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Tag: Smart Maintenance

ONLINE – Expert Group Meeting – Smart Maintenance

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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.

Program

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”

Registration

Please register to the meeting using the form below. For questions you are welcome to contact us directly through lilach.gorenhuber@zhaw.ch or palm@zhaw.ch

Looking forward to meeting you,
Maik, Thomas, Manuel and Lilach

ONLINE – Expert Group Meeting – Smart Maintenance

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Maik Hadorn, Roche Diagnostics

Addressing customer needs in a smart way – an end-to-end case study for Condition-Based Maintenance of Laboratory Equipment

Ensuring reliability while minimizing downtime of laboratory analyzers is not just a convenience – it is critical to maximizing a lab’s success and upholding the standard of patient care to which labs aspire. Joining forces with Roche Service Representatives allowed the Roche data science team to empirically test and refine algorithms that prevent replacing a crucial part of the instrument when it is not broken. With this unnecessary instrument downtime is minimized because troubleshooting is eased while reliability is ensured. A staggered roll-out of the algorithm will not only allow the users to provide continuous feedback but will also allow for customized communication, training, and organizational change management creating enthusiasm and buy-in of the people.

Program

14:30 Introduction round
14:45 Talk: Condition Based Maintenance of Laboratory Equipment by Maik Hadorn, Roche Diagnostics

To receive a link to the meeting, please register to the meeting using the form below. For questions you are welcome to contact us directly at lilach.gorenhuber@zhaw.ch or palm@zhaw.ch.

Looking forward to meeting you,
Lilach Goren Huber and Thomas Palmé

APMS Conference

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APMS 2021 Conference Special Session/Track on Integrated Manufacturing & Service Operations Management for Product- Service Systems

Over the past 12-months there has been an acceleration of the use of digital within the PSS context as remote forms of support have been developed and deployed in the field allowing new collaborative approaches that support value cocreation. This shows new models of integration of manufacturing operations and service management initiatives, enabled by digital that allow these formats to be delivered. Specifically, the implementation of new formats of digitally-enabled value propositions supports innovative forms of asset management, operations and maintenance during the product-service lifecycle. This leads companies to capture new aspects into their product-service system and allows conversions, modifications and upgrades to extend the asset’s overall useful life with both a sustainable and circular view. Different analytical tools can enable new value propositions as well as improving service experience and operational efficiencies. Moreover, the new digitalized solutions help create the ideal conditions to allow the integration of third-party services; namely, system installers, maintenance-focused supplier or existing business partners (e.g. agents or distributors), who may provide service on the operational assets in place of either the customer or the manufacturer.

With the advent of new forms of integrated value propositions focusing on product-service life-cycle, process support services, asset efficiency services or processes delegation services, may thus mean that the traditional PSS models (e.g., Tukker) might no longer be valid in their existing forms. However, additional studies are needed to find new integration schemes, that support the development of emerging business modes, their value propositions, as well as to identify the best underlying technologies. This special session aims to explore different forms of integration within a product-service system context. The objectives support the aim are to:

  • develop new frameworks to describe integrated value proposition within a PSS context;
  • understand and characterize different digitally-enabled value propositions;
  • identify how new technologies enable new value propositions;
  • describe how to better integrate circular economy aspects into the existing value propositions and business models.
    Qualitative and quantitative papers are requested for this session rather than literature reviews.

Implementing Smart Maintenance in Industry: Innovation Projects Flash Talks

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Data-driven methods for predictive maintenance are gaining attention both in applied research and in industry. However, often there is a substantial gap between state-of-the-art methods in research and the actual status of implementation of these methods in industrial systems and infrastructures.
Some of the questions that are often left open are:

  • How can a company identify a business opportunity that can be addressed by data-driven innovations?
  • Which data would be required for the successful implementation of such innovation?
  • What tailor-made tools would be required for the data-acquisition, storage and processing?
  • How would we integrate the innovation in our existing industrial framework and make sure it is efficiently adopted by the operational teams?

In this meeting we focus our attention on one of the primary tools that allow Swiss companies to bridge the gap between research and practice: Innosuisse funding for R&D collaboration projects between applied research institutes and industry companies. We will briefly explain the idea and formal procedure of an Innosuisse funding grant for such projects and then introduce several concrete examples for past successful projects in the field of smart maintenance. Each project presenter will have 10 minutes to summarize the main goal and implemented outcomes. We will conclude with a session of questions and answers to the presenters.

In this way we hope to give our members a clearer perspective of the possibilities that the Swiss Innovation Agency allows in order to transfer knowledge from applied research into industry, with focus on data-driven maintenance applications for industrial assets and infrastructures.

We strongly encourage you to participate in the active discussion, and in addition to forward this invitation to people in your company that might play a relevant role in decision processes regarding innovation.

Program
12:00-12:10 pm Introduction: Innosuisse projects as a business opportunity.
12:10-13:00 pm 5 Flash Talks: Innovation Projects in Smart Maintenance (various speakers from research and industry).
13:00-13:10 pm Databooster industry 4.0: boosting innovation initiatives.
13:10-13:30 pm Q&A session.

Registration
Registered participants will receive a link to the meeting prior to the event. Please register through the form at the bottom of this page. For questions you are welcome to contact us directly through lilach.gorenhuber@zhaw.ch or thomas.palme@ge.com.

Best Regards

Thomas Palmé and Lilach Goren Huber

Expert Group Meeting – Predictive Maintenance

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On behalf of the industrial and academic leaders of our group, I am pleased to invite you to our first meeting of this year. The topic is deep learning for predictive maintenance. We will have Distran presenting their handheld sensor that overlays video and acoustic information, and ZHAW Prof. Dr. Thilo Stadelmann who will talk about deep learning use cases in the industry.

The date is May 10th, starting at 14hrs. The location of the meeting is TL 202 at ZHAW School of Engineering, Technikumstrasse 9, Winterthur.

Find the invitation attached as a pdf, and please let us know that you are coming to estimate the amount of food and drinks.

 

Expert Group – Smart Maintenance Event

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Date: 21.01.2020 3:30pm

Our keynote presentation focusses on “Condition-based Maintenance and Resistance to Change” at the German engineering company Heidelberger Druckmaschinen. Hanna Sotnikova of USU project partners will present the solutions and experiences made as part of this comprehensive digitalization and smart maintenance project. We also look forward to Work in Progress discussed by Lee Sacco of Oracle, followed by an Apero afterwards.

Program:

3:30 pm Welcome & Introduction

3:45 pm “Heidelberger Druckmaschinen: Condition-based Maintenance and Resistance to Change”, presented by Hanna Sotnikova (USU)

4:30 pm Work-in-Progress, presented by Lee Sacco (Oracle)

4:50 pm News & Outlook

Followed by Snacks and Drinks at around 5 pm

Location: ZHAW Zurich, Lagerstrasse 45, Conference room E0.11 on ground floor

Registration: Please register by 13 Jan by email to Angela Meyer

Meet-an-Expert: As usual we are offering “Meet-an-Expert” one-on-one sessions starting 2:30pm as a service and an opportunity to discuss your Smart Maintenance topics and get feedback from technical experts. To book please email Angela Meyer.

Expert Group Meeting – Smart Maintenance

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The meeting planned on April 2 was cancelled due to the Covid-19 outbreak and we have now arranged an “online” meeting as a substitute until we can arrange meetings as usual. We hope that time will come soon.

And, our sincere apologies for the short notice term for this meeting, it was a result of availability of presenters and time zone alignment between Switzerland and Singapore.

Program:

10.00-10.45 “Vibration-based fault detection in CNC machines” presented by Dr. Farzam Farbiz

10.45-11.30 “Digital services in the mobility market: how to optimize value with co-creation” presented by Ruben Lorenzo

For registration, please send a message to Thomas.palme@ge.com or to merg@zhaw.ch and we will send you the MS TEAMS link to join the meeting.

Dr. Farzam Farbiz is currently a senior scientist with the Department of Computing & Intelligence, Institute of High Performance Computing (IHPC), Agency for Science, Technology, and Research (A*STAR), Singapore (https://www.a-star.edu.sg/ihpc). His current research interest is on using physics-based AI to improve the performance of data-driven based machine learning models, and how these new models can be applied for and be benefited by manufacturing applications.

Ruben André Lorenzo is Head of Sales at Siemens Mobility Services. He has been with Siemens since 2009 and is responsible for developing innovative services with a focus on the digital service business.

These two presentations touch base on both “Analytics” and “Business case” areas of Smart Maintenance, hence this will be a very interesting meeting!

Expert Group Event – Smart Maintenance Webinar

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Webinar 28.08.2020

Program:

14.00 Welcome

14.10 Florian Pitschi, Swisscom: Condition Monitoring at Meier Tobler – An IoT journey of a Swiss company

Meier Tobler is a Swiss company selling heating and cooling systems. Together with Swisscom, they started their IoT journey more than three years ago. Florian Pitschi will explain why they started the journey, where Meier Tobler are now, showing different components of the employed solution in the different IoT layers  from PLC data extraction, device management up to condition monitoring, and some possible next steps.

Florian Pitschi holds a Master’s degree in information technology from the University of Ulm and accomplished a doctorate in computational biology in Shanghai on a Max Planck scholarship. Florian has been working as a data solution architect at Swisscom since 2015.

Florian Pitschi holds a Master’s degree in information technology from the University of Ulm and accomplished a doctorate in computational biology in Shanghai on a Max Planck scholarship. Florian has been working as a data solution architect at Swisscom since 2015.

14.35 Jianwen Meng, Université Paris-Sud: Lithium-ion battery monitoring and fault diagnosis for embedded application

Thanks to their high energy/power density and extended life cycle, lithium-ion batteries (LIBs) are currently the state-of-the-art power sources for electrified powertrain systems. Safety operation of LIBs is of vital importance for the development of electric vehicles (EVs). However, reliability and safety of electrified vehicle can be compromised due to overcharge (OC), overdischarge (OD),  internal short circuit (ISC) or external short circuit (ESC) of the battery. They can cause irreversible battery damages, or even lead to battery thermal runaway (TR), which is a catastrophic failure of on-board batteries. Therefore, fault diagnosis and mitigation strategies for EVs’ battery are critical functions to prevent TR.To this end, this presentation will focus on battery incipient short-circuit (SC) diagnosis. Because SC is an important stage before TR regardless of different kinds of battery abuse conditions. Furthermore, during the incubation period of SC, no electrical or thermal thresholds are exceeded for a long time before TR. Therefore, detection of incipient SC at its earliest stage is meaningful as it can prevent battery failure. However, incipient SC fault signature is weak as it may look like healthy operating conditions and fault features may be concealed in environmental nuisances. Hence its detection is challenging.

Jianwen Meng received the Master’s degree in Electrical engineering from University of Nantes, Nantes, France, in 2017. He is currently working toward the Ph.D. degree with the ESTACA Engineering School, Saint-Quentin-en-Yvelines, France; and also with GeePs | Group of Electrical Engineering – Paris, CNRS, CentraleSupélec, Université Paris-Saclay, Sorbonne Université, France with Prof. Demba Diallo and Prof. Moussa Boukhnifer. Jianwen’s research interests include fault diagnosis, fault-tolerant control and energy management with the background of electric vehicle.