Drift Removal in Plant Electrical Signals via IIR Filtering Using Wavelet Energy

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Plant electrical signals often contains low frequency drifts with or without the application of external stimuli. Quantification of the randomness in plant signals in a stimulus-specific way is hindered because the knowledge of vital frequency information in the actual biological response is not known yet. Here we design an optimum Infinite Impulse Response (IIR) filter which removes the low frequency drifts and preserves the frequency spectrum corresponding to the random component of the unstimulated plant signals by bringing the bias due to unknown artifacts and drifts to a minimum. We use energy criteria of wavelet packet transform (WPT) for optimization based tuning of the IIR filter parameters. Such an optimum filter enforces that the energy distribution of the pre-stimulus parts in different experiments are almost overlapped but under different stimuli the distributions of the energy get changed. The reported research may popularize plant signal processing, as a separate field, besides other conventional bioelectrical signal processing paradigms.This work was supported by EU FP7 project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

Active tool vibration control and tool condition monitoring using a self-sensing actuator

Dissertation (PhD (Mechanical Engineering))--University of Pretoria, 2016.The studies consist of two simulations of active tool vibration control and tool condition monitoring respectively and a hardware-in-the-loop laboratory demonstration of active tool vibration control typical to turning. Besides reducing the restricting effects of tool vibrations on productivity, work-piece surface finish and tool life, it is desirable to handle lack of space at the tool tip and the cost of control systems in turning processes in an effective way. These two aspects are here considered by means of the concept of a self-sensing actuator (SSA) in the simulation of tool vibration control. In the simulation an IIRfilter represents the structure of the passive tool holder. A known pre-filtering technique was applied to the error in a feedback filtered-x LMS algorithm to maintain the stability of the control system. The self-sensing path is modelled and illustrated. The IIR-filters and their inverses were used for modelling this path, with equations resulting from the nodal displacements associated with nodes that have forces acting on them. For the cantilever type structure a considerable reduction of 93% of the displacement r.m.s. values of the tool tip, was obtained when using this control system. Signal processing using orthogonal cutting force components for tool condition monitoring (TCM) has established itself in literature. Single axis strain sensors however limit TCM to linear combination of cutting force components. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. Processing of a linear combination of cutting force components to the reference case of processing orthogonal components is compared. The same time-delay neural network structure has been applied in each case. Reconstruction of the dynamic force acting at the tool tip in a turning process is described. By simulation this dynamic force signal was applied to a model of the tool holder equipped with a SSA. Using a wavelet packet analysis, wear-sensitive features were extracted. The probability of a difference less than 5 percentage points between the flank wear estimation errors of abovementioned two processing strategies is at least 95 %. This study proves the basic concept of adaptive feedback active vibration control in combination with a self-sensing actuator to control tool vibrations. The structure involved is representative of a tool post clamped tool holder. The advantages that adaptive control hold when applied to non-stationary vibrations motivate this investigation. Secondly the dual functionality of a piezoelectric element is utilized for system simplification. Actuator linearization measures are considered and a model for the system’s forward path identified. The tool vibrations signal for this work is of 100 Hz bandwidth around the representative tool holder bending mode. A downscaled force based on real cutting force characteristics was artificially applied to the representative tool holder. Limited form locking contact with the tool holder restricted the actuator’s reaction to compressive forces only. Results of up to 70% attenuation of vibration induced strain on the SSA were achieved. This method clearly shows concept viability.Mechanical and Aeronautical EngineeringPhD (Mechanical Engineering)Unrestricte

On-line measurement of partial discharges in high voltage rotating machines.

The on-line condition monitoring of rotating machines is given paramount importance, particularly in Oils and Gas industries where the financial implications of machine shutdown is very high. This project work was directed towards the on-line condition monitoring of high voltage rotating machines by detection of partial discharges (PD) which are indicative of stator insulation degradation. Partial discharge manifests itself in various forms which can be detected using various electrical and non-electrical techniques. The electrical method of detecting small current pulses generated by PD using a Rogowski coil as a sensor has been investigated in this work. Dowding & Mills, who are commercially involved in the condition monitoring of rotating machines, currently use a system called StatorMonotor® for PD detection. The research is intended to develop a new partial discharge detection system that will replace the existing system which is getting obsolete. A three phase partial discharge detection unit was specified, designed and developed that is capable of filtering, amplifying and digitising the discharge signals. The associated data acquisition software was developed using LabVIEW software that was capable of acquiring, displaying and storing the discharge signals. Additional software programs were devised to investigate the removal of external noise. A data compression algorithm was developed to store the discharge data in an efficient manner; also ensuring the backward compatibility to the existing analysis software. Tests were performed in laboratory and on machines on-site and the results are presented. Finally, the data acquisition (DAQ) cards that used the PCMCIA bus was replaced with new USB based DAQ cards with the software modified accordingly. The three phase data acquisition unit developed as a result of this project has produced encouraging results and will be implemented in an industrial environment to evaluate and benchmark its performance with the existing system. Most importantly, a hardware data acquisition platform for the detection of PD pulses has been established within the company which is easily maintainable and expandable to suit any future requirements

Algorithms for sensor validation and multisensor fusion

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate

Advances In Internal Model Principle Control Theory

In this thesis, two advanced implementations of the internal model principle (IMP) are presented. The first is the identification of exponentially damped sinusoidal (EDS) signals with unknown parameters which are widely used to model audio signals. This application is developed in discrete time as a signal processing problem. An IMP based adaptive algorithm is developed for estimating two EDS parameters, the damping factor and frequency. The stability and convergence of this adaptive algorithm is analyzed based on a discrete time two time scale averaging theory. Simulation results demonstrate the identification performance of the proposed algorithm and verify its stability. The second advanced implementation of the IMP control theory is the rejection of disturbances consisting of both predictable and unpredictable components. An IMP controller is used for rejecting predictable disturbances. But the phase lag introduced by the IMP controller limits the rejection capability of the wideband disturbance controller, which is used for attenuating unpredictable disturbance, such as white noise. A combination of open and closed-loop control strategy is presented. In the closed-loop mode, both controllers are active. Once the tracking error is insignificant, the input to the IMP controller is disconnected while its output control action is maintained. In the open loop mode, the wideband disturbance controller is made more aggressive for attenuating white noise. Depending on the level of the tracking error, the input to the IMP controller is connected intermittently. Thus the system switches between open and closed-loop modes. A state feedback controller is designed as the wideband disturbance controller in this application. Two types of predictable disturbances are considered, constant and periodic. For a constant disturbance, an integral controller, the simplest IMP controller, is used. For a periodic disturbance with unknown frequencies, adaptive IMP controllers are used to estimate the frequencies before cancelling the disturbances. An extended multiple Lyapunov functions (MLF) theorem is developed for the stability analysis of this intermittent control strategy. Simulation results justify the optimal rejection performance of this switched control by comparing with two other traditional controllers

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

An improved energy management methodology for the mining industry.

The focus for this work was the development of an improved energy management methodology tailored for the mining sector. Motivation for this research was driven by perception of slow progress in adoption of energy management practices to improve energy performance within the mining sector. Energy audits conducted for an underground mine, a mineral processing facility, and a pyrometallurgical process were reviewed and recommendations for improved data gathering, reporting and interpretation were identified. An obstacle for conducting energy audits in mines without extensive sub-metering is a lack of disaggregated data indicating end use. Thus a novel method was developed using signal processing techniques to disaggregate the end-use electricity consumption, exemplified through isolation of a mine hoist signal from the main electricity meter data. Further refinements to the method may lead to its widespread adoption, which may lower energy auditing costs via a reduced number of meters and infrastructure, as well as lower data storage requirements. Mine ventilation systems correspond to the largest energy demand center for underground mines. Thus a detailed analysis ensued with the development of a techno-economic model that could be used to assess various fan and duct options. Furthermore, the need for a standardized methodology for determination of duct friction factors from ventilation surveys was proposed, which included a method to verify the validity of the resulting value from asperity height measurements. A method was also suggested for determination of leakage and duct friction factor values from ventilation survey data. Dissemination of best practice is a strategy that could be employed to improve energy performance throughout the mining sector, thus a Best Practice database was developed to iv improve communication and provide a standardized reporting framework for sharing of energy conservation initiatives. Demonstration of continuous improvement is an underpinning element of the ISO 50001 energy management standard but as mines extract ore from deeper levels energy use increases. Thus ensued the development of a benchmarking metric, with the use of appropriate support variables that included mine depth, production, and climate data, that demonstrated the benefit of implemented energy conservation measures for an underground mine. The development of an ultimate energy management methodology for all stages of mineral processing from ‘Mine to Bullion’ is beyond the scope of this work. However, this research has resulted in several recommendations for improvement and identified areas for further improvements.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Multi-Node Power Systems Profiling with Modified State Estimation

Abstract Electrical equipment poses a danger to maintenance personnel when the equipment must be serviced energized. While de-energized maintenance is preferred, for some tests, doing so will not provide the needed performance data to validate important performance metrics of the equipment. In particular, sites with a high cost of downtime (health care centers, data centers, continuous process manufacturing, etc.) identifying a problem that could affect series integrity of conductors (continuity) of service is critical. The infrared thermal imaging test is a common method of inferring the resistance of a connection by measuring the temperature of a component, and when normalized to current flow, can be used as a proxy for the effective resistance of the current path. Deviations from historical ratios of temperature to current may indicate a change in the junction resistance. The problem is that IR cameras require line-of-sight visibility with the energized electrical component to be tested. Many times due to available fault currents, fault clearing times and/or voltage levels, this cannot be done from a far enough distance to afford the operator safety even when wearing the maximum available PPE (personal protective equipment). The challenge has been how to perform this measurement safely and accurately, but still recognize that capital (for specialized test equipment) and expenses (for labor to perform testing) are frequently limited. Therefore, having a method of performing these tests throughout a facility, ideally performed from a central location while not requiring specialized equipment (leveraging the already installed IEDs [intelligent electronic devices]), would be a valuable solution. This research discovered a solution that reads low-resolution data from legacy IEDs but processes it to improve resolution to a point where ultimately it can be used to measure junction-to-junction impedances to sufficient resolution to detect failing connections. The research outlines the statistical analysis, filter design and conductor temperature normalization algorithms necessary to achieve this goal