Effective Vibration-based Condition Monitoring (eVCM) of Rotating Machines

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    • Purpose
    The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework.
    • Design/methodology/approach
    The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines condition monitoring of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM.
    • Findings
    This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance.
    • Research limitations/implications
    The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce condition monitoring data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous condition monitoring such as medicine.
    • Practical implications
    The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growly in maintenance related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional condition monitoring practices. In this paper, an effective vibration-based condition monitoring (eVCM) approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data.
    • Social implications
    The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based condition monitoring of rotating machines positively impacts the society with regards to the possibility
    of reducing how much time is actually spent on the accurate detection and classification of faults.
    • Originality/value
    Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework. However, these studies are limited in the scope of faults, severities and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realised using big data management and data combination approaches.
    Original languageEnglish
    JournalJournal of Quality in Maintenance Engineering
    Publication statusPublished - 7 Mar 2017


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