This paper is concerned with accurate, early, and reliable fault diagnosis using an enhanced vibration measurement technique based on short-time Fourier transform. The novelty of this work lies in detecting very low-phase imbalance-related faults. The energy contained within specified frequency bands centred on the rotor frequency and power supply frequency, and their sideband zones were calculated. The technique was firstly demonstrated by simulated signals and then verified by experimental measurements taken from two different-sized test rigs. The first one comprised a 1.1 kW variable speed three-phase induction motor with varying output load (no load, 25%, 50%, 75%, and 100% load). Two types of common faults were introduced: imbalance in one phase as the electrical fault and misalignment of load as the mechanical fault. The second test rig had a 3 kW three-phase induction motor again with varying load, and here the two seeded faults were: phase imbalance and one broken rotor bar. The measured energy levels in the test conditions were found to be affected by type of fault and fault severity. It is concluded that the proposed method offers a potentially reliable and computationally inexpensive condition monitoring tool which can be implemented with real-time monitoring systems.
Rotating machinery such as induction motors and gears driven by shafts are widely used in industry. A variety of techniques have been employed over the past several decades for fault detection and identification in such machinery. However, there is no universally accepted set of practices with comprehensive diagnostic capabilities. This paper presents a new and sensitive approach, to detect faults in rotating machines; based on principal component techniques and residual matrix analysis (PCRMA) of the vibration measured signals. The residual matrix for machinery vibration is extracted using the PCA method, crest factors of this residual matrix is determined and then machinery condition is assessed based on comparing the crest factor amplitude with the base line (healthy) level. PCRMA method has been applied to vibration data sets collected from several kinds of rotating machinery: a wind turbine, a gearbox, and an induction motor. This approach successfully differentiated the signals from healthy system and systems containing gear tooth breakage, cracks in a turbine blade, and phase imbalance in induction motor currents. The achieved results show that the developed method is found very promising and Crest Factors levels were found very sensitive for machinery condition.
Vibration analysis is widely used for rotating machinery diagnostics; however measuring vibration of operational oil well pumps is not possible. The pump’s driver’s current signatures may provide condition-related information without the need for an access to the pump itself. This paper investigates the degree of relationship between the pump’s driver’s current signatures and its induced vibration. This relationship between the driver’s current signatures (DCS) and its vibration signatures (DVS) is studied by calculating magnitude-squared coherence and phase coherence parameters at a certain frequency band using continuous wavelet transform (CWT). The CWT coherence-based technique allows better analysis of temporal evolution of the frequency content of dynamic signals and areas in the time-frequency plane where the two signals exhibit common power or consistent phase behaviour indicating a relationship between the signals. This novel approach is validated by experimental data acquired from 3 kW petroleum pump’s driver. Both vibration and current signatures were acquired under different speed and load conditions. The outcomes of this research suggest the use of DCS analysis as reliable and inexpensive condition monitoring tool, which could be implemented for oil pumps, real-time monitoring associated with condition-based maintenance (CBM) program.
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