<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.</p></abstract>

With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

Institute of Applied Mechanics, Poznań University of Technology, ul. Piotrowo 3, 60-965 Poznań, Poland

International Journal of Applied Mathematics and Computer Science

With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be...

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

Publié en ligne: 30 déc. 2008

Pages: 569 - 580

DOI: https://doi.org/10.2478/v10006-008-0050-7

Mots clés
machine wear, multidimensional observation, vibration, SVD decomposition, fault space, observation space, symptom limit value, forecasting, grey system theory

This content is open access.

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

Publié en ligne: 30 déc. 2008

Pages: 569 - 580

DOI: https://doi.org/10.2478/v10006-008-0050-7

Mots clésmachine wear, multidimensional observation, vibration, SVD decomposition, fault space, observation space, symptom limit value, forecasting, grey system theory

This content is open access.

Mots clés
machine wear, multidimensional observation, vibration, SVD decomposition, fault space, observation space, symptom limit value, forecasting, grey system theory