Thesis Work for Robotics R&D – Smart Service Recommendation System Based on (Artificial) Collective Intelligence i Västerås

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Within ABB Robotics R&D System and Test department we are proposing several Master thesis next spring. The departments two focus areas are developing the software test infrastructure, tools and methodologies and also developing our ABB Ability cloud solutions. That latter is the responsibility of our Digital Services team working with the whole chain from e.g. diagnostics, connectivity and web app development.

Smart Service Recommendation System Based on (Artificial) Collective Intelligence.
The goal of condition-based maintenance is to monitor and spot upcoming equipment failure so maintenance can be proactively schedule when it is needed. Identifying anomalous system behavior alone might not always be enough to be able to advise on the correct service or maintenance intervention for industrial equipment such as robots. Hence, a recommendation system such as those often utilized in e-commerce would be a very interesting topic to explore e.g. others who bought this also bought that versus other who have had these system behaviors made these system interventions. The thesis can be conducted by a pair of students with individual sub-focuses and contributions i.e. focus on how to best construct a service & maintenance recommendation engine and how to optimally apply it in service optimization context respectively.

Details:
• Period: January-June 2020
• Number of credits: Master thesis 30p
• Number of students: 1-2 for each Master thesis
• Location: Robotics R&D office at Finnslätten, Västerås

• Understand the Robotics service business and its operations including the existing smart service and maintenance information tools and available data sources.
• Extensive literature study regarding Collective Intelligence, Recommendation Engines and Condition Based Maintenance.
• Prototype a recommendation engine, on top of existing system, advising on service and maintenance activities. Existing data for service and maintenance recommendations may be clustered using unsupervised learning methods and later mapped to robot system signals for proposing the most probable response using supervised learning.
• Evaluate how to best construct and optimally use a recommendation engine in a service optimization context, possibly using artificial intelligence methods.
• Live ABB’s core values of safety and integrity, which means taking responsibility for your own actions while caring for your colleagues and the business.

• Engineering student within Computer Science, Industrial Engineering and Management, Information Technology, Applied Physics, System Engineering or other program with similar profile.
• Comfortable working with systems and programming in a hobby, university or professional context.
• Curious and eager to learn.