The majority of condition monitoring techniques employed today consider the acquisitioning and analysis of structural responses as a means of profiling machine condition and performing fault detection. Modern research and newer technologies are driving towards non-contact and non-invasive methods for better machine characterisation. In particular, unshrouded rotors which are exposed to a full field of fluid interaction such as helicopter rotors and wind turbines, amongst others, benefit from such an approach. Current literature lacks investigations into the monitoring and detection of anomalous conditions using fluid dynamic behaviour. This is interesting when one considers that rotors of this nature are typically slender, implying that their structural behaviour is likely to be dependent on their aerodynamic behaviour and vice versa. This study sets out to investigate whether a seeded rotor fault can be inferred from the flow field. Studies of this nature have the potential to further a branch of condition monitoring techniques. It is envisaged that successful detection of rotor anomalies from the flow field will aid in better distinction between mass and aerodynamic imbalances experienced by rotor systems. Furthermore, the eventual goal is to better describe the adjustments made to helicopter rotor systems when performing rotor track and balance procedures. Time-dependent fluid dynamic data is numerically simulated around a helicopter tail rotor blade using URANS CFD with the OpenFOAM software package. Pressures are probed at locations in the field of the rotor and compared to results attained in an experimental investigation where good correlation is seen between the results. A blade is modelled with a seeded fault in the form of a single blade out of plane by 4°. Comparisons are drawn between the blade in its ‘healthy’ and ‘faulty’ configuration. It is observed that the fault can be detected by deviations in the amplitudes of the pressure signals for a single revolution at the probed locations in the field. These deviations manifest as increases in the frequency spectrum at frequencies equivalent to the rotational rate (1 per revolution frequencies). The results described are assessed for their fidelity when the pressure is probed at different locations in the domain of the rotor. Deviations in the pressure profiles over the surface of the blades are also seen for the asymmetric rotor configuration but may prove too sensitive for practical application. / Dissertation (MEng)--University of Pretoria, 2019. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

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The underground power cable failures introduce challenges to the reliability of underground residential distribution networks. Smart Grid initiatives have interest to improve equipment reliability. Asset management of several utilities are interested towards online cable insulation condition monitoring and health index evaluation. This dissertation demonstrates a new technique for online condition assessment of power cable insulation condition based on capacitor switching transient voltage measurements. While majority of utilities in USA are following corrective maintenance for MV cables, only few utilities have procedures in place for offline preventive maintenance of MV cables. The technique demonstrated in this research will enable all utilities to carry out preventive online monitoring of MV power cables. This dissertation also demonstrates that capacitive test point of cable elbow connector can be used to measure switching transients for power cables in service. This technique can be easily incorporated into existing capacitive test points of cable accessories. This technique has a potential to develop and deploy measurement units for online monitoring of power cables.

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

Failure of a machine can lead to production downtime and significant financial losses. Condition monitoring is implemented to avoid such downtime and devices can be used to collect data used for monitoring machine health. Vibration data is the most common type of data used for predicting machine failure. To reduce the need for hazardous cables, such devices are often battery-operated, but this can decrease monitoring device lifespans to less than 3 years, if non-rechargeable batteries are used. This thesis first proposes a design framework for implementing radio frequency energy harvesting (RFEH) at a network level. All of the necessary inputs and parameters to ensure the successful implementation of RFEH for a wireless sensor network are explored. A second design framework is then proposed for using RFEH as a source of energy to power devices for condition monitoring. This includes a power analysis of all device components, as well as the design details for an implementation of wireless power transfer using a wireless transmitter and receiver. A comparison of different types of energy sources for the device is given, followed by a case study, using commercially-available components. A simulation is used to analyze the trade-offs for different values of RFEH parameters, trading off the total cost of implementation with the system's lifetime, based on total energy consumed.

The power transformer is a critical equipment in which the protection process is essential for modern societies where continuous electric power supplies are required. Copper corrosion problems due to the formation of sulfur deposits on the copper windings of mineral oil-filled power transformers are considered a major issue that can lead to sudden failures, and in some cases, to costly fire and explosion accidents in the power plants. These kinds of problems are still being reported regardless of available condition monitoring (CM) parameters applied in power transformers' maintenance strategy. The currently applied CM parameters are based on three different types of technologies. The first is oil analysis focuses more on measurable variables such as measuring the concentration of the corrosive sulfur compounds in the insulating oil, evaluating the oil's capability to form sulfur deposits, and measuring an increase in the concentration of specific gases. The second is on-site electrical testing focuses on the variation of the transformer's electrical properties due to the sulfur deposits. The measurable variables used in the electrical testing are Frequency Domain Spectroscopy test and Polarization/Depolarization Current test. The last is online sensor technology using Corrosive Sulfur Sensor, where the sensor's outcome data provide information about the oil's capability to form sulfur deposits. The research problem addressed is how to establish more effective CM parameters for early detection of copper corrosion problems.  The research problem is divided into three concretized research problems: What are the strengths and weaknesses of the currently applied condition monitoring parameters? Which measurable variables could be utilized to improve the currently applied condition monitoring parameters to be more effective for early detection of copper corrosion problems? And how to establish a procedure for the condition monitoring for detecting copper corrosion? Two research methodologies were applied to answer these questions, literature review and experimental work. The literature review showed significant gaps in the currently applied CM parameters for early detection of copper corrosion problems due to incomplete data of the corrosion reaction mechanism. Therefore, qualitative and quantitative investigations in the experimental work were carried out. The most important result was finding new relevant measurable variables, i.e. hydrogen sulfide gas and toluene compound, which are by-products of corrosion reaction. These measurable variables are utilized to establish more effective CM parameters for early detection of copper corrosion problems. The main conclusion of this thesis is the importance of detection corrosion problems in the initial stage by implementing more effective CM parameters to prevent catastrophic and costly failures, reduce the negative impacts on human life and the environment, and save the economic losses. Another conclusion is the importance of regularly following the measurable variables' uptrend during transformer useful life to avoid incorrect evaluation of corrosion conditions.

No / Extensive research has been performed to investigate effective techniques, including advanced sensors and new monitoring methods, to develop reliable condition monitoring systems for industrial applications. One promising approach to develop effective monitoring methods is the application of time-frequency analysis techniques to extract the crucial characteristics of the sensor signals. This paper investigates the effectiveness of a new time-frequency analysis method based on Empirical Model Decomposition and Hilbert transform for analyzing the nonstationary cutting force signal of the machining process. The advantage of EMD is its ability to adaptively decompose an arbitrary complicated time series into a set of components, called intrinsic mode functions (IMFs), which has particular physical meaning. By decomposing the time series into IMFs, it is flexible to perform the Hilbert transform to calculate the instantaneous frequencies and to generate effective time-frequency distributions called Hilbert spectra. Two effective approaches have been proposed in this paper for the effective detection of tool breakage. One approach is to identify the tool breakage in the Hilbert spectrum, and the other is to detect the tool breakage by means of the energies of the characteristic IMFs associated with characteristic frequencies of the milling process. The effectiveness of the proposed methods has been demonstrated by considerable experimental results. Experimental results show that (1) the relative significance of the energies associated with the characteristic frequencies of milling process in the Hilbert spectra indicates effectively the occurrence of tool breakage; (2) the IMFs are able to adaptively separate the characteristic frequencies. When tool breakage occurs the energies of the associated characteristic IMFs change in opposite directions, which is different from the effect of changes of the cutting conditions e.g. the depth of cut and spindle speed. Consequently, the proposed approach is not only able to effectively capture the significant information reflecting the tool condition, but also reduces the sensitivity to the effect of various uncertainties, and thus has good potential for industrial applications.

The availability of industrial machinery is crucial to any business operating in the manufacturingsector. Mechanical failures halt production and unplanned downtime canbe disruptive and costly. Small failures can compound to serious failures which exponentiallyincreases downtime and repair costs. Therefore Identifying a degradationcondition before reaching failure is key to maintaining machine availability. On theother hand, it’s undesirable to spend resources performing maintenance that is notrequired. For these reasons a large field of academic work is dedicated to analyzingthe health of a machine, it’s remaining life and in turn preventing failures. This thesis analyses data from a tube straightening machine used in the steel industrywith the goal of implementing a condition monitoring strategy. The data comes froma real world application provided by a multinational manufacturer of steel products.It was obtained using the existing sensors and data acquisition system. The projectserves as a study of the existing infrastructure (available sensors) and it’s suitability forimplementing a condition monitoring strategy. The work is the first step in a largerstudy and does not attempt to perform any implementation or fault identification. Ina broad sense the aim of the project is to identify relationships and patterns in the datathat could be varying with time as the machine degrades. The data consists of twelve channels taken over a two week duration. It is prepossessedto isolate periods where the machine is operating and separated into cycles. Each ofthese is then further processed to extract time and frequency domain features. Thefeatures within each channel are compared with each other using the R2 coefficientof determination to find combinations that are correlated. A semi automated processis used to select the feature combinations. The same process is performed betweensignals for each feature. A number of linear regression models are created based on the results from the correlatedfeatures as well as some multivariate models. These are then compared usinga goodness of fit metric, Normalized Root Mean Square Error (NRMSE). Potentialclustering of machine states are highlighted based on observations in the feature combinations.The conclusions drawn from this study include identification of correlationsbetween signals, potential non-linear relationships and suggestions for future data collectionand analysis going forward. No one feature was identified as correlated betweenall signals.

The concept of using the vibration transmitted through the structure of space systems whilst they are in flight for monitoring purposes is proposed and analysed.The performed patent review seems to indicate that this technique is not currently used despite being, in principle, a good way to obtain valuable knowledge about the spacecraft’s condition. Potential sources of vibration were listed and some of them were down-selected via a trade-off analysis for implementation in a numerical model of a CubeSat structure. Models were proposed for the sources chosen and implemented in the Ansys Workbench software, along with a simplified structure designed to be representative of a generic picosatellite mission.The results confirmed very different amplitude and frequency ranges for the sources of interest, which would make it difficult to monitor them with one type of sensor.Basic system requirements for accelerometer operating under space conditions were derived and commercial sources were identified as already having the technologies needed.The conclusion was a positive evaluation of the overall concept, although revising negatively the initial expectations for its performance due to the diversity encountered in the sources.

Identifying faults in machinery before they cause critical failure is the core purpose of condition monitoring. This report gives a background to condition monitoring and outlines the current state of research in the field, and its most important theoretical components. It also describes parallels between sustainability goals and condition monitoring. Further, a method for creating a statistical model to predict faults in machines is described. The proposed model is machine specific and is evaluated on three cases. The model’s predictions is then compared to general limit values provided by an ISO-standard. The model successfully detected faults in time for repair in two of the three cases where the ISO-standard did not. The third case was a control and featured a machine with no issues. Neither our model nor the ISO-standard falsely predicted a fault on the control. From the results of the three cases it is concluded that the proposed machine specific approach is required for reliably predicting faults. / Syftet med tillståndsövervakning är att identifiera fel före de orsakar yterligare fel. Denna rapport ger en bakgrund till tillståndsövervakning samt redogör för den aktuella forskningen och de mest centrala teoretiska grunderna inom området. Rapporten beskriver även hur tillståndsövervakningen bidrar till de globala hållbarhetsmålen samt föreslås en konkret metod för tillämpning av tillståndsövervakning. Den föreslagna modellen är maskinspecifik och grundar sig på statistiska avvikelser av vibrationsdata som samlas från maskiner i ett välfungerande tillstånd. Modellen appliceras på tre olika maskiner och resultaten jämförs med ISO-standarden som har definierat generella gränsvärden för flera maskintyper. Den föreslagna modellen visar lovande resultat genom att upptäcka fel som ISO-standarden missade. Av resultaten från fallen dras slutsatsen att en generella gränsvärden inte är tillräckligt, utan en maskinspecifik metod krävs för att, på ett pålitligt sätt, detektera fel.