Vibration-based Condition Monitoring of Rotating Machines

Student thesis: Phd


Vibration-based condition monitoring (VCM) is an accepted approach for classifying machines as healthy or faulty. Although tangible advancements have been made with acceptable VCM techniques such as amplitude spectrum analysis, phase analysis, orbits analysis, etc. This often warrants the acquisition of vibration data from all orthogonal directions using multiple sensors at each bearing pedestal. Consequently, the number of data sets to be processed and interpreted could become overwhelming, especially when dealing with large industrial rotating machines. Rotating machine faults are generally detected by the presence of harmonics of rotating speed in vibration response. Several studies indicate that higher order spectrum (HOS) and higher order coherence (HOC) possess the capabilities to establish the amplitude and phase interactions of frequency components in measured vibration signals. Hence HOS or HOC may be useful for detecting rotating machine faults. However, applications of HOC dominate the literature, with no clarification on which class is more useful. A comparative study was conducted with numerically simulated (with and without noise) and experimental data from a rig. These studies clearly indicate that HOS offers more meaningful results than HOC, owing to the significant dependence of HOC on the signal noise content. Hence HOS was used for further research studies. Earlier studies tried to eliminate the rigour of analysing separate spectrum per measurement location by constructing single composite spectrum (CS) and bispectrum (CB) irrespective of measurement locations. Observations were encouraging but confined to a rig with relatively rigid foundation. Since several industrial rotating machines possess flexible foundations, the current study examined a wider range of faults on identical rigs with different flexible foundations. Composite trispectrum (CT) was also introduced to enhance robustness and it was observed that fault classification was possible at all speeds by combining just one CB and CT components. Despite the encouraging results obtained from earlier CS, it was limited by phase information loss at intermediate measurement locations. Also, the power spectrum density (PSD) computational approach adopted for the final CS makes it phase blind, thereby relying solely on the amplitudes at individual frequencies. Consequently, an improved poly-coherent composite spectrum (pCCS) was developed which retained phase information at all measurement locations. By building upon the earlier successes achieved with CB and CT, poly-coherent composite bispectrum (pCCB) and trispectrum (pCCT) were similarly developed which provided better diagnosis features. Equipment standardisation as a cost-effective means of rationalising maintenance spares has become a very common industrial strategy. As a consequence of this, the existence of several identical rotating machines with different natural frequencies due to variations in their foundation flexibilities is also common. The development of a reliable method that permits the application of measured vibration data from one machine on another identical machine is likely to be appreciated by the industry. Hence, this was achieved by fusing pCCB and pCCT components in a novel hybrid data fusion algorithm on the identical rigs with different flexible foundations. The insensitivity of the proposed method to various scenarios of data availability was also confirmed with experimental and industrial data.
Date of Award1 Aug 2016
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJyoti Sinha (Supervisor)

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