Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

With a view to detecting incipient failures in large-size low-speed rolling bearings and ensuring minimal effect of subjectivity on the process, a new data-driven multivariate and multiscale statistical monitoring method is proposed. The proposed method which combines the Principal Component Analysis (PCA) multivariate monitoring approach and the Ensemble Empirical Mode Decomposition (EEMD) method, which adaptively decomposes signals into various time scales, was called the EEMD-based multiscale PCA (EEMD–MSPCA). The method is very general in nature, which is why it could also be used in different areas and for various tasks. It can be used for controlling each time scale of decomposition or only the selected ones, for multivariate and multiscale filtering or for monitoring system operation on the basis of reconstructed i.e. filtered signals. The efficiency of the proposed EEMD–MSPCA method for the task of bearing condition monitoring and signal filtering was evaluated on simulated as well as on actual vibration and Acoustic Emission (AE) signals measured on a purpose built test stand. The fact that the proposed method is able to identify the local bearing defect of a very small size indicates that AE and vibration signals carry sufficient information on the bearing condition and that the proposed EEMD–MSPCA method ensures high-reliability bearing fault detection.     

Distribution of selenium and phenolics in buckwheat plants grown from seeds soaked in Se solution and under different levels of UV-B radiation

Seeds of common buckwheat (Fagopyrum esculentum) were soaked in water, sodium selenate (5, 10 or 20 mg Se^VI/L), or sodium selenite (10 or 20 mg Se^IV/L) solutions. Plants grown from soaked seeds were exposed to reduced UV-B radiation, ambient, or enhanced UV-B. The mass fraction of selenium in leaves was much higher in plants obtained from seeds soaked with selenate (up to 185 ng/g) in comparison to selenite (up to 103 ng/g). In plants obtained from seeds soaked in water, regardless of UV-B levels, the highest concentration of selenium was found in leaves, where the values were between 45 and 66 ng Se/g. In buckwheat leaves 44.5–63.6 mg/100 g d.m. of fagopyrin was found, and in stems 14.3–26.4 mg/100 g d.m.; here no influence of seed soaking solution or UV-B exposure was found. The content of total flavonoids in leaves was 7.8–15.9% and in stems 1.4–4.1%.     

Online fuzzy identification for an intelligent controller based on a simple platform

The paper presents the identification issues of the self-tuning nonlinear controller ASPECT (Advanced control algorithmS for ProgrammablE logiC conTrollers). The controller is implemented on a simple PLC platform with an extra mathematical coprocessor, but is intended for the advanced control of complex processes. The model of the controlled plant is obtained by means of experimental modelling. A special batch-wise algorithm that is based on the Takagi–Sugeno model and uses “fuzzy instrumental variables” technique is described in the paper. Many robustness problems of the classical adaptive approaches can be circumvented to some extent by the proposed batch-wise approach combined with a supervisory mechanism. The paper also includes some experimental results on the hydraulic pilot plant and some simulation case studies.     

Hybrid explicit model predictive control of a nonlinear process approximated with a piecewise affine model

The recently developed methods of explicit (multi-parametric) model predictive control (e-MPC) for hybrid systems provide an interesting opportunity for solving a class of nonlinear control problems. With this approach, the nonlinear process is approximated by a piecewise affine (PWA) hybrid model containing a set of local linear dynamics. Compared to linear-model-based MPC, a performance improvement is expected with the reduction of the plant-to-model mismatch; however at a cost of controller computation complexity. In order to reduce the computational load, so that desired horizon lengths may be used, we present an efficient sub-optimal solution. The feasibility of the approach for the application was evaluated in an experimental case study, where an output feedback, offset-free-tracking hybrid e-MPC controller was considered as a replacement for a PID-controller-based scheme for the control of the pressure in a wire-annealing machine.     

Salicylaldehyde is a characteristic aroma component of buckwheat groats

Salicylaldehyde (2-hydroxybenzaldehyde) was identified as a characteristic component of buckwheat groats aroma by a sensory analysis guided fractionation of the extract. The extract with the strongest odour was prepared by petroleum ether extraction of water soaked groats. This extract was further extracted with sodium hydrogen carbonate solution and purified by a preparative layer chromatography and identified by NMR, MS and IR spectroscopy. A capillary electrophoresis method was developed and used to determine salicylaldehyde content in buckwheat groats and flour samples. Traditionally dehulled buckwheat grain, which had the strongest odour, contained the highest concentration (1.6 ppm) of salicylaldehyde with an odour activity value (OAV) of 216.     

Identification of buckwheat (Fagopyrum esculentum Moench) aroma compounds with GC–MS

Buckwheat has a strong characteristic aroma, but its phytochemical background has not yet been fully elucidated. The aims of this study were identification and quantification of individual compounds responsible for the buckwheat aroma. Volatiles from a freshly ground buckwheat flour were extracted by different methods: direct extraction with petroleum ether, pentane or methanol, distillation with Clevenger apparatus and a headspace solid-phase microextraction method. The extracts were analysed by GC–MS with electron ionisation. Compounds were identified by MS and by comparison of retention times with reference compounds. Direct extraction with methanol and distillation proved to be very efficient. In these extracts twenty-five and thirty-five compounds were identified, respectively. The first extract contained more hydrophilic compounds and the latter more volatile compounds. Most of the compounds were quantified and their odour activity value (OAV) calculated. Only two compounds (salicylaldehyde and phenylacetaldehyde) were found in both extracts. The compounds with the highest contribution to the buckwheat aroma were: 2,5-dimethyl-4-hydroxy-3(2H)-furanone, (E,E)-2,4-decadienal, phenylacetaldehyde, 2-methoxy-4-vinylphenol, (E)-2-nonenal, decanal, hexanal and salicylaldehyde (2-hydroxybenzaldehyde).     

Electro-hydraulic load sensing with a speed-controlled hydraulic supply system on forming-machines

High-power transmission capacity at low-energy losses, low noise emissions, high reliability, and ease of maintenance, as well as convenient cost-effectiveness, are required for industrial applications regarding variable electro-hydraulic supply systems. Only up-to-date principles regarding volumetrically controlled hydraulic energy using variable supply systems and appropriate control strategy have proven satisfactory for these requirements. The aim of the present work was to study the applicability of a low-priced drive concept using a speed-controlled induction motor in combination with a constant-displacement pump applied in a load-sensing control strategy. The suggested approach of the drive concept has been experimentally verified on a prototype of the drive. A hydraulic press-brake used for the machining of casting products in the automotive industry was taken into consideration.     

Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method

The article presents a novel non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) non-linear multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics. The proposed method which enables us to cope with complex even severe non-linear systems with a wide dynamic range was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The method is quite general in nature and could be used in different areas for various tasks even without any really deep understanding of the nature of the system under consideration. Its efficiency was first demonstrated by an illustrative example, after which the applicability for the task of bearing fault detection, diagnosis and signal denosing was tested on simulated as well as actual vibration and acoustic emission (AE) signals measured on purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that the proposed EEMD-MSKPCA method provides a promising tool for tackling non-linear multiscale data which present a convolved picture of many events occupying different regions in the time-frequency plane.     

Commonly applied smoothing of IR spectra showed unappropriate for the identification of plant leaf samples

The use of infrared spectroscopy has spread from single compound research to the investigation of complex biological samples. In infrared spectroscopy, spectral pre-treatment techniques have been assumed to be equally applicable and effective in the analysis of biological samples with complex chemical composition and structure. In this research, the most commonly used pre-treatment techniques were investigated based on the identification of species from whole leaf samples of pharmaceutically important Epilobium and Hypericum genera. Two spectral collection modes were used; whole leaf transmission and KBr-tablet transmission mode. The results of this study do not support the current standard in pre-treatment methods. After the frequency decomposition of a spectral signal by Fourier transform and wavelet decomposition, it was revealed that the important information of whole plant leaf as an example of biological samples was contained in the spectral details. Therefore, smoothing techniques were not appropriate because high frequency information is lost. A vast majority of published work used a Savitzky-Golay smoothing method on infrared spectra of complex biological samples. This method was shown to be less effective. In contrast, taking the derivative of the spectra showed significantly better results; with this pre-treatment method, the overlapping bands become more evident.