Effects of modern variable frequency drive on the reliability of an old crane induction motor

The work aims to analyze the possibilities of utilizing old crane driving AC induction motors in modern pulse-width-modulated variable frequency drives. Bearing currents and voltage stresses are the two main problems associated with modern IGBT inverters, and they may cause premature failure of an old induction motor. The origins of these two problems are studied. An analysis of the mechanism of bearing failure is proposed. Certain types of bearing currents are considered in detail. The most effective and economical means are chosen for bearing currents mitigation. Transient phenomena of cables and mechanism of over voltages occurring at motor terminals are studied in the work. The weakest places of the stator winding insulation system are shown and recommendations are given considering the mitigation of voltage stresses. Only the most appropriate and cost effective preventative methods are chosen for old motor drives. Rewinding of old motors is also considered.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Market Opportunities for Paper Industry in Radio Frequency Identification

The paper industry has been experiencing remarkable structural changes since paper demand growth has ceased and some markets are declining. One reason behind the declined demand is the Internet, which has partially substituted the newspaper as a source of information. Paper products alone can no longer provide livelihood, and the paper industry has to find new business areas. In this research, we studied radio frequency identification (RFID), and the market opportunities it could provide for paper industry. The research combined a quantitative industry analysis and qualitative interviews. RFID is a growing industry in the beginning of its life cycle, in which value chains and technologies still evolve significantly. The industry is going to concentrate on the future, and in the long term RFID-identifiers will probably be printed on paper substrate or directly onto products. Paper industry has the chance to enter the RFID industry, but it has to obtain the required competences, for example through acquisitions. The business potential RFID offers to paper industry is inadequate, and while reviewing new strategic options, the paper industry must consider more options, for example the entire printed intelligence.

Radio Frequency Measurement Method for Detecting Bearing Currents in Induction Motors

Induction motors are widely used in industry, and they are generally considered very reliable. They often have a critical role in industrial processes, and their failure can lead to significant losses as a result of shutdown times. Typical failures of induction motors can be classified into stator, rotor, and bearing failures. One of the reasons for a bearing damage and eventually a bearing failure is bearing currents. Bearing currents in induction motors can be divided into two main categories; classical bearing currents and inverter-induced bearing currents. A bearing damage caused by bearing currents results, for instance, from electrical discharges that take place through the lubricant film between the raceways of the inner and the outer ring and the rolling elements of a bearing. This phenomenon can be considered similar to the one of electrical discharge machining, where material is removed by a series of rapidly recurring electrical arcing discharges between an electrode and a workpiece. This thesis concentrates on bearing currents with a special reference to bearing current detection in induction motors. A bearing current detection method based on radio frequency impulse reception and detection is studied. The thesis describes how a motor can work as a “spark gap” transmitter and discusses a discharge in a bearing as a source of radio frequency impulse. It is shown that a discharge, occurring due to bearing currents, can be detected at a distance of several meters from the motor. The issues of interference, detection, and location techniques are discussed. The applicability of the method is shown with a series of measurements with a specially constructed test motor and an unmodified frequency-converter-driven motor. The radio frequency method studied provides a nonintrusive method to detect harmful bearing currents in the drive system. If bearing current mitigation techniques are applied, their effectiveness can be immediately verified with the proposed method. The method also gives a tool to estimate the harmfulness of the bearing currents by making it possible to detect and locate individual discharges inside the bearings of electric motors.

Cost estimation framework for modular product

Cost estimation is an important, but challenging process when designing a new product or a feature of it, verifying the product prices given by suppliers or planning a cost saving actions of existing products. It is even more challenging when the product is highly modular, not a bulk product. In general, cost estimation techniques can be divided into two main groups - qualitative and quantitative techniques - which can further be classified into more detailed methods. Generally, qualitative techniques are preferable when comparing alternatives and quantitative techniques when cost relationships can be found. The main objective of this thesis was to develop a method on how to estimate costs of internally manufactured and commercial elevator landing doors. Because of the challenging product structure, the proposed cost estimation framework is developed under three different levels based on past cost information available. The framework consists of features from both qualitative and quantitative cost estimation techniques. The starting point for the whole cost estimation process is an unambiguous, hierarchical product structure so that the product can be classified into controllable parts and is then easier to handle. Those controllable parts can then be compared to existing past cost knowledge of similar parts and create as accurate cost estimates as possible by that way.

Sensor Fusion of Proprioception, Force and Vision in Estimation and Robot

Sensor-based robot control allows manipulation in dynamic environments with uncertainties. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. In this thesis, methods for fusing proprioception, force and vision together are proposed. Making assumptions of object shape and modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. The fusion of force and visual measurements makes it possible to estimate the pose of a moving target with an end-effector mounted moving camera at high rate and accuracy. The proposed approach takes the latency of the vision system into account explicitly, to provide high sample rate estimates. The estimates also allow a smooth transition from vision-based motion control to force control. The velocity of the end-effector can be controlled by estimating the distance to the target by vision and determining the velocity profile giving rapid approach and minimal force overshoot. Experiments with a 5-degree-of-freedom parallel hydraulic manipulator and a 6-degree-of-freedom serial manipulator show that integration of several sensor modalities can increase the accuracy of the measurements significantly.

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Hybrid du/dt-filter in frequency converter applications

The aim of the thesis is to investigate the hybrid LC filter behavior in modern power drives; to analyze the influence of such a du/dt filter on the control system stability. With the implementation of the inverter output RLC filter the motor control becomes more complicated. And during the design process the influence of the filter on the motor should be considered and the filter RLC parameters should be constrained.

Mobile system for pump working point estimation

In the current economy situation companies try to reduce their expenses. One of the solutions is to improve the energy efficiency of the processes. It is known that the energy consumption of pumping applications range from 20 up to 50% of the energy usage in the certain industrial plants operations. Some studies have shown that 30% to 50% of energy consumed by pump systems could be saved by changing the pump or the flow control method. The aim of this thesis is to create a mobile measurement system that can calculate a working point position of a pump drive. This information can be used to determine the efficiency of the pump drive operation and to develop a solution to bring pump’s efficiency to a maximum possible value. This can allow a great reduction in the pump drive’s life cycle cost. In the first part of the thesis, a brief introduction in the details of pump drive operation is given. Methods that can be used in the project are presented. Later, the review of available platforms for the project implementation is given. In the second part of the thesis, components of the project are presented. Detailed description for each created component is given. Finally, results of laboratory tests are presented. Acquired results are compared and analyzed. In addition, the operation of created system is analyzed and suggestions for the future development are given.