Rolling element bearing failure can cause problems for industries ranging from mild inconveniences such as simple replacement to catastrophic damage such as large production-line equipment failure. Rolling element bearing failure has plagued industries for many years. Bearings are currently monitored to determine whether or not there is a defect in the bearing, but the remaining lifetime of the bearing remains unknown. This research estimates the bearings remaining lifetime through digital signal processing in conjunction with a modified version of Pariss equationa fatigue-failure equation well known in rotating machinery prognostics. An energy quantity, coined the Power Spectrum Value (PSV), is the maximum amplitude of the frequencies within a relatively small band around the resonant frequency of the system. The current PSV is estimated and updated using a chronologically weighted least squares algorithm. It is this PSV which is implemented in the modified Paris equation to determine the remaining lifetime of the bearing. This research presents a non-intrusive method of determining the lifetime of the bearing so that the bearings utility is maximized and reactive maintenance procedures are minimized.

The objective of this research is to advance the field of condition monitoring of electrolytic capacitors used in power electronics circuits. The construction process of an electrolytic capacitor is presented. Descriptions of various kinds of faults that can occur in an electrolytic capacitor are discussed. The methods available to detect electrolytic capacitor faults are discussed. The effects of the capacitor faults on the capacitor voltage and current waveforms are investigated through experiments. It is also experimentally demonstrated that faults in the capacitor can be detected by monitoring the capacitor voltage and current. Various ESR estimation based detection techniques available to detect capacitor failures in power electronics circuits are reviewed. Three algorithms are proposed to track and detect capacitor failures: an FFT based algorithm, a system modeling based detection scheme, and finally a parameter estimation based algorithm. The parameter estimation based algorithm is a low-cost real-time scheme, and it is inexpensive to implement. Finally, a detailed study is carried out to understand the failure mechanism of an electrolytic capacitor due to inrush current.

The main types of faults studied in the literature are commonly categorized as electrical faults and mechanical faults. In addition to well known faults, the performance of a diagnostic algorithm and its operational reliability in harsh environments has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been investigated under 1) potential motor feedback error; 2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4) universal application of diagnostic scheme in industry. Low cost and flexible implementation strategies are also presented. 1) Signature-based diagnosis has been performed utilizing the speed feedback information which is used to determine fault characteristic frequency. Therefore, feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise interferences. In this dissertation, the performance under feedback error is analyzed in detail and error compensation algorithms are proposed. 2) Fault signatures are commonly small where the amplitude is continuously being interfered with motor noise. Even though a decision is based on the signature, the detection error will not be negligible if the signature amplitude is within or close to the noise floor because the boundary noise level non-linearly varies and, hence, is quite ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a threshold design strategy is presented to discriminate potential noise content in diagnosis. 3) The compensating procedure of speed feedback errors and electrical machine current noise, characteristics which are basically non-stationary random variables, requires an exhaustive tracking effort. In this dissertation, the effective diagnosis implementation strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis purposes. In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents, a general diagnosis algorithm is derived which can be applied to any diagnostic scheme as a basic tool.

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Air gap eccentricity fault is one of the major faults that afflict the life and performance of rotating machines. Eccentricity fault, in the worst case, causes a stator rotor rub. Thus, a condition monitoring scheme to identify eccentricity fault at its initial stage is necessary. The most widely practised air gap monitoring schemes for synchronous machines are expensive and invasive sensors based. This work has focussed on developing an inexpensive, non-invasive, air gap monitoring technique especially for salient pole synchronous machines. Motor current signature analysis has been mostly preferred for the above mentioned purpose. By monitoring the frequency spectrum of the machine’s current, faulty condition can be isolated provided the fault specific frequency components are known beforehand. The research work, therefore, has developed a specific permeance function using binomial series for salient pole machines that can be used to identify eccentricity specific harmonic components in the line current spectrum. Then by performing the magneto-motive force – specific permeance analysis the characteristic frequency components have been predicted. In order to validate the prediction as well as to identify a trend in the variation of these harmonic components with changing levels of eccentricity, mathematical models of a three phase reluctance synchronous machine and a three phase salient pole synchronous machine based on modified winding function approach have been developed. The models have been made to incorporate static, dynamic and mixed eccentricity conditions of varying severity. Also time stepped finite element based models have been simulated in Maxwell-2D to verify the theoretical predictions. With the help of eccentrically cut bushings, experiments were then conducted in the laboratory to corroborate the proposed eccentricity detection scheme. It has been observed that non-idealities such as supply time harmonics, machine constructional asymmetry, supply voltage unbalance etc. negatively impact the diagnostic technique. Consequently, a residual estimation based fault detection scheme has been implemented successfully to distinguish eccentricity fault from healthy condition. Moreover, detection logic have been put forth to discriminate the type of eccentricity and to estimate the severity of the fault. / Graduate

This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emission signals, it is considered a multi-sensor system. This has the advantage that not only do the two sensors provide increased reliability they also permit a larger range of rotating speeds to be monitored successfully. When more than one sensor is used, if one fails to work properly the other is still able to provide adequate monitoring. Vibration techniques are suitable for higher rotating speeds whilst acoustic emission techniques for low rotating speeds. Vibration techniques investigated in this research concern the use of the continuous wavelet transform (CWT), a joint time- and frequency domain method, This gives a more accurate representation of the vibration phenomenon than either time-domain analysis or frequency- domain analysis. The image processing technique, called binarising, is performed to produce binary image from the CWT transformed image in order to reduce computational time for classification. The back-propagation neural network (BPNN) is used for classification. The AE monitoring techniques investigated can be categorised, based on the features used, into: 1) the traditional AE parameters of energy, event duration and peak amplitude and 2) the statistical parameters estimated from the Weibull distribution of the inter-arrival times of AE events in what is called the STL method. Traditional AE parameters of peak amplitude, energy and event duration are extracted from individual AE events. These events are then ordered, selected and normalised before the selected events are displayed in a three-dimensional Cartesian feature space in terms of the three AE parameters as axes. The fuzzy C-mean clustering technique is used to establish the cluster centres as signatures for different machine conditions. A minimum distance classifier is then used to classify incoming AE events into the different machine conditions. The novel STL method is based on the detection of inter-arrival times of successive AE events. These inter-arrival times follow a Weibull distribution. The method provides two parameters: STL and L63 that are derived from the estimated Weibull parameters of the distribution's shape (y), characteristic life (0) and guaranteed life (to). It is found that STL and 43 are related hyperbolically. In addition, the STL value is found to be sensitive to bearing wear, the load applied to the bearing and the bearing rotating speed. Of the three influencing factors, bearing wear has the strongest influence on STL and L63. For the proposed bearing condition monitoring system to work, the effects of load and speed on STL need to be compensated. These issues are resolved satisfactorily in the project.

Condition monitoring involves taking measurements on an induction motor while it is operating in order to detect faults. For this purpose normally a single sensor type, for example current is used to detect broken rotor bar using fault frequency components only under the full-load condition or a limited number of load cases. The correlations among the different types of sensors and their ability to diagnose single and multiple faults over a wide range of loads have not been the focused in previous research. Furthermore, to detect different faults in machines using any fault frequency components, it is important to investigate the variability in its amplitude to other effects apart from fault severity and load. This area has also often been neglected in the literature on condition monitoring. The stator current and axial flux have been widely used as suitable sensors for detecting different faults i.e. broken rotor bar and eccentricity faults in motors. Apart from detecting the broken rotor bar faults in generalized form, the use of instantaneous power signal has often been neglected in the literature condition monitoring. This thesis aims to improve machine condition monitoring and includes accurate and reliable detection of single and multiple faults (faults in the presence of other faults) in induction machines over a wide range of loads of rated output by using current, flux and instantaneous power as the best diagnostic medium. The research presents the following specific tasks: A comprehensive real database from non–invasive sensor measurements, i.e. vibration measurements, axial flux, 3-phase voltage, 3-phase current and speed measurements of induction motor is obtained by using laboratory testing on a large set of identical motors with different single and multiple faults. Means for introducing these faults of varying severity have been developed for this study. The collected data from the studied machines has been analysed using a custom-written analysis programme to detect the severity of different faults in the machines. This helps to improve the accuracy and reliability in detecting of single and multiple faults in motors using fault frequency components from current, axial flux and instantaneous power spectra. This research emphasises the importance of instantaneous power as a medium of detecting different single and multiple faults in induction motor under varying load conditions. This enables the possibility of obtaining accurate and reliable diagnostic medium to detect different faults existing in machines, which is vital in providing a new direction for future studies into condition monitoring. Another feature of this report is to check the variability in healthy motors due to: test repeatability, difference between nominally identical motors, and differences between the phases of the same motor. This has been achieved by conducting extensive series of laboratory tests to examine fault frequency amplitudes versus fault severity, load, and other factors such as test repeatability and machine phases. The information about the variations in the amplitudes of the fault frequency components is used to check the accuracy and reliability of the experimental set-up, which is necessary for the practical application of the results to reliably detect the different faults in the machines reliably. Finally, this study also considers the detection of eccentricity faults using fault frequency amplitudes as a function of average eccentricity, instead of as a function of load under different levels of loading. This has not been reported in previous studies. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1298314 / Thesis (Ph.D.)-- University of Adelaide, School of Electrical and Electronic Engineering, 2008

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.

Carbon emissions generated by the building sector have come under stricter limits with the amendments to Approved Document L: Conservation of Fuel and Power of the building regulations for England and Wales. Building designs are now checked to ensure that new constructions have the designed capabilities to operate with a higher standard of efficiency. However, there are currently no means of ensuring that the mandatory improvements in design and construction are actually translating into real life improvements during the actual operation of the building. Assessment methodologies such as the Display Energy Certificate are applied annually. The large interval between audits has the potential risk that poor performance may go unnoticed for prolonged periods of time. Real time assessment of energy performance that is linked to legislative requirements would aid the process of ensuring reductions in carbon emissions occur in reality. Evaluating the energy performance in real time is not a straight forward task; commercial buildings are complex nonlinear dynamic systems with a number of operating states, functions and features. These factors need to be taken into consideration for the fair appraisal of energy performance. Condition monitoring has been applied extensively to the field of machine health, in which the state of a system is determined through measuring the parameters that are indicative of its health. Within this thesis, a unique method of real time energy performance has been developed along with the implementation of two condition monitoring strategies for the purposes of state evaluation and fault detection and diagnosis. Kernel based dimensionality techniques have recently gained popularity as a means of modelling nonlinear systems. It was found that the application of nonlinear condition monitoring strategies for determination of building state was proficient in determining slow developing faults and abrupt changes in building state. However, the occurrences of non-acceptable incipient changes in state were harder to detect. Hence the state evaluation techniques were complemented with component level fault detection and diagnosis techniques. These techniques have the combined ability to address the requirement for assessing the state of operation within a building to allow for fair appraisal of the energy performance.

Abstract The aim of this research work is to develop a tool for condition monitoring of squirrel-cage motors using axial magnetic flux measurements, and to design a diagnostics system for electrical motors. The basic theory of the measurements and systems was found through literature reviews and was further developed from the experimental results of this research work. Fluxgate magnetometers and Hall effect sensors are not reliable enough for condition monitoring purposes, but measurements by flux coil sensors can reach adequate reliability. The useful frequency area of the flux coil sensor is from about 0.2 Hz to 15 kHz, an area is well applicable for condition monitoring of squirrel-cage motors. Output voltage is frequency dependent, increasing towards higher frequencies. Sufficient sensitivity is usually reached by a flux coil sensor having a diameter of about 30 cm and the number of turns of about 200. Sensitivity can be improved by increasing the diameter or number of turns of the coil. The sensor should be placed axially centred on the end of the motor, and measurements should be made with the loaded motor in steady operation. Output voltage is typically from the microvolt to millivolt level, however, installation inside the motor can increase it from tens of millivolts to some volts. The dynamic resolution requirement of measurement is about 70 dB and the highest line resolution needed for the spectrum analysis is about 3200 lines. Time base signal can be used to study rapid disturbances of flux caused by mechanical loading or switching of the frequency converter. Various motor failures cause specific variation to the frequency distribution of flux, so spectrum analysis is well applicable for condition monitoring. Reference measurement of each motor is required because stator winding factors, installation tolerances, operating conditions and mechanical load affect leakage flux. A broken rotor bar failure can be detected from the amplitude difference between the supply frequency and its rotor bar induced sideband. A broken rotor end ring failure can be detected by the amplitude difference between the slip frequency and the supply frequency. However, it was found that the stator current spectrum is a more reliable method of detecting both these rotor failures. The supply voltage asymmetry can also be evaluated by specific sidebands of axial flux. Turn to turn failure of the stator winding was most reliably detected by sidebands around the rotor slot pass frequencies. Equations for frequency converter supplied motors are the bases for similar equations, but the supply frequency is replaced by the output current frequency of the converter. The developed diagnostics system design for condition monitoring of ac motors includes stator current, flux coil, temperature, vibration, partial discharge, bearing current and voltage measurements. At the system diagnosis stage these measurable signals are divided to time base and frequency base signals and for each of them a fault indicator is determined. For flux coil measurements four fault indicators were found: rotor bar failure ratio, rotor end ring failure ratio, stator winding turn to turn ratio and supply voltage asymmetry operation ratio. With these failure indicators we determine failure location, state and cause. From this information a lifetime prediction of the motor is made. The results of this work are used to analyse flux coil measurements of squirrel-cage motors. In addition the research has led to a special application to monitor electric motors using an on-line condition monitoring system for paper machines and power plants.