Seppo J. Ovaska

Noise reduction in zero crossing detection by predictive digital filtering

A simple combination of nonlinear and linear digital signal processing methods is proposed for efficient noise reduction in zero crossing detectors. The method is very robust against strong impulsive noise, typically encountered in thyristor power converters, where reliable zero crossing detection is required for firing synchronization. A systematic design procedure is described for the proposed filter-based synchronization method, taking into account the specified line frequency tolerance. The fully digital signal processing approach allows compact implementations, and supports flexible interfacing to digital motor control systems. >

Industrial applications of soft computing: a review

Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich knowledge representation, flexible knowledge acquisition, and flexible knowledge processing, which enable intelligent systems to be constructed at low cost. This paper reviews applications of SC in several industrial fields to show the various innovations by TR, HMIQ, and low cost in industries that have been made possible by the use of SC. Our paper intends to remove the gap between theory and practice and attempts to learn how to apply soft computing practically to industrial systems from examples/analogy, reviewing many application papers.

Angular acceleration measurement: a review

This paper gives a review of sensors, methods, and algorithms available for the measurement of angular acceleration. The emphasis is in delay-sensitive, real-time applications. Although the angular acceleration can be measured indirectly using either a rotating angle sensor or a velocity sensor, the noise-amplification problem related to the differentiation process has motivated the efforts to develop transducers for direct sensing of angular acceleration. Direct measuring of linear acceleration is widely in everyday use, but the angular acceleration sensors, particularly those with unlimited rotation angle, can still be considered as emerging devices. Consequently, there exist two principal challenges for the research and development community: to develop economical and accurate angular accelerometers with unlimited rotation range, and to create wideband indirect techniques with small lag and high signal-to-error ratio.

Digital filtering for robust 50/60 Hz zero-crossing detectors

An improved digital filtering method for line frequency zero-crossing detectors is proposed. The multistage filter efficiently attenuates harmonics, wide-band noise, commutation notches, and other impulsive disturbances without causing any phase shift on the primary sinusoidal waveform. Our novel signal-processing system is a cascade of a median filter and an adaptive sinusoid predictor, followed by up-sampling and interpolation. The three-point median filter effectively removes impulses, and the predictor provides wide-band noise attenuation while compensating for delays in the other processing steps. The predictor adapts to possible line frequency variations within the specified range by changing the set of coefficients, based on an estimate of the instantaneous line frequency. The adaptive approach allows the use of highly selective IIR bandpass predictors.

A general framework for statistical performance comparison of evolutionary computation algorithms

This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A twofold sampling scheme for collecting performance data is introduced, and these data are analyzed using bootstrap-based multiple hypothesis testing procedures. The proposed method is sufficiently flexible to allow the researcher to choose how performance is measured, does not rely upon distributional assumptions, and can be extended to analyze many other randomized numeric optimization routines. As a result, this approach offers a convenient, flexible, and reliable technique for comparing algorithms in a wide variety of applications.

Polynomial predictive filtering in control instrumentation: a review

Additional delay is an unavoidable drawback of conventional filters used frequently in industrial electronics. This delay is particularly harmful if the filtered primary signal is to be used for time-critical feedback or synchronization purposes. Therefore, predictive signal processing methods can offer significant advantages for these real-time applications. Polynomial predictive filters are specified without explicit passbands and stopbands, and they are behaving delaylessly or predictively for smoothly varying signal components. The degree of smoothness of the incoming signal sets the requirements for the applied filtering scheme and its parameters. Smoothness of a signal is a fuzzy and application-specific concept: the degree of smoothness depends on the ratio of the bandwidth of the primary signal and the applied sampling rate, as well as the noise component. In this paper, the authors review the most important polynomial predictive filtering methods and algorithms, their design and implementation techniques, and a collection of successful applications.

Artificial immune optimization methods and applications - a survey

Inspired by natural immune systems, artificial immune systems (AIS) are an emerging kind of computational intelligence paradigm. During the past decade, the AIS have gained great research interest in wide engineering fields. Artificial immune optimization (AIO) methods are an important partner of the AIS. They have been successfully applied to deal with numerous challenging optimization problems with superior performance over classical optimization techniques. This paper gives a concise survey on the recent progresses of the theory as well as applications of the AIO schemes, in which some representative approaches are briefly introduced and discussed.

Soft Computing in Industrial Applications

The research has developed a fuzzy logic approach to handling missing data. A prototype fuzzy model was developed, using the FuzzyTech software, to assess the quality of the steel production in terms of composition, time, and temperature. As tools like FuzzyTech are not able to handle missing data, the research has introduced a fuzzy logic approach to decision making with less data. A number of workshops were carried out in the plant, and the aired expertss knowledge was the basis for the researchs development. This paper will present the state of the art research on the application of artificial intelligence and statistical techniques for handling the missing data problem

Delayless method to generate current reference for active filters

Active power filters are used to eliminate AC harmonic currents by injecting equal but opposite compensating currents. Successful control of active filters requires, among other things, an accurate current reference. In this paper, we introduce a multistage adaptive filtering system which generates the current reference delaylessly and accurately. Our filter structure combines a low-pass prefilter and an adaptive predictive filter, making it possible to extract the sinusoidal active current from the distorted waveform without harmful phase shift, even when the frequency and amplitude alter simultaneously. Although active filters are typically used to compensate for the supply harmonics, where the fundamental frequency remains almost constant, we will show that our filter structure can also be applied in applications where the frequency alters rapidly.

Soft computing methods in motor fault diagnosis

Abstract During the last decade, soft computing (computational intelligence) has attracted great interest from different areas of research. In this paper, we give an overview on the recent developments in the emerging field of soft computing-based electric motor fault diagnosis. Several typical fault diagnosis schemes using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms, with descriptive diagrams as well as simplified algorithms are presented. Their advantages and disadvantages are compared and discussed. We conclude that soft computing methods have great potential in dealing with difficult fault detection and diagnosis problems.