Theoretical approach to obtaining dynamic characteristics of noncontacting spiral-grooved seals

The dynamic characteristics of spiral-grooved seals are theoretically obtained by using the Navier-Stokes equation. First, with the inertia term of the fluid considered, the flow and pressure in the steady state are obtained for the directions parallel to and perpendicular to the groove. Next, the dynamic character is obtained by analyzing the steady state and by analyzing the labyrinth seal. As a result, the following conclusions were drawn: (1) As the land width becomes shorter or the helix angle decreases, the cross-coupling stiffness, direct and cross-coupling damping, and add mass coefficients decrease; (2) As the axial Reynolds number increases, the stiffness and damping coefficients increase. But the add mass coefficient is not influenced by the axial Reynolds number; (3) The rotational Reynolds number influences greatly the direct and cross-coupling stiffness and direct damping coefficients; and (4) As the journal rotating frequency increases, the leakage flow decreases. Therefore zero net leakage flow is possible at a particular rotating frequency

Static and dynamic characteristics of parallel-grooved seals

Presented is an analytical method to determine static and dynamic characteristics of annular parallel-grooved seals. The governing equations were derived by using the turbulent lubrication theory based on the law of fluid friction. Linear zero- and first-order perturbation equations of the governing equations were developed, and these equations were analytically investigated to obtain the reaction force of the seals. An analysis is presented that calculates the leakage flow rate, the torque loss, and the rotordynamic coefficients for parallel-grooved seals. To demonstrate this analysis, we show the effect of changing number of stages, land and groove width, and inlet swirl on stability of the boiler feed water pump seals. Generally, as the number of stages increased or the grooves became wider, the leakage flow rate and rotor-dynamic coefficients decreased and the torque loss increased

An application of decision trees method for fault diagnosis of induction motors

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data

Machine condition prognosis using multi-step ahead prediction and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machine’s operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis

Machine condition prognosis based on regression trees and one-step-ahead prediction

Predicting degradation of working conditions of machinery and trending of fault propagation before they reach the alarm or failure threshold is extremely importance in industry to fully utilize the machine production capacity. This paper proposes a method to predict future conditions of machines based on one-step-ahead prediction of time-series forecasting techniques and regression trees. In this study, the embedding dimension is firstly estimated in order to determine the necessary available observations for predicting the next value in the future. This value is subsequently utilized for regression tree predictor. Real trending data of low methane compressor acquired from condition monitoring routine are employed for evaluating the proposed method. The results indicate that the proposed method offers a potential for machine condition prognosi

Noise Source Identification of Small Fan-BLDC Motor System for Refrigerators

Noise levels in household appliances are increasingly attracting attention from manufacturers and customers. Legislation is becoming more severe on acceptable noise levels and low noise is a major marketing point for many products. The latest trend in the refrigerator manufacturing industry is to use brushless DC (BLDC) motors instead of induction motors in order to reduce energy consumption and noise radiation. However, cogging torque from BLDC motor is an undesirable effect that prevents the smooth rotation of the rotor and results in noise. This paper presents a practical approach for identifying the source of excessive noise in the small fan-motor system for household refrigerators. The source is presumed to a mechanical resonance excited by torque ripple of the BLDC motor. By using finite element analysis, natural frequencies and mode shapes of the rotating part of the system are obtained and they are compared with experimental mode shapes obtained by electronic torsional excitation test which uses BLDC motor itself as an exciter. Two experimental validations are carried out to confirm the reduction of excessive noise

Multiplex reverse transcription-PCR for rapid differential detection of porcine epidemic diarrhea virus, transmissible gastroenteritis virus, and porcine group A rotavirus

A novel multiplex reverse transcription polymerase chain reaction (multiplex RT-PCR) that can detect porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and porcine group A rotavirus (GAR) was developed. The 3 viruses (PEDV, TGEV, and porcine GAR) are major agents in viral enteric diseases of piglets. As the clinical signs of these diseases are similar, including watery diarrhea, differential detection is required for etiologic diagnosis. A mixture of 3 pairs of published primers was used for amplification of viral nucleic acids, yielding 3 different amplicons with sizes of 859 bp, 651 bp, and 309 bp for TGEV, PEDV, and porcine GAR, respectively. A total of 157 specimens (78 fecal and 79 intestinal samples) from piglets with acute gastroenteritis were collected in Korea between January 2004 and May 2005. They were tested for the presence of 3 viruses by multiplex RT-PCR. Coinfections with PEDV and porcine GAR were identified in 16 farms (43.2%). PEDV, porcine GAR, and TGEV infection were 26.3%, 13.2%, and 2.7% respectively. The relative sensitivity and specificity of multiplex RT-PCR were evaluated, with results suggesting that this assay is equal in quality to conventional single-agent RT-PCR assays (sensitivity:100%, 92.9%, 100% for TGEV, PEDV, GARs; specificity: 100% for all 3 viruses). This multiplex RT-PCR is a simple assay and may be a potentially useful for rapid, sensitive, and cost-effective etiological diagnostic tool for acute viral gastroenteritis in piglets.This work was supported by Korea Research Foundation Grants (KRF-2002-070-C00069) and the Brain Korea 21 Project of the Ministry of Education & Human Resources Development, Republic of Korea