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15 May 2018
Birger Jarl Hotel -Stockholm
120 Tickets
22 Speakers
Towards Data-Driven Maintenance
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15th of May, Stockholm, Sweden

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Towards Data-Driven Maintenance
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15th of May, Stockholm, Sweden

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Towards Data-Driven Maintenance
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15th of May, Stockholm, Sweden


The Maintenance Analytics Summit is an annual event bringing together practitioners, experts, academia, and visionaries working with Data-Driven Maintenance to share ideas, and discuss ways to harness the full potential of machine data and Advanced Analytics to improve and automise their condition monitoring and maintenance processes. The agenda is suited to guide you through the process of extracting knowledge from data by using the latest methodologies, tools and algorithms. With domestic and international speakers on stage, interactive panel discussions and plenty of learning and networking activities in the exhibition area, the Maintenance Analytics Summit is the place to be for all professionals and organisations working with Data Management and utilisation of data, Analytics, IIOT, Data Science, and Machine Learning, to innovate and improve their operational processes ex. predict and avoid machine failure.


• Analytics, Modeling and Innovation Stage • Data Integration and Contextualisation Stage The Predictive Maintenance Analytics Summit will focus on practical case studies on taking pro-active measures based on advanced data analytics to predict and avoid machine failure. Domestic and International speakers. All presentations are 30 minutes. Presentations held in English.

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On demand of surveyed delegates, on this first edition we are adding mixed panels composed by practitioners and experts. By mixing the panel, our intent is to create a good discussion and a contrast between what is possible and what is actually done in organisations today.

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As the summit themes and topics are technical, it is natural that demonstrations of the latest technology advancements in the area should be part of the event features. On this year’s summit we are bringing some of the leading technology and service providers in the area, some of them presenting their tools and services for the first time in Sweden.


The agenda is carefully designed to match the delegate community challenges and needs. Based on our pre-event research, we have constructed the agenda that emulates the current market maturity, current project objectives, as well the aspiration for future technology insight. The Predictive Maintenance Analytics Summit will focus on practical case studies on the way how to move towards Data- and AI-Driven Predictive maintenance.


To enable more insightful knowledge sharing between peers and provide bigger event experience, delegates this year will have the opportunity to schedule meetings and share contacts via the official event application.

Qualified delegates

The Maintenance Analytics Summit is tailor made for every professional or organisation working with, or interested in taking proactive measures based on advanced data analytics to predict and avoid machine failure. If you are working with the following disciplines, then this is the must-attend event: Predictive Maintenance (PdM) Strategy, CTO, IIOT, Analytics for PdM, Data Science for PdM, Machine Learning for PdM, Big Data, Data Quality, Data Integration, Data Modeling, Data Visualisation.


The program is tailor-made to follow a red thread and guide you through the entire process of setting up a Data - and AI-Driven Predictive Maintenance

15 May
day 1
15 May
day 1
Registration Starts
Chairman’s Opening remarks - The game changer - Analytics, IIOT, AI and Predictive Maintenance
The Digital Butterfly Effect

Everyone is taking about digitalization and digital strategies. There is no “digital strategy”, there is only strategy! At Stena Line our mission statement is “The works first cognitive ferry operator”. I’ll show you how! Key takeways: - What to actually do when doing “digitalization” - Using A.I as a foundation - Changing the culture to understand machine learning Amer Mohammed Head of Digital Innovation Stena Line Amer Mohammed is an entrepreneur who has crashed three companies and sold two. Friends and colleagues told him that the shipping industry will never change at it will always be “old men on a boat”. Amer is proving them wrong and will show the world how.

Panel: The State of Now: Challenges with Data- and AI-Driven approach to Predictive Maintenance

Martin Lundqvist VP Government Solutions Arundo Analytics Martin is a vice president at Arundo Analytics based in Stockholm. He is responsible for Arundo's work within Government Solutions internationally, and a Nordic sales executive. Before joining Arundo, Martin spent almost two decades as a management consultant at McKinsey Company helping clients transform operational performance using technology across a variety of sectors. He can be seen by the piano or in front of Python code when he's not building something awesome in Minecraft with his 10-year-old son. Rado Kotorov CIO Information Builders Dr. Rado Kotorov works with both the business intelligence (BI) and the iWay product divisions to provide thought leadership, analyze market and technology trends, develop innovative product roadmaps, and create rich programs to drive adoption of BI, analytic, data integrity, and integration technologies. He strives to make BI and business analytics more accessible, intuitive, and collaborative through the adoption of innovative Web 2.0, advanced visualization, predictive modeling, search, and mobile technologies. Lennart Christennsson Global Leader Asset Performance Management/OEM GE Digital Leading the efforts to connect complex, industrial equipment/processes for OEMs and create business values through the GE Predix solution in the IIoT space such as Asset Performance Management, Field Service Management Edge computing powered by Predix/ServiceMax. Background as development leader/CTO in Automation Control/IT, Telecommunications and Intelligent cameras, last 10 years in sales

Quick wins and dead ends: a collection of customer stories in asset-heavy industries

Is the application of machine learning to Predictive Maintenance different to what we initially thought? By reviewing real world successes and failures across diverse industrial applications, this talk will challenge current formulations, priorities and perceptions of value in Predictive Maintenance today. Learning points: - Showcase of real world use cases in asset-heavy industries - Perspective on the feasibility and value of different Predictive Maintenance and related use cases - How to take data science beyond prototypes and scale across the industrial organization Jake Bouma Data Scientist Arundo Analytics Jake is a data scientist at Arundo Analytics based in the Oslo office, where his recent focus has been on anomaly detection and machine learning diagnostics for Oil Gas. Prior to joining Arundo, Jake has several years' experience in the telecommunication and railway transport sectors in South Africa. Jake holds a masters degree in nuclear physics from Katholieke Universiteit Leuven in Belgium and since academia has been feeding a passion for putting big data and data science into operation in a way that fundamentally changes organizations for the better.

Short Round Table discussions - How to start with Data Driven approach to Maintenance
Coffee and networking - Peer-to-Peer meetings
Providing Assurance on Diagnostics and Prognostics Technology

The adoption of diagnostics and prognostics technology is accelerating in different industries. Fundamentally diagnostics is a “health meter” describing the state of the fault, degradation, failure modes in question. Given the deployment of the technology in industrial and safety critical applications there is a need to develop legible approach proving the effectiveness of the technology Learning points: - Diagnostics and prognostic technologies are enablers of availability and safety - Diagnostics acts as a “failure meter” employing sophisticated analytical techniques based on sensors and other information - In reviewing diagnostics and prognostics, there is a need to decompose the architecture into different functional blocks - A robust assurance framework will be necessary to provide comprehensible metrics that allow confidence and wider acceptance of the technology Joseph Morelos Strategic Market Manager, Technology Innovation Lloyd’s Register Joseph is a Technology Market Manager with Lloyd’s Register. Joseph has 15 years of engineering experience across the marine industry covering design, testing and review of engineering systems across different asset types from cruise ships to naval assets. Prior to joining the innovation team he dealt with cryogenic engineering, reviewing LNG applications including monitoring technologies

Adding RPA to your mix of models

Maintenance is a process of physical and digital events. Being able to predict the optimal timing is a leap in cost reduction - but only when you can perform the required sequence of events. Learn how Ørsted is adding Robotics Process Automation and how to increase your chance of a successful implementation. Learning points: - Think big. Have a clear vision of where you wish to go - Start small and iterate often. Try it out and embrace your mistakes - They are your best source for learning - Design for success. Gain support by augmenting human capabilities rather than "compete to replace" Peter Loof Helth Head of the Robotics Excellence Centre Ørsted Peter Loof Helth is Head of Robotics Excellence Centre at Ørsted with responsibility for driving the strategic development and implementation of robotics. Over the past 12 months, Peter and his team has been successful in creating a stable, secure and scalable RPA architecture that mixes well with cognitive technologies. After spending more than 15 years in the energy industry, Peter has a deep insight in the complexities of an industry in fundamental change, and the curiosity and drive to identify opportunities and change things to the better through new methods. Peter and his team has delivered excellent results on a strategic and operational level in several areas including operations research, game theory, transfer pricing and real option valuation. An example of this, is the successful Gas release swap auction concept in 2006-2013. In addition to his extensive experience in mathematics and financial analysis Peter is former Captain in the Danish army reserves. Peter holds a M.Sc. in Operations Research from the University of Aarhus, Denmark.

The Road to Zero Unplanned Downtime

Keeping your assets running is mission critical for your reputation, customer satisfaction and for keeping costs low, but what is the true cost of unplanned downtime? In 2017, ServiceMax from GE Digital commissioned independent research firm Vanson Bourne to uncover the impact unplanned downtime can have on companies and field service management teams as well as how technology is helping companies to manage and maintain assets to prevent outage failures. In this session Kieran Notter, Director of Global Customer Transformation will discuss the results from the ground-breaking research and provide examples of ServiceMax customers who are leading the way when it comes to predictive and preventive maintenance of mission critical assets. Is achieving zero unplanned downtime your number one priority? Join this session to discover why it should be. Kieran Notter Director of Global Customer Transformation ServiceMax Kieran is acknowledged as a service industry domain expert with 30 years’ experience. He specializes in field service revenue and working capital improvements, with a particular passion for supply chain operations. He is highly effective at partnering with customers to deliver tangible, practical results across their service operations. Having previously worked for companies including Kodak, Bell Howell and most recently Pitney Bowes he understands the importance of a logical approach that is supported by real time analytics. His considerable experience in implementing and using systems such as SAP, Servigistics(PTC), Oracle (Siebel), Salesforce ServiceMax enables him to recognise a client’s challenges and facilitate solutions that lead to sustainable growth. His recent consultancy engagements have delivered improvements such as reducing field service Inventory levels by 45% whilst maintaining a higher First Time Fix rate.

Networking Lunch
Condition based maintenance in the manufacturing industry

Presenting frameworks and guidelines to support the development and implementation of condition based maintenance in manufacturing companies, exemplifying practical case studies such as vibration analysis of machine tool spindle units. Learning points: - The need for Condition Based Maintenance (CBM) - Factors to evaluate CBM cost effectiveness - A process of CBM implementation - CBM of machine tools, focusing on the use of vibration monitoring technique to monitor the condition of machine tool spindle units Ali Rastegari Maintenance developer Volvo GTO Ali Rastegari is employed as a maintenance developer at Volvo Group Trucks Operation (GTO). He has been awarded the PhD degree in Innovation and Design from Mälardalen University in 1 December 2017. He has been an industrial PhD student at Mälardalen University and part of the INNOFACTURE Research School since September 2012. He has also been employed as a maintenance engineer at Volvo Group Trucks Operation. The area of his research has been relied on development and implementation of condition based maintenance in the manufacturing industry. The results of his studies are published in the form of journal and conference papers. Ali received his M.Sc. from Mälardalen University in the area of Product and Process Development – Production and Logistics and his B.Sc. from Tehran Azad University in Mechanical Engineering. His background includes work as a mechanical and maintenance engineer in manufacturing industries.

Intelligent Service

Anders Paulsen Head of Research Technology Intelligent Asset Management Rolls-Royce

Challenges in modeling elevators for predictive maintenance

I present the unique challenge that the variety of different elevator makes, models, and types present to a company that provides predictive maintenance to all of them. Learning points: - Predictive maintenance of assets that vary a lot in their behavior presents an interesting challenge - Asset behavior can be homogenized by a careful feature selection Matti Laakso Condition Monitoring Expert KONE Corporation Matti Laakso joined KONE in 2014. From the start he has been part of a project to utilize sensor data in the predictive maintenance of elevators. His expertise is in the overall sensor data pipeline from physical sensors and edge processing of sensor data to the generation of actionable insights. Matti got his PhD in engineering physics in 2012 from Aalto University, Finland, and worked as a university research fellow in RWTH Aachen, Germany. He has written more than ten scientific publications.

Predictive maintenance use cases from the Energy sector

In this session we’ll discuss some use cases from an energy company that is taking advantage of predictive analytics to make the world greener. Key takeaways: Align with the business strategy Build a solid data backbone Formalize the process Umid Akhmedov Head of Advanced Analytics Ørsted A/S Umid Akhmedov holds a MSc degree in Finance from Aarhus University. He comes with extensive experience of building data driven solutions from financial and energy sectors. His team of data engineers and data scientists is responsible for supporting Ørsted’s initiatives in extracting value form data assets.

Coffee and networking - Peer-to-Peer meetings
Automation of maintenance prediction for rotating assets using infrequent vibration measurements

Combient is a joint venture with a mission to accelerate digital transformation within the companies participating in our federated collaboration platform, with companies representing some of the largest traditional enterprises in Sweden and Finland. A significant part of this work has involved solving advanced analytics projects related to predictive maintenance. In this session, we will share challenges, methods and preliminary results from an ongoing project that Combient is carrying out together with SKF. We will describe a machine learning approach for determining the health state of rotating systems. The results are based on vibration data collected from 30,000 machines since 2001. Learning points: - Key challenges with building models for real-world data - What can different industries learn from each other? Rerngvit Yanggratok Data scientist Combient AB Rerngvit Yanggratoke is currently working as a data scientist at Combient (, a joint venture owned by several global enterprises from Sweden and Finland, e.g., SAAB, Electrolux, LKAB, SKF, and Ericsson. His educational background is a Ph.D. degree in the area of applying data science for optimizing operations and management in data centers from KTH Royal Institute of Technology in Stockholm, Sweden. He has published over 20 scientific articles in international peer- reviewed journals and conferences. He received his MSc in Security and Mobile Computing from Aalto University, Finland and KTH, Sweden. He received his BSc degree in Computer Engineering from Chulalongkorn University, Bangkok, Thailand. His current interests are industrial advanced analytics, deep learning for natural language processing, and management of large distributed systems. Renato Silva Neves Manager Advanced Analytics and Visualization SKF Sverige AB Renato works with asset monitoring services, industrial maintenance and reliability in high sized companies for more than 14 years. He has a degree in Mechanical engineering and MBA in project management, solid experience in managing projects and people, digitalization and analytics solutions, maintenance services, fluency in the English language, intermediate level in Spanish and a Green Belt Lean Six Sigma certification. Currently an Engineering Coordinator at SKF Brazil, responsible for the management of four different areas and 22 collaborators.

The algorithm is never the problem -yet!

This talk is a short story about how ABB went from selling products to collaborating services. The business model is the key to success. In this talk I will explain how a Collaborative journey in digital transformation could eventually lead us so far that we start questioning our algorithms. Key Takeways: • Give an example of how advanced analytics most successfully can be delivered • What impact does organizational structures have on our development? • What can I do as a manager to enable a digital transformation? Mikael Miglis Collaborative Operations Manager Product Manager - Advanced Series ABB I have been working with Service Sales for the last 8 years, including front end sales, Account Management, Product development and now as operations manager for the center from where we deliver our digital solutions. I am an engineer (M.Sc. Mechanical Engineering) but with a strong focus on business. My biggest interest right now is in the development of business models for digital solutions.

SJ goes Digital: The journey towards predictive maintenance

How to transform a company from mainly doing corrective and preventive maintenance, towards predictive maintenance. With different conditions applying to the assets and limited influence over infrastructure. Learning points: - Cooperation with external organizations - Use of remote diagnostics - Using IoT for PdM purposes Andreas Stjernudde Project manager SJ AB Andreas Stjernudde has been a maintenance analyst at SJ since the end of 2016 with the object of optimizing maintenance plans and increasing reliability. As of 2017 Andreas was assigned as project manager for the digitalization of SJ’s rolling stock, which is a part of the SJ Digital program. Andreas responsibilities as project manager are to coordinate different digitalization related projects to work towards a common goal. Andreas has a master’s degree in Industrial Management and has during his career managed projects, primarily related to transformation and organization.

Chairman´s Closing Remarks
Networking cocktail
IIoT for Process Control (practical talk on what to measure, who to report it to)

Process Control Systems are uniquely positioned as one of the first industrial systems that can leverage the IIoT. Control systems are, by their very nature, already gathering and historizing massive quantities of data in real-time. Further, these systems typically have connectivity to office networks, intranet, and, in some cases, to the internet. Much of the talk about IIoT has been equipment-related, with monitoring of machines, turbines, compressors, and other large equipment. This paper addresses the routine monitoring of industrial control systems themselves. This includes monitoring of the performance of not only basic equipment, such as instrumentation and valves, but also monitoring of control loops, control strategies, and process results. This paper will show practical examples of using IIoT principles to monitor thousands of control loops remotely. The paper will cover practical issues of IIoT, such as data access, security, filtering, and especially how to layer “knowledge filters” to provide intelligent, targeted information to many different types of users. Examples will also show how the resulting reports and analytics can be used to drive business results. George Buckbee Head of Performance Solutions Mesto George Buckbee, P.E. is an ISA Fellow, author of several process control books, and is currently Head of Performance Solutions, featuring Expertune family of products and services, at Metso. An experienced instructor, George has over 25 years of practical experience improving process performance in a wide array of process industries, including Oil Gas, Pulp Paper, Pharmaceuticals, and Consumer Products. George holds a B.S. in Chemical Engineering from Washington University, and an M.S. in Chemical Engineering from the University of California, Santa Barbara.

Edge Analytics Data Lineage

Edge computing is the hot topic in the IIOT space at the moment and daily we are seeing exciting new examples of the use of edge in this space. Trusting where your sensor data is coming from and how it's been manipulated along the way is something that's still a challenge. Join me as I talk through technical examples of how this can be solved giving subscribers to your data clear transparency of where it has come from and how it's been manipulated along the way. Learning points: - Transparent data lineage from the edge - Examples of edge applications - Machine learning models on the edge - Blockchain technology on the edge Marty Cochrane VP Solution Architecture EMEA Arundo Analytics Marty Cochrane is a software developer that specialises in developing software for the power industry. Working on the ground in 1960's coal fired power stations in Ireland all the way to the top of wind turbines on islands in the north of Norway. With a mechanical engineering background Marty strives to write software and control systems that optimises the running of any asset with the smart use of sensors in any heavy asset industry. Today in Arundo he is the director of Solution Architecture and loves getting his hands dirty with control systems and smart analytics on the edge.

Industrial IOT - maintaining the machines

The 4th Industrial revolutions is all about data – using data as an asset and managing assets with data. The manufacturing sector is significantly impacted by this trend as most machines and processes embed sensors that collect vast amounts of data. Companies that leverage this data to optimize performance, maintain machines and even extend the lifecycle of machines reap significant benefits. This has lead to the emergence of new business models such as PAS (products as a service). In this presentation you will learn how to leverage data management and BI to get more value from your equipment. Rado Kotorov CIO Information Builders Dr. Rado Kotorov works with both the business intelligence (BI) and the iWay product divisions to provide thought leadership, analyze market and technology trends, develop innovative product roadmaps, and create rich programs to drive adoption of BI, analytic, data integrity, and integration technologies. He strives to make BI and business analytics more accessible, intuitive, and collaborative through the adoption of innovative Web 2.0, advanced visualization, predictive modeling, search, and mobile technologies.

Networking Lunch
Clear expectations about data quality – the foundation for measuring and managing data

Crappy data is hindering swift delivery of analytics insight and we spend too much time cleaning up. Let’s start measure and manage. Learning points: - How data quality affect feasibility of a great opportunity - A practical way to measure data quality in three dimensions - Producing data is harder than consuming data - How to help the data producer become better Christian Rasmussen Senior Manager, Data Analytics Grundfos Christian has worked with technology development, technology management and frontend innovation for the past 18 years. In 2017 he took up the challenge to build a Data Analytics capability for new digital offerings in Grundfos.I have always been a strong believer of more impact through cross functional collaboration. This is definitely the case working with data. Says Christian Christian holds a MSc from the Technical University of Denmark. Signe Horn Thomsen Data Analyst Grundfos Signe holds a Master's degree in IT, Communication and Organization from Aarhus University. In February 2018 she joined the Data Analytics team in Grundfos and is now working as a Data Analyst with focus on data quality. Signe is developing an assessment method for measuring the quality of data."The goal with this method is to ensure good data quality and thereby deliver valuable and trustworthy data analytics" says Signe.

Implementation of IoT and Leveraging Machine Learning for data cleansing

Session Description: As Vestas begins the journey towards Industry 4.0 new challenges and obstacles arise. This session will explore how Vestas is beginning to use machine learning to overcome some of these challenges with legacy systems, poor master data, and the difficultly with digitalizing unstructured documentation. In addition, the session will cover what activities are currently underway at Vestas to ensure the successful implementation of the IoT project roadmap and our vision for the future. Key Takeaways: - Machine learning is a bedrock of data quality in the future - Unstructured documentation comprehension is a key element in building a digital twin - A strong data foundation will ensure harvest of full value from IoT Implementation Mark Jaxion IoT Lead and Industrialization 4.0 Vestas Mark Jaxion is a Senior Specialist at Vestas Wind Systems. He is responsible for the IoT and Industry 4.0 roadmap for Industrialization within the Technology and Development area within Vestas, and has over 12 years working on deployment, implementation, and optimizations of ERP and PLM systems. Previously, Mark has worked on modelling and optimize supply chain, production operations and inventory planning.

Innovation in service and maintenance industry using predictive analytics: Practical experience of a service and maintenance partner

Karsten Moholt is one of the largest workshops in the Nordics for service, maintenance, condition monitoring and lifetime extension of electromechanical machines. In this session, Ashutosh Kumar will talk about how Karsten Moholt is using data and predictive analytics in the era of digitalization to provide predictive maintenance (PdM) solutions. He will discuss about the changing maintenance strategies, their effects in the maintenance industry, new challenges and how Karsten Moholt is adapting with the new business needs. Key takeaways: - Innovation and Digitalisation enabling PdM in maintenance industry - Traditional Condition monitoring and Predictive maintenance: Advantages and Challenges in both approaches - How Karsten Moholt has always adapted with new business needs and new technologies. - Case study of PdM in asset heavy industry Ashutosh Kumar Project Manager Karsten Moholt A/S Ashutosh is a Project manager for predictive maintenance (PdM) solutions at Karsten Moholt AS since August 2016. Currently he is leading pilot projects in PdM and innovative sensor technologies. He holds a Masters degree in RAMS (Reliability, Maintainability, Availability and Safety) from NTNU, Trondheim and has almost five years of industry experience in reliability and maintenance solutions and process automation in asset heavy industries. Prior to NTNU, he has worked in ABB India and Norway with Engineering and Commissioning of Industrial Automation Systems with clients namely Statoil and TATA Steel.

Supporting Digital Transformation Through Data and Analytics

Lessons learned from establishing Data Analytics initiative in Danfoss leveraging advanced analytics techniques as well data mining and visualization for optimization and innovation. Learning points: - Meeting your stakeholders at eye level: Power of the 3 C's Concepts, Capabilities, Culture - Fail Fast, be flexible and iterate - Presentation of use cases with applied data analytics and mining techniques for both internal and external stakeholders Bjarke Osmundsen Data Analyst Danfoss Is currently driving the development and consolidation of data analytics projects in Danfoss and have more than five years of experience with applying data mining and visual analytics for innovation and optimization. The ability to understand business context and user perspectives has been key in creating value with data analytics in Danfoss. Bjarke is therefore driving business development with a focus on delivering flexible solutions that unlock data-driven insights.

Coffee and networking - Peer-to-Peer meetings
Reverse Engineering Approach for System Condition Monitoring under Big Data and Advanced Data Analytics

A novel mathematical framework to support condition monitoring and condition based maintenance is presented in this study. The framework consists of a data flow path, i.e. from Industrial IoT (i.e. with Big Data) to advanced data analytics with digital models and that can be a part of industrial data handling processes. The digital models are derived from ship performance and navigation data sets and a combination of such models facilitates towards proposed data analytics. Since the respective data sets are used to derive these analytics, that can be a good representation of the respective systems under different modeling levels. Hence, this mathematical framework is also categorized as a reverse engineering approach. Furthermore, a data anomaly detection and recover procedure associated with the same framework to improve the respective data quality is also described in this study. Learning points: - Utilization in big data sets for condition monitoring and condition based maintenance - Reverse engineering of systems up to component levels from big data - Advanced data analytics with digital models for conditions monitoring - Data anomaly detection and recovery from data analytics - Visual analytics towards system health conditions Lokukaluge Prasad Perera Associate Professor UiT The Arctic University of Norway L. P. Perera received the BSc (1999) and MSc (2001) degrees in Mechanical Engineering and Systems Controls from the Oklahoma State University, USA and the PhD (2012) degree in Naval Architecture and Marine Engineering from the Technical University of Lisbon, Portugal. Currently, he is an Associate Professor at the Department of Engineering and Safety, UiT The Arctic University of Norway, Norway. His research experience includes the SINTEF Ocean (former MARINTEK) (2012-2017), Norway, Centre for Marine Technology and Engineering (2008-2012), Portugal and the Advanced Technology Research Center (1998-2001), USA. His academic experience includes the Naval Maritime Academy (2005-2008), Sri Lanka and the Ocean University of Sri Lanka (2003-2005), Sri Lanka. Furthermore, Dr. Perera was a visiting lecture (2001-2005) for several academic institutes in Sri Lanka: University of Ruhuna, University of Moratuwa, Open University of Sri Lanka, Colombo International Nautical Engineering College. His industrial experience includes Wartsila Finland, Finland (2012-2014). Dr. Perera's research interests include Maritime and Offshore Systems Controls, Instrumentation, Data Analytics, Machine Learning Artificial Intelligence, Autonomous Navigation, Intelligent Guidance Decision Support, Condition Monitoring Condition based Maintenance, Energy Efficiency Emission Control.

DNVGL Predictive Maintenance Examples

Presenting the importance of data management and data quality in order to capture the right data, to adapt (transform and integrate) data and use it for advanced predictive maintenance analytics. Learning points: - Do we underestimate the need for data management and data quality? - Profiling and Rules Libraries - What is needed for continuous monitoring? - Presenting some DNVGL and Customer examples from predictive maintenance Jarl S. Magnusson Principal Consultant - Information Risk Management Data Management Competency Centre (DMCC) DNV GL

Networking Lunch

Time is precious. Register for one of the leading and most data driven event on maintenance in 2018. Get insight from some of the leading innovators in the area, and meet qualified peers to share knowledge, experience, and ideas.

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A Presenter is a class that contains the logic that is needed to generate your view (or views). When the controller is done with your user input and is done with whatever actions it needed to take, it turns execution over to the Presenter to retrieve and process whatever data is needed for the view. A Presenter shouldn't do any data manipulation but can contain database calls and any other retrieval or preparation operations needed to generate the View's data.

Presenters are optional. If you don't need them, you can use Views directly, and keep the pre-processing logic in your controller.

First we'll create an empty Presenter class in :

Then you create the view that is associated with the presenter in :

On view names A Presenter and its view are by default expected to share the same name. Thus a Presenter expects the view to be in . And underscores work here the same as with classes, which means that the view for is expected to be in . This default can be overwritten by setting a non-static property in your Presenter with the View name (without its suffix), or passing a custom View name when forging the Presenter.

On view names

And last we'll create the Presenter from the controller:

Now we have everything setup; however, there is still no data passed to the view. It still needs to get a string and array passed to it. We do this by adding a method to the Presenter which will assign this data:

And you're done.

In your code, Views and Presenters are interchangeable. You can return Presenters from your controller actions, you can set a Presenter as a Theme partial, or assign it to a section of your page template. The basic API of the Presenter is compatible with the View. This makes it easy to swap a View for a Presenter in your code without having to do a major code overhaul.

To pass a View specific function from your Presenter to your View, you use an anonymous function or Closures :

Closures are also treated like variables when it comes to filtering. If you have a closure that returns a value, use the method, so the value will be encoded according to your filtering setting. If you have a closure that will be used as a modifier, like in the example above, or you have a closure that returns a value that should not be encoded, use the method, or pass as the third parameter of .

For legacy applications that expect a closure to never be filtered, edit your applications config.php configuration file, add the following key:


It works the same as with View. This means that anything set on the Presenter will be output encoded as long as you don't switch that off. You can use the same method on the Presenter as you'd use on the View directly. More on this in the Security section of View .

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