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Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

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Removal of metals from wastewaters causes a big concern from the environmental point of view due to their extreme toxicity towards aquatic life and humans. Application of As(III) from aqueous solution by ZnO nanorods as adsorbent has been investigated in the present study. The synthesized nanorods were characterized by XRD, FT-IR spectroscopy, SEM, and thermogravimetric analysis. Optimum biosorption conditions were determined with respect to pH, adsorbent dose, contact time, and temperature. The experimental data were examined using the Lagergren's first-order, pseudo-second-order and intraparticle diffusion kinetic models. The results revealed that the pseudo-second-order kinetic model provided the best description of the data. Langmuir and Freundlich isotherm models were applied to the equilibrium data. The maximum As(III) sorption capacity of ZnO nanorods was found to be 52.63 mg/g at pH 7, adsorbent dose 0.4 g, contact time 105 min, and temperature 323 K. The calculated thermodynamic parameters, ΔG o (between − 5.741, − 5.342 and − 4.538 kJ/mol at 303-323 K), ∆H o (13.75 kJ/mol) and ∆S o (0.0616 J/mol K) showed that the sorption of As(III) onto ZnO nanorods was feasible, spontaneous and exothermic, respectively.