Prognostic Approaches Using Transient Monitoring Methods

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information regarding both systems and equipment. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. The development for algorithms and techniques to quickly, and intuitively develop generic quantification of deviations a transient signal towards the goal of prognostic predictions has until now, largely been overlooked. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring. This research is based on the hypothesis that equipment health signals for some failure modes are stronger during transient conditions than during steady-state because transient conditions (e.g. start-up) place greater stress on the equipment for these failure modes. From this it follows that these signals related to the system or equipment health would display more prominent indications of abnormality if one were to know the proper means to identify them. This project seeks to develop methods and conceptual models to monitor transient signals for equipment health. The purpose of this research is to assess if monitoring of transient signals could provide alternate or better indicators of incipient equipment failure prior to steady state signals. The project is focused on identifying methods, both traditional and novel, suitable to implement and test transient model monitoring in both an useful and intuitive way. By means of these techniques, it is shown that the addition information gathered during transient portions of life can be used to either to augment existing steady-state information, or in cases where such information is unavailable, be used as a primary means of developing prognostic models

Experimental investigations of two-phase flow measurement using ultrasonic sensors

This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow

A Review of ECG Signal De-noising and Peaks Detection Techniques

The Modern era is marked by tension and, therefore, heart is in trouble. Whole of the world is busy making research in ECG techniques. on Electrocardiogram and its feature extraction is the area of interest.. Analysis and classification let the medical professional to detect the heart ailments and other diseases. In our research method for De-noising of ECG signal and Feature extraction Algorithm is proposed. We considered the baseline wander problem in ECG signal which is basically due to the measurement error. This work involves the IIR filter Savitzky-Golay filter and Wavelet Transform. ECG signal is de-noised without affecting any information from ECG. We have also designed a feature Extraction algorithm which automatically detects the RR interval and QRS interval along with the amplitude of Q, R, and S. The whole algorithm including the de-noising of signal and Feature extraction. This work has been simulated on the MATLab software

A smart monitoring system for bearing fault detection

Rolling element bearings are commonly used in rotating machinery to support shafts, reduce friction, and increase power transmission efficiency. For a machinery system, bearing fault could be the most possible cause of mechanical failures. If bearing defect can be detected at its early stage, mechanical performance degradation and even economic losses can be avoided. Although many signal processing techniques have been proposed in the literature for bearing fault detection, reliable bearing fault diagnosis is still a challenging task in this R&D field, especially in industrial applications. The objective of this work is to develop a smart condition monitoring system and a signal processing technique for bearing fault detection. Firstly, a Field Programmable Gate Arrays (FPGA) based sinusoidal generator is developed to generate controllable sinusoidal waveforms and explore FPGA’s potential applications in a data acquisition system to collect vibration signals. Secondly, an adaptive variational mode decomposition (AVMD) technique is proposed for bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to select the optimal intrinsic mode function (IMF) to decompose the target signal. 3) The envelope spectrum analysis is performed using the selected IMF to identify the representative features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by simulation and experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques

Advanced Fault Detection Methods for Permanent Magnets Synchronous Machines

The trend in recent years of transport electrification has significantly increased the demand for reliability and availability of electric drives, particularly in those employing Permanent Magnet Synchronous Machines (PMSM), often selected due to their high efficiency and energy density. Fault detection has been identified as one of the key aspects to cover such demand. Stator winding faults are known to be the second most common type of fault, after bearing fault. An extensive literature review has shown that, although a number of methods has been proposed to address this type of fault, no tool of general application, capable of dealing effectively with fault detection under transient conditions unrelated to the fault, has been proposed up to date. This thesis has made contributions to modelling, real-time emulation and stator winding fault detection of PMSM. Fault detection has been carried out through model-based and signal-based methods with a specific aim at operation during transient conditions. Furthermore, fault classification methods already available have been implemented with features computed by proposed signal-based fault detection methods. The main conclusion drawn from this thesis is that model-based fault detection methods, particularly those based on residuals, appear to be better suited for transient conditions analysis, as opposed to signal-based fault detection methods. However, it is expected that a combination of the two (model/signal) would yield the best results

Detection of faults in a scaled down doubly-fed induction generator using advanced signal processing techniques.

The study ventures into the development of a micro-based doubly fed induction generator (DFIG) test rig for fault studies. The 5kW wound rotor induction machine (WRIM) that was used in the test rig was based on a scaled-down version of a 2.5MW doubly fed induction generator (DFIG). The micromachine has been customized to make provision for implementing stator inter-turn short-circuit faults (ITSCF), rotor ITSCF and static eccentricity (SE) faults in the laboratory environment. The micromachine has been assessed under the healthy and faulty states, both before and after incorporating a converter into the rotor circuit of the machine. In each scenario, the fault signatures have been characterised by analyzing the stator current, rotor current, and the DFIG controller signals using the motor current signature analysis (MCSA) and discrete wavelet transform (DWT) analysis techniques to detect the dominant frequency components which are indicative of these faults. The purpose of the study is to evaluate and identify the most suitable combination of signals and techniques for the detection of each fault under steady-state and transient operating conditions. The analyses of the results presented in this study have indicated that characterizing the fault indicators independent of the converter system ensured clarity in the fault diagnosis process and enabled the development of a systematic fault diagnosis approach that can be applied to a controlled DFIG. It has been demonstrated that the occurrence of the ITSCFs and the SE fault in the micro-WRIM intensifies specific frequency components in the spectral plots of the stator current, rotor current, and the DFIG controller signals, which may then serve as the dominant fault indicators. These dominant components may be used as fault markers for classification and have been used for pattern recognition under the transient condition. In this case, the DWT and spectrogram plots effectively illustrated characteristic patterns of the dominant fault indicators, which were observed to evolve uniquely and more distinguishable in the rotor current signal compared to the stator current signal, before incorporating the converter in the rotor circuit. Therefore, by observing the trends portrayed in the decomposition bands and the spectrogram plots, it is deemed a reliable method of diagnosing and possibly quantifying the intensity of the faults in the machine. Once the power electronic converter was incorporated into the rotor circuit, the DFIG controller signals have been observed to be best suited for diagnosing faults in the micro-DFIG under the steady-state operating condition, as opposed to using the terminal stator or rotor current signals. The study also assessed the impact of undervoltage conditions at the point of common coupling (PCC) on the behaviour of the micro-DFIG. In this investigation, a significant rise in the faulted currents was observed for the undervoltage condition in comparison to the faulty cases under the rated grid voltage conditions. In this regard, it could be detrimental to the operation of the micro-DFIG, particularly the faulted phase windings, and the power electronic converter, should the currents exceed the rated values for extended periods

Guided Lamb Wave Based 2-D Spiral Phased Array for Structural Health Monitoring of Thin Panel Structures

In almost all industries of mechanical, aerospace, and civil engineering fields, structural health monitoring (SHM) technology is essentially required for providing the reliable information of structural integrity of safety-critical structures, which can help reduce the risk of unexpected and sometimes catastrophic failures, and also offer cost-effective inspection and maintenance of the structures. State of the art SHM research on structural damage diagnosis is focused on developing global and real-time technologies to identify the existence, location, extent, and type of damage. In order to detect and monitor the structural damage in plate-like structures, SHM technology based on guided Lamb wave (GLW) interrogation is becoming more attractive due to its potential benefits such as large inspection area coverage in short time, simple inspection mechanism, and sensitivity to small damage. However, the GLW method has a few critical issues such as dispersion nature, mode conversion and separation, and multiple-mode existence. Phased array technique widely used in all aspects of civil, military, science, and medical industry fields may be employed to resolve the drawbacks of the GLW method. The GLW-based phased array approach is able to effectively examine and analyze complicated structural vibration responses in thin plate structures. Because the phased sensor array operates as a spatial filter for the GLW signals, the array signal processing method can enhance a desired signal component at a specific direction while eliminating other signal components from other directions. This dissertation presents the development, the experimental validation, and the damage detection applications of an innovative signal processing algorithm based on two-dimensional (2-D) spiral phased array in conjunction with the GLW interrogation technique. It starts with general backgrounds of SHM and the associated technology including the GLW interrogation method. Then, it is focused on the fundamentals of the GLW-based phased array approach and the development of an innovative signal processing algorithm associated with the 2-D spiral phased sensor array. The SHM approach based on array responses determined by the proposed phased array algorithm implementation is addressed. The experimental validation of the GLW-based 2-D spiral phased array technology and the associated damage detection applications to thin isotropic plate and anisotropic composite plate structures are presented

Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained