The PIM team is interested in a mutual learning process between "theory" and "practice". To accomplish this goal, we are striving to come up with innovative solutions and to go beyond the state-of-the-art in maintenance management. Hence, the following Topic Areas (TAs) are coming into consideration:

TA-I: Knowledge-Based Maintenance

Methods of Artificial Intelligence (AI), semantic technology, big data analytics and Machine Learning (ML) are employed and adopted to efficiently discover knowledge from heterogeneous data structures and provide informed decision alternatives timely and effectively.

The current focus of research is on:

• Data-Driven and Predictive Maintenance (Maintenance Analytics)
• Artificial Intelligence and Semantic Technology Application in Maintenance
• Maintenance Knowledge Management
• Prescriptive Maintenance Models and Strategies
• Automated Maintenance Decision-Support and Recommendation Systems

TA-II: Human-Centered Cyber Physical Production Systems & Maintenance

Maintenance management principles, models and strategies are reexamined and rethought, based on emerging requirements and challenges in the era of Industry 4.0, towards enhancing the design and realization of smart factories and CPPS.

The current focus of research is on:

• Human-Machine Collaborative Problem-Solving (Human and Machine as a Problem-Solver in Maintenance)
• Human-Machine Reciprocal Learning  (Human and Machine as a Learner in Maintenance)
• Human-Machine Job Knowledge Analysis (Human and Machine Task Allocation and Sharing in Maintenance)

TA-III: Future-oriented Maintenance

Innovative maintenance principles, model and strategies are investigated based on emerging trends and foreseeable industrial challenges such as:

• Digital Transformation in Manufacturing
• Digital Twin for Maintenance
• Digital Assisted Maintenance
• Cyber-Security and Maintenance (e.g. Application of Block Chain Technology in Maintenance)  
• Biological Transformation in Manufacturing
• New Learning and education paradigms, concepts and models for training maintenance human resources

PIM Maintenance Cube Philosophy

The PIM Maintenance Cube combines in its 3 dimensions the 9V of Big Data, the multimodality of data, and the predominant, different maintenance strategies. PIM's goal is to achieve a shift in the mindset of industry partners through this holistic approach and turn AI-based maintenance from a black box to a white box

Partnership with Academia and Industry

We aim at establishing and extending our partnership with national and international academic institutions and industry, based on the above-mentioned TAs. In particular, we are interested to establish joint research projects (funded e.g. by FFG, FWF, and EU), conduct studies and supervise student projects (Diploma or Master Thesis).

The current list of associated scientific partners includes:

• IMW Chair of Human-Machine Interaction (Prof. Dr-Ing. Sebastian Schlund, Topic Area II)
• Fraunhofer Austria, Division of Production Management (Topic Area I and III) and Division of Advanced Industrial Management (Topic Area I)