An Investigation of Electrical Motor Parameters in a Sensorless Variable Speed Drive for Machine Fault Diagnosis

Motor current signature analysis (MCSA) is regarded as an effective technique for motor and its downstream equipment fault diagnostics. However, limited work has been carried out for motors based on a sensorless variable speed drive (VSD). This study focuses on investigation of mechanical fault detection and diagnosis using electrical signatures from a VSD system. An analytic analysis was conducted to show that the fault can induce sidebands in instantaneous current, voltage and power signals in the VSD system, rather than just the sideband in a drive without closed loop control. Then different degrees of tooth breakages in an industrial two-stage helical gearbox were experimentally studied. It has found that even though the measured signal is very noisy, common spectrum analysis can discriminate the small sidebands for the fault detection and diagnosis. However, it has found that the power signals resulted from the multiplication of the current and voltage can provide a better diagnostic result

THE ANALYSIS OF POWER SUPPLY SIGNALS BY INCLUDING PHASE EFFECTS FOR MACHINE FAULT DIAGNOSIS

Substantial efforts have been devoted to developing Condition Monitoring techniques to provide timely preventative measures for ensuring a safe and cost-effective operation of electromechanical systems. High investment of installation and implementation in using conventional condition monitoring techniques such as vibration based monitoring makes it difficult to be used in most industries such as petrochemical processing, food and drinking processes, paper mills and so on where large number of motor drives are used but operational profits are very limited. To overcome the shortages of vibration based monitoring, this project focuses on developing condition monitoring techniques based on electrical signal analysis which can offer great savings as electric signatures that can monitor a large system are generally available in most motor drives. However, fault signatures in electrical signatures such as instantaneous current and voltage signals are very weak and contaminated by noise. To enhance the signatures, this study has focused on using two more advanced signal processing approaches: 1) Modulation signal bispectrum analysis, which enhances the modulation and suppresses random noise by including phase linkages. 2) Instantaneous phase quantities including conventional instantaneous power factor and a novel instantaneous phase of voltage and current which highlights instantaneous phase changes through a summation of instantaneous phases in current and voltage signals. It has the ability of enhancing the phase components that are of the same phases in both voltage and current signals, and also cancel out any random components to a great extent, producing more diagnostic information. These two approaches emphasis the use of phase information along with that of amplitudes and frequency in a signal that is based on in most previous methods in the condition monitoring fields. Based on a general electromechanical system comprising of a AC motor, a gearbox and a DC generator, it firstly explored the characteristics of the signatures by modelling and simulation studies, which lead to that faults in a sensorless Variable speed drive system can produce combined amplitude and frequency modulation effects in both current and voltage signals fed to the AC motor. Moreover, the modulating frequencies and levels are closely associated with the rotational frequencies of the gearbox and fault severity respectively, which become more significant at higher load conditions. Experimental evaluations have found that these two proposed methods allow common faults in the downstream gearbox including gear tooth breakage, oil shortage and excessive bearing clearances to be detected and diagnosed under high load conditions, showing the effectiveness and accuracy of these two new approaches. Furthermore, the results show that the electrical signature analysis is capable of detecting and diagnosing different faults in sensorless variable speed drive systems. Instantaneous phase of voltage and current has been shown to provide more consistent and accurate separation between the three different faults under different loads. The use of the modulation signal bispectrum analysis succeed to provide an improved, accurate and reliable diagnostic with the power signal providing the best means of detecting and determining fault severity with good separation between fault levels

FAULT DIAGNOSIS OF MECHANICAL SYSTEMS BASED ON ELECTRICAL SUPPLY CHARACTERISTICS

Induction motors are the main workhorses of industry. Condition monitoring (CM) of motor based systems plays an important role in the early detection of possible defects, averting adverse operational and financial effects caused by unexpected breakdowns. Limited information has been found which explores the diagnostic abilities of voltage and motor current signals from motors with variable speed drives (VSDs), which are increasingly used in industry to obtain better dynamic response, higher efficiency and lower energy consumption. This study addresses the gap identified by carrying out a systematic review of the monitoring of mechanical systems using induction motors with sensorless VSDs. Specifically, existing techniques often prove ineffective for common internal and external faults that develop in Induction motors. The primary aim is to extract accurate diagnostic information from the power supply parameters of a VSD to monitor IM driven systems for early diagnosis of both mechanical and electrical faults. This thesis examines the effectiveness of both motor current and voltage signals using spectrum analysis for detecting broken rotor bar(s) and/or shaft misalignment and gear oil viscosity changes with different degrees of severities under sensorless control (close) modes. The results are obtained from common spectrum analysis applied to signals from a laboratory experimental setup operating under different speeds and loads. Evaluation of the results shows that the faults cause an increase in sideband amplitudes, which can be observed in both the current and voltage signals under the sensorless control mode. In addition, combined faults cause an additional increase in the sideband amplitudes and this increase can be observed in both the current and voltage signals. The voltage signals show greater change compared with the current signals because the VSD adapts the voltage supply source to compensate for changes in the system dynamics. Furthermore, this study also presents a model of an induction motorfed by a variable speed drive (VSD), as an approach to simulate broken rotor bars and shaft misalignments to give an in-depth understanding of fault signatures. The model was validated with experimental results in both current and voltage signals, with good agreement. The model confirmed that BRB causes a shift and increase in the amplitudes of the sidebands with the amplitudes of the rotor frequency components increased due to shaft misalignment

The Monitoring of Induction Machines Using Electrical Signals from the Variable Speed Drive

Induction motors are the most widely used industrial prime movers, mainly because of their simple yet powerful construction, ergonomic adaptability, rugged and highly robust structure combined with high reliability. However, under extreme and complex operations, such motors are subject to premature faults, which can be more significant when variable speed drive (VSDs) are used, due to the presence of more voltage harmonics, spikes and increases in operating temperature. In addition, VSD based systems also cause more noise in measured instantaneous current signals. These make it more difficult to investigate and accurately diagnose system faults in order to keep VSD based motors operating at an optimal level and avoid excessive energy consumption and damage to system. However, insufficient work has been carried out exploring fault diagnosis using terminal voltage and motor current signals of VSD motors which are increasingly used in industry. To fill these gaps, this thesis investigates the detection of stator and rotor faults (i.e. shorted turn faults, open-circuit faults, broken rotor bars, and stator winding asymmetry combined with broken rotor bar faults) using electrical signals from VSDs under different loads and different speeds conditions. Evaluation results show that under open loop control mode, both stator and rotor faults cause an increase in the amplitude of sidebands of the motor current signature. However, no changes were found that could be used for fault detection in the motor voltage signature with respect to open loop control mode. This is because, when the drive is in open-loop operation, there is no feedback to the drive and torque oscillations modulate the motor current only. The V/Hz ratio is kept constant even when the slip changes either due to the load or the fault. On the other hand, the increase in the sideband amplitude can be observed in both the current and voltage signals under the sensorless control mode with the voltage spectrum demonstrating a slightly better performance than the motor current spectrum, because the VSD regulates the voltage to adapt changes in the electromagnetic torque caused by the faults. The comparative results between current and voltage spectra under both control modes show that the sensorless control gives more reliable diagnosis. In order to monitor the condition of electrical drives accuratly and effectively, demodulation analysis such as modulation signal bispectrum (MSB) of the electrical signals from the VSDs has been explored extensively in this thesis to detect and diagnose different motor faults. MSB analysis has been shown to provide good noise reduction, and more accurate and reliable diagnosis. It gives a more correct indication of the fault severity and fault location for all operating conditions. This study also examines detecting and diagnosing the effect of an asymmetric stator winding combined with broken rotor bar (BRB) faults under the sensorless operation mode. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using power spectrum (PS) and MSB analysis. The obtained results show that the combined fault causes an additional increase in the sideband amplitude and this increase can be observed in both the current and voltage signals. The MSB diagnosis based on the voltage signals is more sensitive to detect motor faults at lower loads compared with that of current signals. Moreover, this research presented a new method based on MSB sideband estimation (MSB-SE). It is shown that using MSB-SE, the sidebands due to weak fault signatures can be quantified more accurately, which results in more consistent detection and accurate diagnosis of the fault severity

Condition Monitoring of Helical Gear Transmissions Based on Vibration Modelling and Signal Processing

Condition monitoring (CM) of gear transmission has attracted extensive research in recent years. In particular, the detection and diagnosis of its faults in their early stages to minimise cost by maximising time available for planned maintenance and giving greater opportunity for avoiding a system breakdown. However, the diagnostic results obtained from monitored signals are often unsatisfactory because mainstream technologies using vibration response do not sufficiently account for the effect of friction and lubrication. To develop a more advanced and accurate diagnosis, this research has focused on investigating the nonlinearities of vibration generation and transmission with the viscoelastic properties of lubrication, to provide an in-depth understanding of vibration generating mechanisms and hence develop more effective signal processing methods for early detection and accurate diagnosis of gear incipient faults. A comprehensive dynamic model has been developed to study the dynamic responses of a multistage helical gear transmission system. It includes not only time-varying stiffness but also tooth friction forces based on an elastohydrodynamic lubrication (EHL) model. In addition, the progression of a light wear process is modelled by reducing stiffness function profile, in which the 2nd and 3rd harmonics of the meshing frequency (and their sidebands) show significant alteration that support fault diagnostic at early stages. Numerical and experimental results show that the friction and progressive wear induced vibration excitations will change slightly the amplitudes of the spectral peaks at both the mesh frequency and its sideband components at different orders, which provides theoretical supports for extracting reliable diagnostic signatures. As such changes in vibrations are extremely small and submerged in noise, it is clear that effective techniques for enhancing the signal-to-noise ratio, such as time synchronous averaging (TSA) and modulation signal bispectrum (MSB) are required to reveal such changes. MSB is preferred as it allows small amplitude sidebands to be accurately characterised in a nonlinear way without information loss and does not impose any addition demands regarding angular displacement measurement as does TSA. With the successful diagnosis of slight wear in helical gears, the research progressed to validate the capability of MSB based methods to diagnose four common gear faults relating to gear tribological conditions; lubrication shortfall, changes in lubrication viscosity, water in oil, and increased bearing clearances. The results show that MSB signatures allows accurate differentiation between these small changes, confirming the model and signal processing proposed in this thesi