• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.1
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• pp.63-69
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• 2018

Bearing is an essential component in many rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management(PHM), in which the key issue is to establish a proper feature reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics because it tends to increase abruptly at the end of life. This paper proposes a new feature extraction method using the envelope analysis and weighted sum with correlation coefficient. The developed method is demonstrated using the IMS bearing data given by NASA Ames Prognostics Data Repository. Results by the proposed feature are compared with those by conventional approach.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.801-806
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• 2015

Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Structural Engineering and Mechanics
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• v.58 no.5
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• pp.757-781
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• 2016

The results of diagnostics and analysis of an industrial hall located on the premises of a thermal power plant severely damaged by fire are presented in the paper. The comprehensive failure-related diagnostics, non-destructive and destructive tests of steel and concrete materials, geodetic surveying of selected structural members, numerical modelling, static analysis and reliability assessment were focused on two basic goals: The determination of the current technical condition of the load bearing structure and the assessment of its post fire resistance as well as assessing the degree of damage and subsequent design of reconstruction measures and arrangements which would enable the safe and reliable use of the building. The current mechanical properties of the steel material obtained from the tests and measured geometric characteristics of the structural members with imperfections were employed in finite element models to study the post-fire behaviour of the structure. In order to compare the behaviour of the numerically modelled steel roof truss, subjected to the effects of fire, with the real post-fire response of the damaged structure theoretically obtained resistance, critical temperature and the time at which the structure no longer meets the required reliability criteria under its given loading are compared with real values. A very good agreement between the simulated results and real characteristics of the structure after the fire was observed.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.61-68
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• 2001

A rotor system is composed of a rotating shaft with supporting bearings. The rotor system is widely used in every rotating machinery such as the turbine generator and the high precision machine tools. A negligible error or malfunction in the rotor, however, can cause a catastrophic failure in the system then result in the environmental and economic disasters. A diagnosis of the rotor system is important in preventing these kinds of failures and disasters. Up to now, many researchers have devoted in the development of diagnosing tools for the system. The basic principles behind the tools are to retrieve the data through the sensors for a specific state of the system and then to identify the specific state through the heuristic methods such as neural network, fuzzy logic, and decision matrix. The proper usage of the heuristic methods will enhance the performance of the diagnostic procedure when together used with the statistical signal processing. In this paper, the methodologies in using the above 3 heuristic methods for the diagnostics of the rotor system are established and also tested and validated for the data retrieved from the rolling element bearing and journal bearing supported system.

• Smart Structures and Systems
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• v.4 no.4
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• pp.429-438
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• 2008

The aim of this paper is to define the required measurements and processing tools necessary for developing a maintenance approach applied to rotating machines in the presence of multiple faults. The system responses measured were accelerations and transmission errors. Acceleration measurements provide most of the information on bearing conditions, while transmission error measurements provide pertinent information on gear conditions. The measurements were carried out for several operating conditions (loads and speeds). System responses were processed in several analyzing domains (Time, Spectrum, and Cepstrum domains). The approach developed enables the detection and identification of combined faults and it can be applied to other types of rotating machines once the critical elements and their associated faults have been defined.

• Korean Journal of Plant Tissue Culture
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• v.27 no.5
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• pp.379-385
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• 2000

tRNA and 7SL RNA based antisense vehicles were prepared by inserting conserved anti-viral and anti-viroid domains. Anti-PVS coat protein leader sequence (ACPL) and antistructural antihairpin domain of PSTVd (AHII) were inserted in tRNA cassette; anti- zing finger domain of PVS, AHII and anti hop latent viroid ribozyme were inserted in 7SL RNA gene isolated from A. thaliana. These constructs were shown to be transcribed both, in in vitro and in in vivo conditions. However, it followed from our work that closely linked position of PoIII reference genes and PoIIII antisense genes within T-DNA lead to the impairment of RNA expression in transgenic plants. To assay in vivo transcription of antisense genes, hairy root potato cultures were established using h. tumefaciens A4-24 bearing both, Ri plasmid and PoIII-promoterless plant expression vectors with antisense RNA genes. Expression of antisense RNA in transgenic potato tissues was proven by specific RT-PCR reactions.