The paper presents the method of on-line diagnostics of the bed temperature controller for the fluidized bed boiler. Proposed solution is based on the methods of statistical process control. Detected decrease of the bed temperature control quality is used to activate the controller self-tuning procedure. The algorithm that provides optimal tuning of the bed temperature controller is also proposed. The results of experimental verification of the presented method is attached. Experimental studies were carried out using the 2 MW bubbling fluidized bed boiler.

The objective of this article is to carry out a systematic review of the literature on multivariate statistical process control (MSPC) charts used in industrial processes. The systematic review was based on articles published via Web of Science and Scopus in the last 10 years, from 2010 to 2020, with 51 articles on the theme identified. This article sought to identify in which industry the MSPC charts are most applied, the types of multivariate control charts used and probability distributions adopted, as well as pointing out the gaps and future directions of research. The most commonly represented industry was electronics, featuring in approximately 25% of the articles. The MSPC chart most frequently applied in the industrial sector was the traditional T2 of Harold Hotelling (Hotelling, 1947), found in 26.56% of the articles. Almost half of the combinations between the probabilistic distribution and the multivariate control graphs, i.e., 49.4%, considered that the data followed a normal distribution. Gaps and future directions for research on the topic are presented at the end.

The paper presents an analysis of SPC (Statistical Process Control) procedures usability in foundry engineering. The authors pay particular attention to the processes complexity and necessity of correct preparation of data acquisition procedures. Integration of SPC systems with existing IT solutions in area of aiding and assistance during the manufacturing process is important. For each particular foundry, methodology of selective SPC application needs to prepare for supervision and control of stability of manufacturing conditions, regarding specificity of data in particular “branches” of foundry production (Sands, Pouring, Metallurgy, Quality).

Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing

industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product

parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this

assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the

present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial

data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data

with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases

It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors.

The manufacturing and characterization of polymer nanocomposites is an active research trend nowadays. Nonetheless, statistical studies of polymer nanocomposites are not an easy task since they require several factors to consider, such as: large amount of samples manufactured from a standardized procedure and specialized equipment to address characterization tests in a repeatable fashion. In this manuscript, the experimental characterization of sensitivity, hysteresis error and drift error was carried out at multiple input voltages (����) for the following commercial brands of FSRs ( force sensing resistors): Interlink FSR402 and Peratech SP200-10 sensors. The quotient between the mean and the standard deviation was used to determine dispersion in the aforementioned metrics. It was found that a low mean value in an error metric is typically accompanied by a comparatively larger dispersion, and similarly, a large mean value for a given metric resulted in lower dispersion; this observation was held for both sensor brands under the entire range of input voltages. In regard to sensitivity, both sensors showed similar dispersion in sensitivity for the whole range of input voltages. Sensors’ characterization was carried out in a tailored test bench capable of handling up to 16 sensors simultaneously; this let us speed up the characterization process.