Free vibration of composite piezoelectric transducers is analyzed by a modified two-dimensional axisymmetric finite element in this study. The modified finite element is formulated based on assumed displacement fields and electric potential, and is capable for three-dimensional analyses of vibration of axisymmetric composite piezoelectric structures. Different arrangements and radius of piezoelectric disk and metal ring, with various thicknesses will be considered and first radial vibration frequencies are derived and their mode shapes are demonstrated, which might be helpful for the design of radial composite transducers. Also, the results of the present study will be compared with those by other methods.

Vibrations in machines increase friction on moving parts which cause chafing that will tear down the fabric of the machine components when given time, thus monitoring and analysis of machine vibrations are important for preventive maintenance. Vibration analysis utilizes time domain as well as frequency domain analysis for which there have been analog solutions for quite some time. This work has been about moving a predominantly analog mixed signal system onto an FPGA and making it mostly digital. Vibration analysis on an FPGA have its own challenges and benefits compared to other methods. The inherent parallelism of the FPGA makes it suitable for high performance signal analysis. This report shows through two proof-of-concept solutions that the translation of a predominantly analog system is viable, economic and can deliver improved performance. The two solutions have utilized two different units from Xilinx, the Spartan-6 FPGA and the Zynq-7000 system on chip FPGA. The solution implemented on Spartan-6 produces a result in 9.32 ms and the other implementation based on Zynq-7000 produces a result in 9.39 ms, which is more than a 10-fold increase in performance of the current system. The results obtained show that both solutions can perform the calculations for the proof of concept within 20% of the allotted time. Costs of both solutions as well as other qualities of each solution are presented in this paper.

published_or_final_version / Civil and Structural Engineering / Doctoral / Doctor of Philosophy

Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations. Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components. Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions. To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed: 1. An amplitude demodulation differentiation (ADD) method, 2. A calculus enhanced energy operator (CEEO) method, 3. A higher order analytic energy operator (HO_AEO) approach, and 4. A higher order energy operator fusion (HOEO_F) technique. The proposed methods have been evaluated using both simulated and experimental data.

Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations. Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components. Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions. To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed: 1. An amplitude demodulation differentiation (ADD) method, 2. A calculus enhanced energy operator (CEEO) method, 3. A higher order analytic energy operator (HO_AEO) approach, and 4. A higher order energy operator fusion (HOEO_F) technique. The proposed methods have been evaluated using both simulated and experimental data.

This paper presents an exploratory study of aircraft engine fault indicators obtained from sound and vibration recordings. Observing time sequence and frequency spectra of the recordings, a simple yet cost-effective method of detecting engine fault is achieved. A detailed discussion of the study performed, ranging from the hardware and software selection, initial tests, and discoveries made in the time domain and subsequently in the frequency domain will be presented.

Acoustic emission (AE) is one of many available technologies for condition health monitoring and diagnosis of rotating machines such as bearings. In recent years there have been many developments in the use of Acoustic Emission technology (AET) and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on high speed machinery. Unlike conventional technologies such as oil analysis, motor current signature analysis (MCSA) and vibration analysis, AET has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This research presents an experimental investigation that is aimed at developing a mathematical model and experimentally validating the influence of operational variables such as film thickness, rotational speed, load, power loss, and shear stress for variations of load and speed conditions, on generation of acoustic emission in a hydrodynamic bearing. It is concluded that the power losses of the bearing are directly correlated with acoustic emission levels. With exponential law, an equation is proposed to predict power losses with reasonable accuracy from an AE signal. This experimental investigation conducted a comparative study between AE and Vibration to diagnose the rubbing at high rotational speeds in the hydrodynamic bearing. As it is the first known attempt in rotating machines. It has been concluded, that AE parameters such as amplitude, can perform as a reliable and sensitive tool for the early detection of rubbing between surfaces of a hydrodynamic bearing and high speed shaft. The application of vibration (PeakVue) analysis was introduced and compared with demodulation. The results observed from the demodulation and PeakVue techniques were similar in the rubbing simulation test. In fact, some defects on hydrodynamic bearings would not have been seen in a timely manner without the PeakVue analysis. In addition, the application of advanced signal processing and statistical methods was established to extract useful diagnostic features from the acquired AE signals in both time and frequency domain. It was also concluded that the use of different signal processing methods is often necessary to achieve meaningful diagnostic information from the signals. The outcome would largely contribute to the development of effective intelligent condition monitoring systems which can significantly reduce the cost of plant maintenance. To implement these main objectives, the Sutton test rig was modified to assess the capability of AET and vibration analysis as an effective tool for the detection of incipient defects within high speed machine components (e.g. shafts and hydrodynamic bearings). The first chapter of this thesis is an introduction to this research and briefly explains motivation and the theoretical background supporting this research. The second and third chapters, summarise the relevant literature to establish the current level of knowledge of hydrodynamic bearings and acoustic emission, respectively. Chapter 4 describes methodologies and the experimental arrangements utilized for this investigation. Chapter 5 discusses different NDT diagnosis. Chapter 6 reports on an experimental investigation applied to validate the relationship between AET on operational rotating machines, such as film thickness, speed, load, power loss, and shear stress. Chapter 7 details an investigation which compares the applicability of AE and vibration technologies in monitoring a rubbing simulation on a hydrodynamic bearing.

Solar energy harvesting using photovoltaic (PV) systems has gained popularity in recent years due to its relative ease of use and its cost efficiency compared to the rest of the clean energy sources. However, to further expand the application of PV systems requires making them more desirable than the other competitive energy sources. The improvement of safety and cost efficiency are requisites for further popularization of PV system application. To satisfy these requisites it is necessary to optimally design the system against the environmental conditions. Wind action is one of the main ambient loads affecting the performance of PV systems. This dissertation aims to investigate the effects of wind load on residential scale roof mounted PV panels and their supporting structures as well as evaluating the structural response of the system to the wind-induced vibration. To achieve these goals, several full- and large-scale experimental tests were performed at the Wall of Wind Experimental Facility at Florida International University (FIU). The wind effects on different PV system and roof configurations were investigated in these tests. The results shed light on the most influential parameters affecting the wind pressures acting on the PV panel surface. In addition, the findings are presented in the form of design pressure coefficients for adoption to future building codes and wind standards. The second phase of the physical testing included the investigation of the actual response of the PV system to the wind action. Because of the dynamic properties of the PV panel, it was expected that the wind induced vibration can affect the dynamic response of the system including the acceleration at the panel surface and support reactions at the racking system to roof interface. To test this theory, two different models of the system were developed, one with the real PV panels and the other one with wooden rigid panels. Comparing the results, it was concluded that the dynamic response of the system was not considerably affected by wind-induced fluctuations. Finally, and to better understand the dynamic response of the system, an analytical model was developed using ANSYS and dynamic analysis was carried out using as input the wind induced pressure data acquired from the physical testing. At the first step, the analytical model was verified by comparing the analytical modal frequencies to the experimental natural frequencies obtained from the hammer test. It was shown that the analytical model can well represent the dynamic properties of the actual model. However, once the reaction output was compared to the loadcell data recorded during the wind tunnel test, there was a considerable discrepancy between the results. It was assumed that the deflection of the supporting structure caused this discrepancy. This assumption was verified and it was concluded that the supporting structure can significantly influence the dynamic response of the system.

This paper presents the layout optimization of passive constrained layer damping (PCLD) treatment for vibration control of cylindrical shells under a broadband force excitation. The equations governing the vibration responses are derived using the energy approach and assumed-mode method. These equations provided relationship between the integrated displacement response over the whole structural volume, i.e. the structural volume displacement (SVD), of a cylindrical shell to structural parameters of base structure and multiple PCLD patches, Genetic algorithms (GAs) based penalty function method is employed to find the optimal layout of rectangular PCLD patches with minimize the maximum displacement response of PCLD-treated cylindrical shells. Optimization solutions of PCLD patches’ locations and shape are obtained under the constraint of total amount of PCLD in terms of percentage added weight to the base structure. Examination of the optimal layouts reveals that the patches tend to increase their coverage in the axial direction and distribute over the whole surface of the cylindrical shell for optimal control of the structural volume displacement. / Singapore-MIT Alliance (SMA)

The development of condition monitoring and fault diagnosis systems for wind turbines has received considerable attention in recent years. With wind playing an increasing part in Canada’s electricity demand from renewable resources, installations of new wind turbines are experiencing significant growth in the region. Hence, there is a need for efficient condition monitoring and fault diagnosis systems for wind turbines. Gearbox, as one of the highest risk elements in wind turbines, is responsible for smooth operation of wind turbines. Moreover, the availability of the whole system depends on the serviceability of the gearbox. This work presents signal processing and soft computing techniques to increase the detection and diagnosis capabilities of wind turbine gearbox monitoring systems based on vibration signal analysis. Although various vibration based fault detection and diagnosis techniques for gearboxes exist in the literature, it is still a difficult task especially because of huge background noise and a large solution search space in real world applications. The objective of this work is to develop a novel, intelligent system for reliable and real time monitoring of wind turbine gearboxes. The developed system incorporates three major processes that include detecting the faults, extracting the features, and making the decisions. The fault detection process uses intelligent filtering techniques to extract faulty information buried in huge background noise. The feature extraction process extracts fault-sensitive and vibration based transient features that best describe the health of the gearboxes. The decision making module implements probabilistic decision theory based on Bayesian inference. This module also devises an intelligent decision theory based on fuzzy logic and fault semantic network. Experimental data from a gearbox test rig and real world data from wind turbines are used to verify the viability, reliability, and robustness of the methods developed in this thesis. The experimental test rig operates at various speeds and allows the implementation of different faults in gearboxes such as gear tooth crack, tooth breakage, bearing faults, iv and shaft misalignment. The application of hybrid conventional and evolutionary optimization techniques to enhance the performance of the existing filtering and fault detection methods in this domain is demonstrated. Efforts have been made to decrease the processing time in the fault detection process and to make it suitable for the real world applications. As compared to classic evolutionary optimization framework, considerable improvement in speed has been achieved with no degradation in the quality of results. The novel features extraction methods developed in this thesis recognize the different faulty signatures in the vibration signals and estimate their severity under different operating conditions. Finally, this work also demonstrates the application of intelligent decision support methods for fault diagnosis in gearboxes. / UOIT