The early detection of damage of in-service structural or mechanical systems is of vital importance. With early detection, the damage may be repaired before the integrity of the system is jeopardized, avoiding possible monetary losses, environmental impacts, injury and death. With this goal in mind, many structural health monitoring techniques have been developed which use a combination of sensors and algorithms to collect, process and interpret data to detect damage in a structure. This thesis presents work completed in support of the experimental validation of a novel structural health monitoring technique developed with the aim of providing improved qualitative results compared to those methods currently available.

This dissertation proposes hybrid models for (i) diagnosis and (ii) remaining useful life estimation of a single fatigue crack in a low-pressure turbine blade. The proposed hybrid methods consist of physics-based methods and data-driven methods. In this dissertation, blade tip timing is used to measure the relative tip displacement of a rotor blade. The natural frequency of the blade is determined by detecting the critical speeds of the blade using a newly derived least squares spectral analysis method. The method shares its origin from the Lomb-Scargle periodogram and can detect resonance frequencies in the blade’s displacement while the rotor is in operation. A Campbell diagram is then used to convert the critical speed into a natural frequency. Two kinds of shaft transients are considered, a run-up run-down crossing the same critical speed, is used to test the new method. This dissertation shows that the relative displacement of the blade tip is comparable to those simulated from an analytical single degree of freedom model. It is also shown that the newly proposed resonance detection method estimates the natural frequency of the blade to a high degree of accuracy when compared to the measurements from a modal impact hammer test. The natural frequency obtained from the real time measurement is then used in a pre-constructed hybrid diagnostics model. The diagnostics model provides a probability density function estimation of the surface crack length given the measured natural frequency. A Gaussian Process Regression model is trained on data collected during experiments and finite element simulations of a fatigue crack in the blade. The final part of this dissertation is a sequential inference model for improving the estimation of the crack length and the prediction of the crack growth. The suggested model uses an unscented Kalman filter that improves estimations of the crack length and the rate of crack growth from Paris’ Law coefficients. The model is updated each time a diagnosis is performed on the blade. The RUL of the blade is then determined from an integration of Paris’s Law given the uncertainty estimates of the current damage in the blade. The result of the algorithm is an estimation of the remaining number of cycles to failure. The algorithm is shown to improve the overall estimation of the RUL; however, it is suggested that future work looks at the convergence rate of the method. / Dissertation (MEng)--University of Pretoria, 2019. / Eskom Power Plant Engineering Institute (EPPEI) / Mechanical and Aeronautical Engineering / MEng / Unrestricted

The reliable operation of planetary gearboxes is critical for the sustained operation of many machines such as wind turbines and helicopter transmissions. Hybrid methods that make use of the respective advantages of physics-based and data-driven models can be valuable in addressing the unique challenges associated with the condition monitoring of planetary gearboxes. In this dissertation, a hybrid framework for diagnostics and prognostics of planetary gearboxes is proposed. The proposed framework aims to diagnose and predict the root crack length in a planet gear tooth from accelerometer measurements. Physics-based and data-driven models are combined to exploit their respective advantages, and it is assumed that no failure data is available for training these models. Components required for the implementation of the proposed framework are studied separately and challenges associated with each component are discussed. The proposed hybrid framework comprises a health state estimation and health state prediction part. In the health state estimation part of the proposed framework, the crack length is diagnosed from the measured vibration response. To do this, the following model components are implemented: A first finite element model is used to simulate the crack growth path in the planet gear tooth. Thereafter, a second finite element model is used to establish a relationship between the gearbox time varying mesh stiffness, and the crack length in the planet gear tooth. A lumped mass model is then used to model the vibration response of the gearbox housing subject to the gearbox time varying mesh stiffness excitation. The measurements from an accelerometer mounted on the gearbox housing are processed by computing the synchronous average. Finally, these model components are combined with an additional data-driven model for diagnosing the crack length from the measured vibration response through the solution of an inverse problem. After the crack length is diagnosed through the health state estimation model, the Paris crack propagation law and Bayesian state estimation techniques are used to predict the remaining useful life of the gearbox. To validate the proposed hybrid framework, an experimental setup is developed. The experimental setup allows for the measurement of the vibration response of a planetary gearbox with different tooth root crack lengths in the planet gear. However, challenges in reliably detecting the damage in the experimental setup lead to the use of simulated data for studying the respective components of the hybrid method. Studies conducted using simulated data highlighted interesting challenges that need to be overcome before a hybrid diagnostics and prognostics framework for planetary gearboxes can be applied in practice. / Dissertation (MSc)--University of Pretoria, 2021. / Eskom EPPEI / Mechanical and Aeronautical Engineering / Msc / Unrestricted

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One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors and extracting information from them. In this regard, instrumenting with too many sensors may be inefficient as this could lead to superfluous data that is expensive to capture and process. This research aims to explore an alternate SHM technique that optimizes the data acquisition process by eliminating the amount of redundant data that is sensed and uses this sufficient data to detect and locate the fault present in the structure. Efficient data acquisition requires a mechanism that senses just the necessary amount of data for detection and location of fault. For this reason Compressive Sensing (CS) is explored as a plausible idea. CS claims that signals can be reconstructed from what was previously believed to be incomplete information by Shannon's theorem, taking only a small amount of random and linear non - adaptive measurements. As responses of many physical systems contain a finite basis, CS exploits this feature and determines the sparse solution instead of the traditional least - squares type solution.As a first step, CS is demonstrated by successfully recovering the frequency components of a simple sinusoid. Next, the question of how CS compares with the conventional Fourier transform is analyzed. For this, recovery of temporal frequencies and signal reconstruction is performed using the same number of samples for both the approaches and the errors are compared. On the other hand, the FT error is gradually minimized to match that of CS by increasing the number of regularly placed samples. Once the advantages are established, feasibility of using CS to detect damage in a single degree of freedom system is tested under unforced and forced conditions. In the former scenario, damage is indicated when there is a change in natural frequency of vibration of the system after an impact. In the latter, the system is excited harmonically and damage is detected by a change in amplitude of the system's vibration. As systems in real world applications are predominantly multi-DOF, CS is tested on a 2-DOF system excited with a harmonic forcing. Here again, damage detection is achieved by observing the change in the amplitude of vibration of the system. In order to employ CS for detecting either a change in frequency or amplitude of vibration of a structure subjected to realistic forcing conditions, it would be prudent to explore the reconstruction of a signal which contains multiple frequencies. This is accomplished using CS on a chirp signal. Damage detection is clearly a spatio-temporal problem. Hence it is important to additionally explore the extension of CS to spatial reconstruction. For this reason, mode shape reconstruction of a beam with standard boundary conditions is performed and validated with standard/analytical results from literature. As the final step, the operation deflection shapes (ODS) are reconstructed for a simply supported beam using CS to establish that it is indeed a plausible approach for a less expensive SHM. While experimenting with the idea of spatio-temporal domain, the mode shape as well as the ODS of the given beam are examined under two conditions - undamaged and damaged. Damage in the beam is simulated as a decrease in the stiffness coefficient over a certain number of elements. Although the range of modes to be examined heavily depends on the structure in question, literature suggests that for most practical applications, lower modes are more dominant in indicating damage. For ODS on the other hand, damage is indicated by observing the shift in the recovered spatial frequencies and it is confirmed by the reconstructed response.

La surveillance de santé structurale (SHM) a attiré beaucoup d'attention dans de nombreux domaines tels que l'industrie civile, aéronautique, mécanique, etc., car il est important de surveiller l'état de la structure afin d'éviter des défaillances structurelles imprévues. Le processus d'identification des endommagements à quatre niveaux: existence, localisation, sévérité et prédiction de l'évolution des endommagements peut être partiellement réalisé si un propre indicateur est bien choisi. Il existe différents indicateurs d'endommagements dont la gamme d'application de la fréquence s'étend de la réponse vibratoire à basses fréquences aux régimes ultrasoniques dans la gamme méga hertz.Les structures sandwich sont largement utilisées dans diverses applications d'ingénierie en raison de son rapport rigidité / poids exceptionnellement élevé par rapport aux structures monocoques. Dans ce travail, une structure sandwich a été étudiée et des indicateurs basés sur la réponse vibratoire ont été conçus en utilisant ses caractéristiques de directivité de propagation et d'amortissement relativement élevé de la structure. Des investigations numériques sur différents scénarios d'endommagement (càd, différents types d'endommagement et leurs combinaisons) et une discussion associée sur la plage d'application ont d'abord été effectuées. La configuration expérimentale a été facilement réalisée à l'aide d'un vibromètre laser à balayage Doppler (SLDV). L'endommagement a été détecté avec succès par les indicateurs proposés. / Structural health monitoring (SHM) has attracted much attention in many engineering fields like civil, aeronautic, mechanical industry, etc. since it is important to monitor the healthy condition of the operational structure in order to avoid unpredicted structural failure which may have severe consequences. The four-level damage identification process: existence, localization, severity and prediction of damage evolution, can be partly realized if a suitable indicator is chosen. It exists different damage indicators whose application range of frequency spans from vibrational response at low frequencies to the ultrasonic regimes in the mega hertz range.The sandwich structures are widely used in various engineering applications due to its exceptionally high flexural stiffness-to-weight ratio compared to monocoque structures. In this thesis a specified twin-wall sandwich structure in polypropylene was studied and vibration-based indicators were designed by taking use of its relative high damping and propagation directivity characteristics. Numerical investigations on different damage scenarios (i.e., different types of defect and their combinations) and an associated discussion on the range of application were first carried out. Experimental configuration was easily realized with the help of a scanning laser doppler vibrometer (SLDV). Defect was successfully detected by the proposed indicators.

Nowadays, by increasing competitions in business, decision-making in maintenance management gets higher priorities. While maintenance in many industries is known as a necessary evil, making cost-effective decisions constructed based on analytical and quantitative assessments improve maintenance position from a cost center into a profit generating one. Within the scope of rotating equipment maintenance, it is vital to consider both economic and technical variables integrated to survive in the competitive market. Taking the role of improving rotating equipment performance within a cost-effective approach into account, this project proposes a consistent model followed by substantial results. The model application can be briefed in improvement of decision-making processes from technical and economic perspectives to direct them into the company profitability aligned with the company goals. Further, it proposes technical and economic indicators and improves utilization of maintenance resources including personnel skills and experience to enhance maintenance performance and develop cost-effective scenarios. Within the model, eMDSS (software) is used as an intelligent facilitator/tool which improves accurate decision-making and identifying and prioritizing maintenance problems. To ensure from the model and its tool applicability, it is applied on a real case study and reliability of the achieved results are tested and approved in detail. In result, the project is successful in evaluating previous technical and economic decisions and selecting the most cost-effective scenario for future with a saving value exceeding 133K€ and profit of 114K€ for the case equipment.

For many years batteries have been used as the main power sources for portable electronic devices. However, the rate of scaling in integrated circuits and micro-electro-mechanical systems (MEMS) has been much higher than that of the batteries technology. Therefore, a need to replace these temporary energy reservoirs with small sized continuously charged energy supply units has emerged. These units, named as energy harvesters, use several types of ambient energy sources such as heat, light, and vibration to provide energy to intelligent systems such as sensor nodes. Among the available types, vibration based electromagnetic (EM) energy harvesters are particularly interesting because of their simple structure and suitability for operation at low frequency values (&lt / 10 Hz), where most vibrations exits. However, since the generated EM power and voltage is relatively low at low frequencies, high performance interface electronics is required for efficiently transferring the generated power from the harvester to the load to be supplied. The aim of this study is to design low power and efficient interface electronics to convert the low voltage and low power generated signals of the EM energy harvesters to DC to be usable by a real application. The most critical part of such interface electronics is the AC/DC converter, since all the other blocks such as DC/DC converters, power managements units, etc. rely on the rectified voltage generated by this block. Due to this, several state-of-the-art rectifier structures suitable for energy harvesting applications have been studied. Most of the previously proposed rectifiers have low conversion efficiency due to the high voltage drop across the utilized diodes. In this study, two rectifier structures are proposed: one is a new passive rectifier using the Boot Strapping technique for reducing the diode turn-on voltage values / the other structure is a comparator-based ultra low power active rectifier. The proposed structures and some of the previously reported designs have been implemented in X-FAB 0.35 &micro / m standard CMOS process. The autonomous energy harvesting systems are then realized by integrating the developed ASICs and the previously proposed EM energy harvester modules developed in our research group, and these systems have been characterized under different electromechanical excitation conditions. In this thesis, five different systems utilizing different circuits and energy harvesting modules have been presented. Among these, the system utilizing the novel Boot Strap Rectifier is implemented within a volume of 21 cm3, and delivers 1.6 V, 80 &micro / A (128 &micro / W) DC power to a load at a vibration frequency of only 2 Hz and 72 mg peak acceleration. The maximum overall power density of the system operating at 2 Hz is 6.1 &micro / W/cm3, which is the highest reported value in the literature at this operation frequency. Also, the operation of a commercially available temperature sensor using the provided power of the energy harvester has been shown. Another system utilizing the comparator-based active rectifier implemented with a volume of 16 cm3, has a dual rail output and is able to drive a 1.46 V, 37 &micro / A load with a maximum power density of 6.03 &micro / W/cm3, operating at 8 Hz. Furthermore, a signal conditioning system for EM energy harvesting has also been designed and simulated in TSMC 90 nm CMOS process. The proposed ASIC includes a highly efficient AC-DC converter as well as a power processing unit which steps up and regulates the converted DC voltages using an on-chip DC/DC converter and a sub-threshold voltage regulator with an ultra low power management unit. The total power consumption on the totally passive IC is less than 5 &micro / W, which makes it suitable for next generation MEMS-based EM energy harvesters. In the frame of this study, high efficiency CMOS rectifier ICs have been designed and tested together with several vibration based EM energy harvester modules. The results show that the best efficiency and power density values have been achieved with the proposed energy harvesting systems, within the low frequency range, to the best of our knowledge. It is also shown that further improvement of the results is possible with the utilization of a more advanced CMOS technology.

The problem of vibration induced structural faults has been a real one in engineering over the years. If left unchecked it has led to the unexpected failures of so many structures. Needless to say, this has caused both economic and human life losses. Therefore for over forty years, structural damage identification has been one of the important research areas for engineers. There has been a thrust to develop global structural damage identification techniques to complement and/or supplement the long-practised local experimental techniques. In that respect, studies have shown that vibration-based techniques prove to be more potent. Most of the existing vibration-based techniques monitor changes in modal properties like natural frequencies, damping factors and mode shapes of the structural system to infer the presence of structural damage. Literature also reports other techniques which monitor changes in other vibration quantities like the frequency response functions, transmissibility functions and time-domain responses. However, none of these techniques provide a complete identification of structural damage. This study presents a damage detection technique based on operational response monitoring, which can identify all the four levels of structural damage and be implemented as a continuous structural health monitoring technique. The technique is based on monitoring changes in internal data variability measured by a test statistic <font face="symbol">c</font>2Ovalue. Structural normality is assumed when the <font face="symbol">c</font>2Om value calculated from a fresh set of measured data is within the limits prescribed by a threshold <font face="symbol">c</font>2OTH value . On the other hand, abnormality is assumed when this threshold value has been exceeded. The quantity of damage is determined by matching the <font face="symbol">c</font>2Om value with the <font face="symbol">c</font>2Op values predicted using a benchmark finite element model. The use of <font face="symbol">c</font>2O values is noted to provide better sensitivity to structural damage than the natural frequency shift technique. The analysis carried out on a numerical study showed that the sensitivity of the proposed technique ranged from three to thousand times as much as the sensitivity of the natural frequencies. The results from a laboratory structure showed that accurate estimates of damage quantity and remaining service life could be achieved for crack lengths of less than 0.55 the structural thickness. This was due to the fact that linear elastic fracture mechanics theory was applicable up to this value. Therefore, the study achieved its main objective of identifying all four levels of structural damage using operational response changes. / Dissertation (MSc (Mechanics))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / unrestricted