TY - GEN
T1 - Lean instrumentation framework for sensor pruning and optimization in condition monitoring
AU - Sanda, M. Aminu
AU - Abrahamsson, L.
AU - Galar, D.
AU - Kumar, U.
AU - Sandin, F.
AU - Delsing, J.
PY - 2011
Y1 - 2011
N2 - This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.
AB - This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.
UR - http://www.scopus.com/inward/record.url?scp=84905734101&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84905734101
SN - 9781618390141
T3 - 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011, CM 2011/MFPT 2011
SP - 202
EP - 215
BT - 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011, CM 2011/MFPT 2011
PB - British Institute of Non-Destructive Testing
T2 - 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011, CM 2011/MFPT 2011
Y2 - 20 June 2011 through 22 June 2011
ER -