Smart maintenance will decrease downtime by improving the co-operation between production and maintenance organisations. This is made possible by for example shared methods, tools and a common production and maintenance planning. Since smart maintenance means that decisions are based on data, it also requires shared data and information sources.
We have research projects within all four head principals of Smart Maintenance. You can find publications and information using the links under each project.
DAIMP - Data Analytics in Maintenance Planning
The DAIMP project connects data structures on a machine level to analyses needed on a systems level. Expected results are for example: data and information structures for improved internal and external collaboration, algorithms for predictive and prescriptive analytics, and data–driven criticality analysis to support differentiated maintenance planning.
Smart Maintenance Assessment (SMASh)
The main idea is to develop a Smart Maintenance Assessment (SMA) tool for benchmarking of maintenance organizations within and across companies. Such a tool will help organizations to implement Smart Maintenance through extended collaboration within the maintenance community.
Data-driven disturbance handling (D3H)
The major effect goal of the Data-driven disturbance handling project is to digitalize established disturbance handling tools by making them data-driven and, thus, more effective. Desired effects include reduced disturbance frequency, increased Overall Equipment Effectiveness, and better opportunities for cost-effective automation.
Predictive Maintenance using Advanced Cluster Analysis (PACA)
The PACA project aims to develop PdM algorithms, based on advanced cluster analysis, to increase the precision and make them understandable for decision makers.
Anders Skoogh, research leader. Focus on production system services and maintenance.
Torbjörn Ylipää, lecturer
Maheshwaran Gopalakrishnan, PhD student
Jon Bokrantz, PhD student
Mukund Subramaniyan, PhD student
Camilla Lundgren, PhD student
Ebru Turanoglu-Bekar, post-doc
Adriana Ito, PhD student
Omkar Salunkhe, PhD student