Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,”

Abstract. 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

Molecular detection of vanA and vanB genes among vancomycin-resistant enterococci in ICU-hospitalized patients in Ahvaz in southwest of Iran

Mojtaba Moosavian,1,2 Hosein Ghadri,2 Zahra Samli3 1Infectious and Tropical Diseases Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 2Department of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 3School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Objective: Nosocomial infections due to vancomycin-resistant enterococci (VRE) are known as a source of spreading these bacteria. The aim of this prospective study was molecular detection of vanA and vanB genes among VRE isolated from patients admitted to intensive care units (ICUs) in Ahvaz in southwest of Iran.Materials and methods: Overall, 243 non-duplicate rectal swab specimens were collected from ICU-hospitalized patients in teaching hospitals affiliated to Ahvaz Jundishapur University of Medical Sciences, Iran. The specimens were inoculated on suitable culture media, and isolates were identified by standard biochemical tests. The susceptibility and resistance of enterococci to 10 antibiotics were determined based on the Clinical and Laboratory Standards Institute guidelines. Resistance to vancomycin was phenotypically detected by vancomycin screening test, and the vanA and vanB genes in vancomycin-resistant isolates were amplified by multiplex PCR method.Results: Of 175 specimens containing enterococci, 129 (73.7%) isolates were detected as Enterococcus faecium and Enterococcus faecalis and 46 (26.3%) isolates as Enterococcus spp. The results of susceptibility test showed high rates of resistance to tetracycline, erythromycin, ciprofloxacin, and ampicillin. Moreover, based on this test, out of 129 Enterococcus isolates, 56 (43.4%) were resistant to vancomycin and teicoplanin. Also, among 59 vancomycin-resistant or semi-susceptible isolates, vanA gene was detected in 54 (91.5%) isolates, while none of the isolates had vanB gene.Conclusion: According to the results of this study, to prevent the spread of vancomycin-resistant Enterococcus strains, especially in nosocomial infections, the susceptibility of isolates should be determined before vancomycin prescription. Keywords: Enterococcus faecalis, Enterococcus faecium, multiplex PCR, vancomyci

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

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