An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method.

In this paper both envelope approach and morphological filters for characterisation of surface textures were proposed, applied and thoroughly examined. Obtained results were compared with those received after appliance of commonly-used algorithms. The effect of appliance of proposed procedures on surface topography parameters (from ISO 25178 standard) was taken into consideration. The following types of surface textureswere assessed: two-process plateau-honed cylinder liners, plateau-honed cylinder liners with additionally burnished dimples, turned piston skirts, grinded and/or isotropic topographies. It was assumed that envelope characteristics (envelope filtration) can provide results useful for assessments of deep and/or wide oil-reservoirs especially when they are edge located. Moreover, some near-valley areas of surface texture details can be less distorted when envelope filtering is accomplished. It was also found that closing and/or opening envelope filtration can be valuable for reduction of some surface topography measurement errors.

Recently, simultaneous monitoring of process mean and variability has gained increasing attention. By departing from the accurate measurements assumption, this paper investigates the effect of gauge measurement errors on the performance of the maximum generally weighted moving average (Max-GWMA) chart for simultaneous monitoring of process mean and variability under an additive covariate model. Multiple measurements procedure is employed to compensate for the undesired impact of gauge inaccuracy on detection capability of the Max- GWMA chart. Simulation experiments in terms of average run length (ARL) are conducted to assess the power of the developed chart to detect different out-of-control scenarios. The results confirm that the gauge inaccuracy affects the sensitivity of the Max-GWMA chart. Moreover, the results show that taking multiple measurements per item adequately decreases the adverse effect of measurement errors. Finally, a real-life example is presented to demonstrate how measurement errors increases the false alarm rate of the Max-GWMA chart.