We would like to invite you to the next Smart Maintenance Expert Group meeting. In this meeting we will host Dr. Gabriel Michau from Stadler Services, who will tell us about
Full Steam Ahead: Unveiling the Future of Railway Rolling Stock Maintenance.
The maintenance regime of rolling stock creates by nature a competition between the reliability of the asset and the availability. Traditional maintenance regimes, mixing preventive and corrective maintenance strategies, look for a cost effective optimum between these metrics and years of optimisations have left little room for further improvements using the traditional optimisation strategies.
To achieve even more efficient maintenance strategies, dynamically scheduled maintenance, based on the real condition of the assets and of its components needs to be introduced, the so-called “Condition Based Maintenance” but this requires the development of three new types of capabilities: collecting and managing the right data, processing this data to assess the health of the monitored components and the ability to change and adapt the maintenance schemes seamlessly. Each aspect brings its own challenges, and many of these challenges are similar to the ones faced by the industry in general. In this presentation, we will draw upon exemplary projects carried out within Stadler Service’s fleet management to delve into the specific challenges encountered and the strategies employed to address them.
Dr. Gabriel Michau leads the New Maintenance Technologies team at Stadler Service AG, where he focuses on pioneering innovative maintenance strategies driven by data insights and cutting-edge technologies within the workshop. Prior to this role, he served as a Senior Scientist at ETH Zurich and at the ZHAW, specializing in the application of Deep Learning to Time Series for unsupervised fault detection and diagnostics in complex industrial systems, with a particular focus on very high frequency data (up to 100s of MHz). There, he led research initiatives aimed at advancing intelligent maintenance practices in collaboration with esteemed industrial partners, including SBB-CFF, Airbus, Stadler AG, GE, Oerlikon-Metco, Bystronic and more.
In 2016, he earned a dual PhD in Physics (Signal Processing over Networks) and Transport Engineering through a joint program between the Queensland University of Technology in Brisbane and ENS de Lyon in France. His research focused on inferring traffic flows in cities using Bluetooth and traffic counts data, and he developed an innovative traffic representation tool with graph theory and advanced convex optimization algorithms. His work earned him the first place in both the National and International Abertis Award in 2016.
The talk will be followed by an active discussion about successes and challenges in implementing and scaling condition-based maintenance in real operational systems.
The Data Innovation Alliance’s second Expert Day in March 2023 was a hub of activity as experts from four key areas – Smart Maintenance, NLP & AI Technology, Spatial Data, and Smart Services – gathered to share their insights and mingle with researchers and industry professionals. The event kicked off with leaders from each Expert Group pre-discussing their plans for 2023, generating a wealth of innovative ideas for joint events and initiatives, and paving the way for exciting collaborations in the (near) future.
But that’s not all! The NLP and Digital Health groups are teaming up to bring you joint events that will revolutionize the way we approach data. And with the next Expert Day set for August 2023, featuring four expert groups once again, get ready for even more ground-breaking discussions and initiatives, organized jointly with other Innovation Boosters. Keep an eye on our events calendar for more information.
While the keynote speech may not have met expectations in terms of insights, it set the stage for what was to come – dynamic discussions and collaborations in the expert group break sessions. To ensure everyone had access to the wealth of information shared, short summaries of the discussions were written by participants in each room.
In short, the second Expert Day was a superb success, bringing together a diverse group of experts to debate their ideas and shape the future of data innovation.
Smart Services for Sustainability – Circular Servitization by Jürg Meierhofer
The Smart Services for Sustainability – Circular Servitization discussion was a dynamic conversation among highly experienced individuals from different industries. They explored how value is created in business ecosystems, focusing on both individual and organizational perspectives.
It was inspiring to have diverse industry representatives in the same room and to create a common understanding. Departing from economic value creation, the group extended its scope to ecological factors. An intense discussion arose about how environmental value can be created without negatively impacting economic value. Statements that economic value creation is still the predominant requirement were made, meaning that in many cases, even a slight reduction of economic value for the sake of ecological value would be treated with suspicion. As sustainability becomes increasingly relevant and regulations loom, the balance between economic and ecological value may shift in the near future.
Overall, the Smart Services for Sustainability – Circular Servitization discussion was thought-provoking and left participants eager to continue exploring the intersection of business and sustainability.
Spatial Data by Reik Leiterer
In a room buzzing with ideas, each data expert chimed into the discussion about the creation of a platform that would benefit cantons, individuals, and service providers. There was a shared understanding that it might not be possible to cater to everyone’s needs and that a simpler visualization and analytics approach may be the way forward. However, some uncertainties still remained, such as identifying where the necessary data is available and how it can be integrated, setting limits, and ensuring that data is not misinterpreted. Despite these challenges, the group remained enthusiastic about the potential benefits of the platform and is looking forward to overcoming these obstacles.
NLP & AI Technology by Lina Scarborough
The group opened the floor with how chatbots are great to answer questions, but what happens when users don’t know where to begin asking questions? This is a common issue in legal situations where the average client may not have the necessary background to understand what information is needed. Retrieval augmented language models like KATIE have emerged as a solution to this problem. These models use grounded reasoning and promote a chain of thought to handle complex queries and create a context for users who may not know what subset of questions to ask.
With the rise of machine-generated text, it’s becoming more difficult to distinguish between human and machine-generated content. While probabilistic token selection and frameworks like SCARECROW can help scrutinize machine-generated text, it can still be difficult, to nigh impossible, to identify. However, ChatGPTZero, an app that uses watermarking to create a statistical fingerprint in the sampling method, claims to be able to detect whether an essay is written by ChatGPT or a human – for instance, ChatGPT generally makes redundancy errors whereas humans make grammatical mistakes. This approach hopes to maintain the integrity of human-generated content in the face of increased machine-generated text.
The discussion then flowed into a lively and engaging presentation on how AI technology can make the tricky SQL “minefield” as easy to navigate as a soccer player scoring a goal – literally, by demonstrating SQL prompts on the soccer World Cup!
Smart Maintenance by Melanie Geiger
The five use case presentations highlighted the versatility of data technology in different applications, showcasing how it can be adapted to meet various needs. With input data ranging from domain knowledge to error log data, these use cases demonstrated how AI models can process and analyze complex data sets to provide valuable insights and decision support.
One of the key themes that emerged was the use of AI for diverse condition-based maintenance, specifically anomaly detection and fault diagnosis. By leveraging ML algorithms, these use cases were able to detect potential issues and predict equipment failures for timely maintenance and preventing downtime.
The highlight of the event was not only the apèro treats, but the opportunity to engage with the 60 participants and learn about their projects, challenges, solutions, and ideas for collaboration. Many attendees seemed to share this sentiment, as numerous participants were still engrossed in conversation at the end of the event, and some discussions had to be continued elsewhere. Those who wish to follow up on these conversations have the option to do so at SDS2023. On a more lowkey note, maybe you wanted to add someone on LinkedIn and send them a message. Here you go, this is your reminder!
Our conclusion of the event: the Alliance has many experts in various subtopics of data-driven value creation, but only together we can move faster.
We invite you to the second iteration of the Expert Day. Join us in an exchange of expertise and find inspiration. These following groups will participate:
Natural Language Processing & Big Data Technologies
Spatial Data Analytics
15:00 – Welcome 15:30 – Keynote by Prof. Pierre Dersin 16:10 – Expert Group Meetings in breakout rooms (see below) 17:40 – Apéro
Data-driven Value Added for Words, Images and Things
Digital transformation is a defining feature of our epoch.
Abundance of data, immense increase in hardware processing capabilities and breakthroughs in analytics algorithms have made practical some of the visions put forward about three quarters of a century ago. The branch of Artificial Intelligence called Machine Learning, and in particular Deep Learning, permeates image processing, natural language processing ( “ words”) and smart maintenance (‘things’), and furthermore enables rich synergies between those three fields, which span a great deal of human activity, with profound potential impacts—some already visible, on industry, science, the arts and social life.
Natural Language Processing & Big Data Technologies
Everyone is talking about ChatGPT these days and some of its output is truly impressive! We will discuss how the most recent wave of text generation algorithms can transform business, science and teaching. The meeting will feature the following expert talks:
Grounded Copywriting with ChatGPT & Co by Michael Wechner (Wyona AG) + Colin Carter (Coop Rechtsschutz) Everyone talks about the pros and cons of ChatGPT, its competitors and how to combine the generated text with grounded knowledge. We will demonstrate how ChatGPT & Co can be applied in insurance and discuss the future of retrieval augmented language models.
Can we Identify Machine-Generated Text? An Overview of Current Approaches by Anastassia Shaitarova (UZH Institute for Computational Linguistics)
The detection of machine-generated text has become increasingly important due to the prevalence of automated content generation and its potential for misuse. In this talk, we will discuss the motivation for automatic detection of generated text. We will present the currently available methods, including feature-based classification as a “first line-of-defense.” We will provide an overview of the detection tools that have been made available so far and discuss their limitations. Finally, we will reflect on some open problems associated with the automatic discrimination of generated texts.
Using AI to Query the Football World Cup Database in Natural Language by Kurt Stockinger (ZHAW Institute for Applied Information Technology)
Football is one of the most popular sports on earth with millions of people watching the FIFA world cup. In this talk, we describe how we built a system to query the world cup database in natural language. We explain how we translate natural language into the database query language SQL using modern transformer architecture. We also demonstrate how we have used large language models such as Open AI’s GPT-3 and Google’s T5 to explain how the system interprets users’ questions.
We are looking forward to exchanging opinions, experiences and questions, and to exploring this exciting field together!
The value of condition monitoring data: 5 use cases.
In this meeting of the Smart Maintenance Expert Group we will hear about successful student projects conducted together with industry partners from various fields. The focus points of the projects are very diverse, ranging from prediction of energy losses, through anomaly detection, fault diagnostics, prediction of the remaining useful life and optimal maintenance scheduling. We will have 5 short pitch presentations, followed by an interactive discussion of future interest topics of our expert group, including active feedback of all participants.
Anomaly Detection in Marine Engines with Convolutional Neural Networks (Company: WinGD)
Aircraft Scheduling Optimization based on Prognostics Degradation Models (Company: Swiss International Airlines)
Modeling Wake Energy Losses in Wind Farms using Graph Neural Networks (Company: Fluence Energy)
Using Error Code Patterns to Predict Service Requests on Production Machines with Machine Learning (Company: Zünd Systemtechnik).
Fault Detection in Solar Power Plants using Physics Informed Deep Learning (Company: Fluence Energy)
Smart services for sustainability – circular servitization
With data-driven services, industrial companies can create quantifiable value for their customers, partners and themselves. At the same time, these services also have the potential for ecological benefits, e.g., through optimized processes in operations or logistics. To make this possible, economic and ecological goals must be captured in a targeted and combined manner when designing the services.
The 1.5-hour workshop will discuss how specific problems from everyday business can be systematically addressed to create relevant added value for business and ecology. Participants will bring their own business issue and leave the workshop with a first approach on how to create economic and environmental value through smart services. The workshop will run through typical phases of a project in a compressed time format to give an impression of what such a project might look like on a larger scale.
Spatial Data Analytics
High-quality spatial data is increasingly available for free use. However, with the large amount of data and the sometimes very specific data types and formats, it is challenging to find the appropriate data sources. In addition, some of the data access platforms are only partially intuitive and can be used without expert knowledge. Accordingly, the question arises whether the full potential of the available data base could not be better exploited if data access and data sharing were simplified. In this co-creation workshop, concepts and approaches will be reflected and discussed with representatives from research and industry as well as from cantonal and federal agencies, with the aim of developing possible approaches for joint implementation.
We would like to invite you to a special “End of the Year” webinar on Friday 16.12.2022 at 14:30. We are delighted to have Dr. Gabriel Michau as our speaker, telling about his research work titled:
Whispering machines: Deep Learning for viable Condition Based Maintenance
Which was recently published in the highly prestigious journal PNAS. The talk description can be found here: Link.
The Speaker: Dr. Gabriel Michau is leading the development of Data-Driven maintenance solutions at Stadler Service AG, piloting end-to-end data-driven projects, from identifying the sensing technology to the optimisation of the existing maintenance strategies. He specialised in the development of innovative deep learning algorithms for the processing of industrial data as Senior Scientist at the ETH Zürich, in the Chair of Intelligent Maintenance Systems. At the ZHAW, he worked on several innovation projects with industrial partners to develop machine learning solutions to specific problems met by the industries. Gabriel holds a joint PhD between the Ecole Normale Supérieure de Lyon, in Physics, specialised in convex optimisation and the Queensland University of Technology in Brisbane, in Traffic Engineering.
Organized by the Smart Services and Smart Maintenance Expert Groups
Showcase Kistler & Digitalization
In this afternoon event, we will introduce the digitalization initiatives of Kistler and then focus on the digital service initiatives with a focus on pilot projects in various fields of advanced services. The event will be rounded off with a presentation of a turn-key solution by Kistler innovation lab and digital hub, followed by a tour around the company and apéro.
We are pleased to invite you to our next meeting of the Smart Maintenance Expert Group. The meeting will take place online.
Thomas Wuhrmann from Kistler Instrumente AG will talk about
Machine Learning based Cause of Failure Analysis in Injection Molding & Metal Machining
In industrial manufacturing processes, the most important tasks are usually concerned with an improvement of the quality outcome, a reduction of waste and an improvement of the process stability. This applies specifically to injection molding and metal machining with a turning lathe. Injection molding is the state-of-the-art process to produce plastic parts in different shapes and sizes. Machining on the turning lathe allows to perform highest precision process steps on expensive raw materials using specialized cutting tools. In both applications, machine learning allows to harness the process information more effectively to improve the yield of in-specification parts and understanding the quality-determining process mechanisms. In this talk Kistler approaches to implement solutions to realize this in both applications will be presented. In both applications, the insights obtained by machine learning, though different in nature, allow a reduction of waste. In both fields, further investigations are aimed to generalize these models and fully integrate them into the corresponding process automation and control.
14:30-14:45 An Introduction Round. 14:45-15:30 ML based Cause of Failure Analysis in Injection Molding & Metal Machining (Thomas Wuhrmann, Kistler Instrumente AG). 15:30-16:30 Networking on “Wonder”.
By Lilach Goren Huber, Thomas Palmé, Manuel Arias Chao (all ZHAW), Maik Hadorn, Roche
Smart Maintenance Expert Group Meeting 20.01.2022
Once more, we met online for an interesting presentation followed by vivid discussions and networking. Yes, online networking!
We started by proudly introducing our new industrial Lead: Dr. Maik Hadorn, International Product Manager, from Roche Diagnostics. Welcome Maik, we are honored to profit from your expertise!
Next, Niels Uitterdijk, the CTO and founder of Amplo exposed us not only to success stories but also to challenges and pitfalls on the way to successful machine-learning-based predictive maintenance. As usual in our EG, this included concrete use case examples, this time from several different application fields.
After an intense Q&A session (we were 29 attendees!) we switched from Zoom to Wonder, where we had the chance to meet and network with group members. Similarly to previous meetings of our EG, this worked out really well!
We look forward to the next meeting – this time, finally, face to face.
We are pleased to invite you to open 2022 with a meeting of the “Smart Maintenance Expert Group”. The meeting will take place online on 20 January 2022 at 14:30.
Niels Uitterdijk from Amplo GmbH will talk about Successes and pitfalls on the road to a generic, operational machine learning platform for service engineers
Industrial machine manufacturers have heavily invested in IoT infrastructures in the last decade, yet tools that extract actionable insights remain illusive. To drastically reduce the maintenance costs and data analysis efforts, Amplo developed a smart maintenance platform which gives service engineers themselves access to state of the art operational machine learning systems that are deployable without any code or machine learning expertise. Currently, the platform provides models that diagnose failures, analyse production quality and monitor machine performance.
The benefits are clear. Tritium avoids hours of data analysis and enjoys automated root cause analysis, allowing them to instantly send out repair orders. Solarmanager is able to notify thousands of homeowners when their PV panels are underperforming. TB Safety now services their filters only when necessary, instead of inspecting them every year.
14:30 – 14:45 An introduction round
14:45 – 15:30 Successes and pitfalls on the road to a generic, operational machine learning platform for service engineers
15:30 – 16:30 Networking on “Wonder”
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