• Title/Summary/Keyword: Damage Pattern

Search Result 781, Processing Time 0.031 seconds

Fault Detection and Damage Pattern Analysis of a Gearbox Using the Power Spectra Density and Artificial Neural Network (파워스펙트럼 및 신경망회로를 이용한 기어박스의 결함진단 및 결함형태 분류에 관한 연구)

  • Lee, Sang-Kwon
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.27 no.4
    • /
    • pp.537-543
    • /
    • 2003
  • Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

Construction of Oil-Spill Warning System based on Remote Sensing/Numerical Model and Its Application to the Natural Resource Damage Assessment and Restoration System

  • Goto, Shintaro;Kim, Sang-Woo
    • Proceedings of the KSRS Conference
    • /
    • 1999.11a
    • /
    • pp.243-248
    • /
    • 1999
  • From the lessons after the Nakhodka oil-spill in Jan. 1997, oil slick detection by using remote sensing data and assimilating the data to the simulation program is important for monitoring the oil-drift pattern. For this object, we are going to construct the oil-spill warning system for estimating the oil-drift pattern using remotesensing/numerical simulation Model. Additionally we plan to use this system for restorating oil-spill damage domestically, such as estimating the ecological damage and making the priority fur restorating the oil-spilled shoreline. This report is intended to summarize the role of geo-informatics in the oil spill accident by not only paying attention to the effect of information provision/information management via the map, but also reporting the interim result in part based on the details discussed in the processes of recovery support and environmental impact assessment during the Nakhodka's accident.

  • PDF

Contact Damage and Fracture of Poreclain/Glass-Infiltrated Alumina Layer Structure for Dental Application (치아 응용을 위한 /유리침윤 알루미나 이중 층상구조의 접촉손상 및 파괴)

  • 정연길;여정구;최성설
    • Journal of the Korean Ceramic Society
    • /
    • v.35 no.12
    • /
    • pp.1257-1265
    • /
    • 1998
  • Hertzian contact tests were used to investigate the evolution of fracturedamage in the coating layer as functions of contact load and coating thickness by studying crack patterns in porcelain on glass-infiltrated alumina bilayer system conceived to simulate the crown structure of a tooth. Cone cracks initiated at the coating top surface without delamination at interface and crack propagation to substrate. Preferentially the cracks made multi-cracks at the coating top surface rather than proceeding to interface. The cracks were highly stabilized with wide ranges between the loads to initiate first cracking and to cause final failure im-plying damage-tolerant capability. Finite element modelling was used to evaluate the stress distribution. Maximum tensile stress were responsible for the cracking at the coating layer and had a profound influence on the crack pattern and fracture damage in the layered structure materials.

  • PDF

Acoustic emission technique to identify stress corrosion cracking damage

  • Soltangharaei, V.;Hill, J.W.;Ai, Li;Anay, R.;Greer, B.;Bayat, Mahmoud;Ziehl, P.
    • Structural Engineering and Mechanics
    • /
    • v.75 no.6
    • /
    • pp.723-736
    • /
    • 2020
  • In this paper, acoustic emission (AE) and pattern recognition are utilized to identify the AE signal signatures caused by propagation of stress corrosion cracking (SCC) in a 304 stainless steel plate. The surface of the plate is under almost uniform tensile stress at a notch. A corrosive environment is provided by exposing the notch to a solution of 1% Potassium Tetrathionate by weight. The Global b-value indicated an occurrence of the first visible crack and damage stages during the SCC. Furthermore, a method based on linear regression has been developed for damage identification using AE data.

Optimization of the seismic performance of masonry infilled R/C buildings at the stage of design using artificial neural networks

  • Kostinakis, Konstantinos G.;Morfidis, Konstantinos E.
    • Structural Engineering and Mechanics
    • /
    • v.75 no.3
    • /
    • pp.295-309
    • /
    • 2020
  • The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design.

Analysis of Spectral Fatigue Damage of Linear Elastic Systems with Different High Cyclic Loading Cases using Energy Isocline (에너지 등고선을 이용한 고주파 가진 조건들에 따른 선형 시스템의 피로 손상도 분석)

  • Shin, Sung-Young;Kim, Chan-Jung
    • Transactions of the Korean Society for Noise and Vibration Engineering
    • /
    • v.24 no.11
    • /
    • pp.840-845
    • /
    • 2014
  • Vibration profiles consist of two kinds of pattern, random and harmonic, at general engineering problems and the detailed vibration test mode of a target system is decided by the spectral condition that is exposed under operation. In moving mobility, random responses come generally from road source; whereas the harmonic responses are triggered from rotating machinery parts, such as combustion engine or drive shaft. Different spectral input may accumulate different damage in frequency domain since the accumulated fatigue damage dependent on the pattern of input spectrum in high cyclic loading condition. To evaluate the sensitivity of spectral damage according to different loading conditions, a linear elastic system is introduced to conduct a uniaxial vibration testing. Measured data, acceleration and strain, is analyzed using energy isocline function and then, the calculated fatigue damage is compared by different loading cases, random and harmonic.

Pipeline Structural Damage Detection Using Self-Sensing Technology and PNN-Based Pattern Recognition (자율 감지 및 확률론적 신경망 기반 패턴 인식을 이용한 배관 구조물 손상 진단 기법)

  • Lee, Chang-Gil;Park, Woong-Ki;Park, Seung-Hee
    • Journal of the Korean Society for Nondestructive Testing
    • /
    • v.31 no.4
    • /
    • pp.351-359
    • /
    • 2011
  • In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.

Emergent damage pattern recognition using immune network theory

  • Chen, Bo;Zang, Chuanzhi
    • Smart Structures and Systems
    • /
    • v.8 no.1
    • /
    • pp.69-92
    • /
    • 2011
  • This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

Frequency Domain Pattern Recognition Method for Damage Detection of a Steel Bridge (강교량의 손상감지를 위한 주파수 영역 패턴인식 기법)

  • Lee, Jung Whee;Kim, Sung Kon;Chang, Sung Pil
    • Journal of Korean Society of Steel Construction
    • /
    • v.17 no.1 s.74
    • /
    • pp.1-11
    • /
    • 2005
  • A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally-acquired signals are used to test the NN. The results of this example application suggest that the SAI-based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.

Effect of Rock Damage Induced by Blasting on Tunnel Stability (발파굴착의 암반손상이 터널안정성에 미치는 영향분석)

  • Lee, In-Mo;Yoon, Hyun-Jin;Kim, Dong-Hyun;Lee, Sang-Don;Park, Bong-Ki
    • Proceedings of the Korean Geotechical Society Conference
    • /
    • 2003.03a
    • /
    • pp.681-688
    • /
    • 2003
  • Rock damage induced by blasting can not be avoided during tunnel construction and may affect tunnel stability. But the mutual interaction between tunnel blasting design and tunnel stability design is generally not considered. Therefore this study propose a methodology to take into considration the results of the blasting damage in tunnel stability design. Rock damage is evaluated by dynamic numerical analysis for the most common blasting pattern adopted in road tunnel. Damage zone is determined by using the continuum damage model which is expressed as a function of volumetric strain. And the damage effect is taken into account by the damaged rock stiffness and the damaged failure criteria in tunnel stability assessment. The extend of plastic zone and deformation increase compared to the case of not considering blast-induced rock damage.

  • PDF