameras was on the average helpful in reducing expedition costs and may be considered practically significant. It is recommended that more detailed and consistent maintenance cost records be prepared for accurate analysis of cost records for this type of study in the future.

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.

Applicability of Power-Line Communications to Data Transfer of On-line Condition Monitoring of Electrical Drives

The research of power-line communications has been concentrated on home automation, broadband indoor communications and broadband data transfer in a low voltage distribution network between home andtransformer station. There has not been carried out much research work that is focused on the high frequency characteristics of industrial low voltage distribution networks. The industrial low voltage distribution network may be utilised as a communication channel to data transfer required by the on-line condition monitoring of electric motors. The advantage of using power-line data transfer is that it does not require the installing of new cables. In the first part of this work, the characteristics of industrial low voltage distribution network components and the pilot distribution network are measured and modelled with respect topower-line communications frequencies up to 30 MHz. The distributed inductances, capacitances and attenuation of MCMK type low voltage power cables are measured in the frequency band 100 kHz - 30 MHz and an attenuation formula for the cables is formed based on the measurements. The input impedances of electric motors (15-250 kW) are measured using several signal couplings and measurement based input impedance model for electric motor with a slotted stator is formed. The model is designed for the frequency band 10 kHz - 30 MHz. Next, the effect of DC (direct current) voltage link inverter on power line data transfer is briefly analysed. Finally, a pilot distribution network is formed and signal attenuation in communication channels in the pilot environment is measured. The results are compared with the simulations that are carried out utilising the developed models and measured parameters for cables and motors. In the second part of this work, a narrowband power-line data transfer system is developed for the data transfer ofon-line condition monitoring of electric motors. It is developed using standardintegrated circuits. The system is tested in the pilot environment and the applicability of the system for the data transfer required by the on-line condition monitoring of electric motors is analysed.

Techno-Economic Feasibility of Novel On-Line Condition Monitoring Methods in Low Voltage Distribution Networks

The focus in this thesis is to study both technical and economical possibilities of novel on-line condition monitoring techniques in underground low voltage distribution cable networks. This thesis consists of literature study about fault progression mechanisms in modern low voltage cables, laboratory measurements to determine the base and restrictions of novel on-line condition monitoring methods, and economic evaluation, based on fault statistics and information gathered from Finnish distribution system operators. This thesis is closely related to master’s thesis “Channel Estimation and On-line Diagnosis of LV Distribution Cabling”, which focuses more on the actual condition monitoring methods and signal theory behind them.