Taking Tacit Knowledge into Account by Using Problem Cases as Guidelines in HMI Development

IMPROVE is aiming for developing a more efficient way of providing plant field maintenance in highly complex industrial facilities. Therefore, knowledge based models and devices for data mining and processing are to be created in order to support the facility’s maintenance operators who keep the system running in a good condition.

My task in this context contained two challenges. Firstly, I generally had to observe and analyse the socio-technical development of this new maintenance system ethnographically by using ethnomethodological field observation, document or rather code/model analysis, and potentially also interviews. Thereby, I have been investigating the organizational, social, cultural, and engineering processes involved in this project. Secondly, I have supported the part of IMPROVE which is engaged with the development of the HMI (human machine interface).

Interestingly, the development of the HMI requires models and specific data in advance being conceivable by common human cognition capacities. This is a great opportunity to not only observe ethnographically the discursive production of what good data and models are but also to get the chance to have some especially emic insight. Thus, the HMI development can be constructively reflected without neglecting its afore prerequisites of modeling and simulation so that the HMI design will eventually benefit.

Project leader(s):
TUM Maschinenwesen, PD Dr. Jan-Hendrik Passoth, Prof. Dr. Sabine Maasen

Period:
01.10.2016 – 31.08.2019

Project type:
Array

Funding institution:
H2020