Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to variations of the rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique is introduced. One of main advantages of order tracking over traditional vibration monitoring lies in its ability to clearly identify non-stationary vibration data and to a large extent exclude the influences of varying rotational speed. In recent years, different order tracking techniques have been developed. Each of these has their own pros and cons in analyzing rotating machinery vibration signals. In this research, three existing order tracking techniques are extensively investigated and combined to further explore their abilities in the context of condition monitoring. Firstly, computed order tracking is examined. This allows non-stationary effects due to the variation of rotational speed to be largely excluded. However, this technique was developed to deal with the entire raw signal and therefore looses the ability to focus on each individual order of interest. Secondly, Vold-Kalman filter order tracking is considered. It is widely reported that this technique overcomes many of the limitations of other order tracking methods and extracts order signals into the time domain. However because of the adaptive nature of the Vold-Kalman filter, the non-stationary effects due to the rotational speed will remain in the extracted order waveform, which is not ideal for conventional signal processing methods such as Fourier analysis. Yet, the strict mathematical filter (the Vold-Kalman filter is based upon two rigorous mathematical equations, namely the data equation and the structural equation, to realize the filter) gives this technique an excellent ability to focus on the orders of interest. Thirdly, the empirical mode decomposition method is studied. In the literature, this technique is claimed to be an effective diagnostic tool for various kinds of applications including diagnosis of rotating machinery faults. Its unique empirical way of extracting non-stationary and non-linear signals allows it to capture machine fault information which is intractable by other order tracking methods. But since there is no precise mathematical definition for an intrinsic mode function in empirical mode decomposition and – as far as could be ascertained – no published assessment of the relationship between an order and an intrinsic mode function, this technique has not been properly considered by analysts in terms of order tracking. As a result, its abilities have not really been explored in the context of order related vibrations in rotating machinery. In this research, the relationship between an order and an intrinsic mode function is discussed and it is treated as a special kind of order tracking method. In stead of focusing individually on each order tracking technique, the current work synthesizes different order tracking techniques. Through combination, exchange and reconciliation of ideas between these order tracking techniques, three improved order tracking techniques are developed for the purpose of enhancing order tracking analysis in condition monitoring. The techniques are Vold-Kalman filter and computed order tracking (VKC-OT), intrinsic mode function and Vold-Kalman filter order tracking (IVK-OT) and intrinsic cycle re-sampling (ICR). Indeed, these improved approaches contribute to current order tracking practice, by providing new order tracking methods with new capabilities for condition monitoring of systems which are intractable by traditional order tracking methods, or which enhances results obtained by these traditional methods. The work commences with a discussion of the inter-relationship between the order tracking methods which are considered in the thesis, and exposition of the scope of the work and an explanation of the way these independent order tracking techniques are integrated in the thesis. To demonstrate the abilities of the improved order tracking techniques, two simulation models are established. One is a simple single-degree-of-freedom (SDOF) rotor model with which VKC-OT and IVK-OT techniques are demonstrated. The other is a simplified gear mesh model through which the effectiveness of the ICR technique is proved. Finally two experimental set-ups in the Sasol Laboratory for Structural Mechanics at the University of Pretoria are used for demonstrating the improved approaches for real rotating machine signals. One test rig was established to monitor an automotive alternator driven by a variable speed motor. A stator winding inter-turn short was artificially introduced. Advantages of the VKC-OT technique are presented and features clear and clean order components under non-stationary conditions. The diagnostic ability of the IVK-OT technique of further decomposing an intrinsic mode function is also demonstrated via signals from this test rig, so that order signals and vibrations that modulate orders in IMFs can be separated and used for condition monitoring purposes. The second experimental test rig is a transmission gearbox. Artificially damaged gear teeth were introduced. The ICR technique provides a practical alternative tool for fault diagnosis. It proves to be effective in diagnosing damaged gear teeth. / Thesis (PhD)--University of Pretoria, 2011. / Mechanical and Aeronautical Engineering / unrestricted

Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to changing rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique was introduced. One of main advantages of order tracking over traditional vibration monitoring techniques, lies in its ability to clearly identify non-stationary vibration data, and to a large extent exclude the influences from varying rotational speed. Several order tracking techniques have been developed and researched during the past 20 years. Among these techniques, Fourier Transform Based Order Tracking (FT-OT), Angle Domain Sampling Based Order Tracking (AD-OT) and Vold-Kalman Filter Order Tracking (VKF-OT) are the three most popular techniques and have been commercialised in software. While the VKF-OT is comparatively new, and both its theory and application are different from the other two techniques, the unique advantages of this technique has led to increased research attention in this field. This growing interest in research on the application of the VKF-OT technique on real machines, and its comparative advantages with respect to other order tracking techniques, inspired the present research. With this work, a comprehensive literature of electrical machine condition monitoring was surveyed, which gives a broad perspective of electrical machine monitoring methods ranging through electrical techniques, vibration techniques, temperature techniques and chemical techniques. To simply the process of applying VKF-OT in initial investigations, simulated single-degree-of freedom and two-degree-of freedom rotor models were established, and the application of the VKF-OT technique on these simulated models was explored. Because most of the current research draws significantly on an understanding of the VKF-OT theory, it was also necessary to review and summarize the current status of VKF-OT theory from previous work, as well as explore the procedures for selection of its filter bandwidth when dealing with real data. An experimental set-up for monitoring an electrical alternator was constructed. Real experimental data were subsequently used to compare the advantages and disadvantages of the three popular order tracking techniques. The unique time domain advantage of VKF-OT was implemented, using crest factor and kurtosis values as indictors of the fault condition of the machine. This gave encouraging results. / Dissertation (MSc)--University of Pretoria, 2008. / Mechanical and Aeronautical Engineering / unrestricted

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The aim of this study is to apply computed order tracking with subsequent rotation domain averaging and statistical analysis to typical mining environments. Computed order tracking is a fault detection method that is unaffected by varying speed conditions often found in industry and has been proven effective in laboratory conditions. However in the controlled environment of a laboratory it is difficult to test the robustness of the order-tracking procedure. The need thus exists to adjust the order tracking procedure so that it will be effective in the mining environment. The procedure needs to be adjusted to function with a two pulse per revolution speed input. The drag gear aboard a dragline rotates in two directions. This gives the unique opportunity to observe the performance of the order tracking method in a bi-directional rotating environment allowing relationships between the results of each operating direction to be investigated. A monitoring station was set up aboard a dragline and data was captured twice daily for a period spanning one year. The data captured consisted of accelerometer and proximity sensor data. The key on the shaft triggers the proximity sensors allowing speed and direction to be measured. The rudimentary measured speed is interpolated using various documented speed interpolation techniques and by a newly developed speed interpolation technique. The interpolated speed is then used to complete the order tracking procedure that re-samples the vibration data with reference to the speed. The results indicate that computed order tracking can be successfully implemented in typical mining environments. Furthermore there is a distinct relationship between vibration data taken in both rotational directions: one direction provides a better indication of incipient failure. It is thus important not to choose a direction randomly when monitoring rotating machinery of this kind. / Dissertation (MEng)--University of Pretoria, 2008. / Mechanical and Aeronautical Engineering / unrestricted

Condition monitoring is very important for critical assets such as gearboxes used in the power and mining industries. Fluctuating operating conditions are inevitable for wind turbines and mining machines such as bucket wheel excavators and draglines due to the continuous uctuating wind speeds and variations in ground properties, respectively. Many of the classical condition monitoring techniques have proven to be ine ective under uctuating operating conditions and therefore more sophisticated techniques have to be developed. However, many of the signal processing tools that are appropriate for uctuating operating conditions can be di cult to interpret, with the presence of incipient damage easily being overlooked. In this study, a cost-e ective diagnostic methodology is developed, using machine learning techniques, to diagnose the condition of the machine in the presence of uctuating operating conditions when only an acceleration signal, generated from a gearbox during normal operation, is available. The measured vibration signal is order tracked to preserve the angle-cyclostationary properties of the data. A robust tacholess order tracking methodology is proposed in this study using probabilistic approaches. The measured vibration signal is order tracked with the tacholess order tracking method (as opposed to computed order tracking), since this reduces the implementation and the running cost of the diagnostic methodology. Machine condition features, which are sensitive to changes in machine condition, are extracted from the order tracked vibration signal and processed. The machine condition features can be sensitive to operating condition changes as well. This makes it difficult to ascertain whether the changes in the machine condition features are due to changes in machine condition (i.e. a developing fault) or changes in operating conditions. This necessitates incorporating operating condition information into the diagnostic methodology to ensure that the inferred condition of the machine is not adversely a ected by the uctuating operating conditions. The operating conditions are not measured and therefore representative features are extracted and modelled with a hidden Markov model. The operating condition machine learning model aims to infer the operating condition state that was present during data acquisition from the operating condition features at each angle increment. The operating condition state information is used to optimise robust machine condition machine learning models, in the form of hidden Markov models. The information from the operating condition and machine condition models are combined using a probabilistic approach to generate a discrepancy signal. This discrepancy signal represents the deviation of the current features from the expected behaviour of the features of a gearbox in a healthy condition. A second synchronous averaging process, an automatic alarm threshold for fault detection, a gear-pinion discrepancy distribution and a healthy-damaged decomposition of the discrepancy signal are proposed to provide an intuitive and robust representation of the condition of the gearbox under uctuating operating conditions. This allows fault detection, localisation as well as trending to be performed on a gearbox during uctuating operation conditions. The proposed tacholess order tracking method is validated on seven datasets and the fault diagnostic methodology is validated on experimental as well as numerical data. Very promising results are obtained by the proposed tacholess order tracking method and by the diagnostic methodology. / Dissertation (MEng)--University of Pretoria, 2016. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

Conventional gearbox vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the gears in the gearbox. However, this assumption is not valid under fluctuating load conditions, since the fluctuating load will amplitude modulate the measured vibration signal and cause the rotational speed of the system to change. In general monitoring of machines subject to fluctuating load conditions is dealt with by considering the constant load conditions on gearboxes or during free rotational tests. The need to monitor the condition of large gearboxes in mineral mining equipment has attracted greater interest in order to improve asset management. An inherent need for signal processing techniques, with the ability to indicate degradation in gear condition, under fluctuating load conditions exist. Such techniques should enable the online monitoring of gearboxes that operate under fluctuating load conditions. A continued flow of up to date information should consequently be available for asset and production management. With this research, a load demodulation normalisation procedure was developed to remove the modulation caused by fluctuating load conditions, which obscures the detection of an incipient gear fault conditions. A rotation domain averaging technique is implemented which combines the ability of computer order tracking and time domain averaging to suppress the spectral smearing effect caused by the fluctuation in speed, as well as to suppress the amplitude of the vibration which is not synchronous with the rotation of the gear shaft. It is demonstrated that the instantaneous angular speed of a gearbox shaft can be utilised to monitor the condition of the gear on the shaft. The instantaneous angular speed response measurement is less susceptible to phase distortion introduced by the transmission path when compared to conventional gearbox casing vibration measurements. A phase domain averaging approach was developed to overcome the phase distortion effect of the transmission path under fluctuating load conditions. The load demodulation normalisation and rotation domain averaging signal processing procedures were applied to both the conventional gearbox casing vibration and instantaneous angular speed measurements prior to the calculation of a smoothed pseudo Wigner-Ville distribution of the data. Statistical parameters such as the energy ratio were calculated from the distribution. These parameters could be monotonically trended under different load conditions to indicate the degradation of gear conditions. / Thesis (PhD (Mechanical Engineering))--University of Pretoria, 2005. / Mechanical and Aeronautical Engineering / unrestricted

Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tire-pavement interaction noise is tire tread excitation. The tire tread contributes to the tire-pavement interaction noise mainly through two mechanisms: (1) tread block impact, and (2) the compression and expansion of the air in the tread groove at the contact patch. The tread pattern is the critical part of the tire design since it can be easily modified. Hence, the main focus of this study is to quantify the tread pattern contribution in total tire-pavement interaction noise. To achieve this goal, the noise produced by the tread pattern is separated from the total tire-pavement interaction noise. Since the tread pattern excitation is periodic with tire rotation, the noise produced by the tread is assumed to be related to the tire rotation. Hence, the order domain synchronous averaging method is used in this study to separate and quantify the tread pattern contribution to the total tire-pavement interaction noise. The experiment has been carried out using an On-Board-Sound-Intensity (OBSI) system. Five tires were tested including the Standard Reference Test Tire (SRTT). Compared to the conventional OBSI system, an optical sensor was added to the system to monitor the tire rotation. The once per revolution signal provided by the optical sensor is used to identify the noise signals associate to each revolution. In addition to the averaging method using optical signals, other data processing techniques have been investigated for separating the tread-pattern noise without utilizing the once per revolution signal. These techniques are autocorrelation analysis, a frequency domain filter, principal component analysis, and independent component analysis. In the tread-pattern noise generation, the tread profile is the most important input parameter. To characterize the tread profile, the tread pattern spectral content and air volume velocity spectral content for all the five tires are computed. Then, the tread pattern spectrum and the air volume velocity spectrum are both correlated with the separated tread-pattern noise by visual inspection of the spectra shape. / Master of Science

Rotating blades are considered to be the one of the most common cause of failures in rotating machinery. Blade failure modes normally occur as a result of cracks due to unexpected operating conditions, which are normally caused by accidents of foreign objects damage, high cycle fatigue, blade rubbing, blade root looseness, and degradation from erosion and corrosion. Thus, detection of blade faults has an important role in reducing blade related failures and allowing repairs to be scheduled for the machinery. This in turn will lead to reduction in maintenance costs and thus raise productivity and safety aspects of operation. To maintain vital components of rotating machines, such as blades, shafts, bearings and gear boxes, at optimal levels, detection of failures in such components is important, because this will prevent any serious damage that could affect performance. This research study involves laboratory tests on a small rig with a bladed disc rotor that applied vibration measurements and analysis for blade fault detection. Three measurements: shaft torsional vibration, on-bearing vibration (OBV) and on-casing vibration (OCV), are used. A small test rig of a single stage bladed disc holding 8-blades was designed and manufactured, to carry out this research study to assess the usefulness and capability of each vibration technique in detection of incipient defects within machine blades. A series of tests was conducted on a test rig for three different cases of blade health conditions: (a) healthy blade(s) with mistuned effects, (b) blade root looseness and (c) cracks in a blade on two different blade sizes (long and short blades) in order to discover changes in blades' dynamic behaviour during the machine running-up operation. The data were collected using the three measurements during machine run-up and then recorded. The measured vibration data were analysed by computing the blades' resonance at different engine orders (EOs) related to the blade(s) resonance frequencies and their higher harmonics, to understand the blade(s) dynamics behaviour for the cases of healthy and faulty blade(s). Data have been further processed using a polar plot presentation method which provides clear results that can be used for monitoring blade integrity. To validate the obtained experimental results, a simplified mathematical model was also developed. Finally, a comparative study between three methods was undertaken to understand the relative advantages and limitations in the blade heath monitoring.

The doctoral thesis focuses on a Vold-Kalman filter (VKF). Theoretical part describes properties of VKF and other order tracking methods, namely computed order tracking (COT) and Gabor order tracking (GOT). It also characterizes requirements for rotational speed measurements as one of the key elements for correct functionality of VKF. Practical part depicts own filter implementation and its properties. Main stress is put on computational efficiency, that is in result better than in available codes. Thesis also points out possible issues with numerical instabilities within calculation caused by limited dynamic range of double data type. This is solved by restricting the inputs to prevent the instabilities. Restriction is applied also to cases where the result is numerically correct but unusable. Following part extends the comparison with methods STFT, COT and GOT, where benefits of VKF for nonstationary conditions are shown. The last section shows given information used on simulated signals. This is then applied to show mentioned techniques on experimental data, for instance from turbo engine or electric motor, where the ability of VKF in checking the accordance between speed profile and vibration data is illustrated.

This master's thesis deals with order analysis. The first part of the thesis describes common methods for order analysis and methods for processing tacho and vibration signal. The second part contains a brief description of some open source tools for order analysis focusing on Sound and Vibration Measurement Suite (SVMS) package for LabVIEW by National Instruments company. The main purpose of the thesis has been designing and realization of own library for order analysis as a plugin for development tool LabVIEW. In the library, there are implemented methods for order analysis including basic functions for processing vibration and tacho signal. Their brief description with manual for its usage is in the third part of the thesis. In the last chapter, implemented functions are compared with those, which are implemented in SVMS by National Instruments, using both simulated and real data.