Structural systems often show nonlinear behavior under severe excitations generated by natural hazards. In that condition, the restoring force becomes highly nonlinear showing significant hysteresis. The hereditary nature of this nonlinear restoring force indicates that the force cannot be described as a function of the instantaneous displacement and velocity. Accordingly, many hysteretic restoring force models were developed to include the time dependent nature using a set of differential equations. This survey contains a review of the past, recent developments and implementations of the Bouc-Wen model which is used extensively in modeling the hysteresis phenomenon in the dynamically excited nonlinear structures.

The Hysteresis Bouc-Wen Model, a Survey

Gas leaks can cause major incidents resulting in both human injuries and financial losses. To avoid such situations, a considerable amount of effort has been devoted to the development of reliable techniques for detecting gas leakage. As knowing about the existence of a leak is not always enough to launch a corrective action, some of the leak detection techniques were designed to allow the possibility of locating the leak. The main purpose of this paper is to identify the state-of-the-art in leak detection and localization methods. Additionally we evaluate the capabilities of these techniques in order to identify the advantages and disadvantages of using each leak detection solution.

https://www.aut.upt.ro/~pal-stefan.murvay/papers/survey_gas_leak_detection_localization_techniques.pdf

A survey on gas leak detection and localization techniques

Due to the inherent nonlinear nature of magnetorheological (MR) dampers, one of the challenging aspects for developing and utilizing these devices to achieve high performance is the development of models that can accurately describe their unique characteristics. In this review, the characteristics of MR dampers are summarized according to the measured responses under different conditions. On these bases, the considerations and methods of the parametric dynamic modelling for MR dampers are given and the state-of-the-art parametric dynamic modelling, identification and validation techniques for MR dampers are reviewed. In the past two decades, the models for MR dampers have been focused on how to improve the modelling accuracy. Although the force–displacement behaviour is well represented by most of the proposed dynamic models for MR dampers, no simple parametric models with high accuracy for MR dampers can be found. In addition, the parametric dynamic models for MR dampers with on-line updating ability and the inverse parametric models for MR dampers are scarcely explored. Moreover, whether one dynamic model for MR dampers can portray the force–displacement and force–velocity behaviour is not only determined by the dynamic model itself but also determined by the identification method.

https://iopscience.iop.org/article/10.1088/0964-1726/20/2/023001/pdf

Magnetorheological fluid dampers: a review of parametric modelling

As one of the most important field-responsive intelligent and smart soft matter materials, magnetorheological (MR) fluids, consisting of magneto-responsive magnetizable particles suspended in non-magnetic fluids, have drawn a lot of attentions in both academia and industry as their physical and rheological characteristics can be controlled with external magnetic field strength. In this highlight, preparation methods and MR properties of various magnetic composites with soft magnetic particles and polymers are reviewed. In addition, some industrial applications, such as a MR dampers and a MR polishing, are briefly summarized.

Magnetorheology: materials and application

Cost-effective wind turbine (WT) condition monitoring assumes more importance as turbine sizes increase and they are placed in more remote locations, for example, offshore. Conventional condition monitoring techniques, such as vibration, lubrication oil, and generator current signal analysis, require the deployment of a variety of sensors and computationally intensive analysis techniques. This paper describes a WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal. The detection algorithm uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal. The central frequency of the filter is controlled by the generator speed, and the filter bandwidth is adapted to the speed fluctuation. Using this technique, fault features can be extracted, with low calculation times, from direct- or indirect-drive fixed- or variable-speed WTs. The proposed technique has been validated experimentally on a WT drive train test rig. A synchronous or induction generator was successively installed on the test rig, and both mechanical and electrical fault like perturbations were successfully detected when applied to the test rig.

/pdf/cost-effective-condition-monitoring-for-wind-turbines-1k1sap7pf9.pdf

Cost-Effective Condition Monitoring for Wind Turbines

Driver`s feeling is variously affected by lots of components such as engine, frame, wheels, and seats during the operation of automobiles. The main objective of this research is to identify the correlation between subjective evaluation and vibration metrics that was set by ISO to investigate development of the car vibration quality index using multiple linear regressions(MLR). A previous research related with automotive vibration quality used the method of calculating acceleration values of the point of a seat, a seat back, foot as RMS for objective evaluation. The automotive comfort is determined by RMS values. In comparison with the previous research, this study includes not only the vibration metrics, but also subjective values by jury evaluation. By indentifying the correlation between subjective evaluation and vibration metrics, the automotive vibration quality index is developed through MLR. Based on the results of this study, the proposed the automotive vibration quality index which developed through MLR will be helpful to obtain objective and reliable automotive comfort values.

/pdf/development-of-vibration-index-for-the-objective-evaluations-1hwx36f6be.pdf

Development of Vibration Index for the Objective Evaluations of Idle Vibration Quality in a Passenger Car

LCD 제조 업체의 대량 생산 시설이 가전 제품과 모바일 통신 기기 같은 시장에서 기하 급수적으로 증가하는 LCD 패널에 대한 수요에 대응하기 위해 효율적으로 운영되어야 하고, 합리적인 결함 관리 시스템의 활용은 생산성과 수익성 측면에서 중요하다. 각종 생산 설비는 유기적 통일체로 연속해서 연결된다. 그 중 하나가 심각한 오류가 있다면, 전체 생산라인은 예상 보수와 조사가 기계 구성 요소 간의 연쇄 반응에 의한 치명적인 손상을 방지하기 위해 끝날 때까지 천천히 작동되어야 한다. 한편, 고급 신호 처리 기술을 기반으로 한 다양한 방법론이 개발되고 있고 생산 시설에서 기계 구성 요소의 결함 검출에 사용되는데, 일반적으로 진동, 음향 방출 그리고 주요 신호로써의 전기적 신호를 바탕으로 한다. 위에 있는 방법론과는 달리, Fig. 1 에서 보여진 것처럼, 이 연구는 이동하는 로봇으로부터 비정상적인 작동 음이 작동할 때 인간 청각 인지와 같은 결함 진단 시스템을 사용 함으로써 엔지니어의 불확실한 결정을 최소화하는데 있다. 비록 이것의 민감도는 다른 인접해 있는 설비가 작동하는 소리와 주위의 소음으로 인해 약간 낮지만, 이것의 장점은 움직이는 공정로봇(transfer robot) 의 작업 공간 내부에 마이크로폰만 사용 함으로서 동시에 여러 오류의 감시가 가능하다는 점이다. 이 연구의 초기 과정으로, 주위의 소음과 다른 인접해 있는 설비가 작동하는 Key Words: Abnormal Operating Sound(비정상 작동 소음), Wavelet Packet Transform(웨이블릿 변환), Artificial Neural Network(인공 신경망회로), Shannon Entropy(섀넌 엔트로피) 초록: LCD 생산라인의 공정 로봇에서 방사되는 비정상 작동 소음은 로봇의 결함 탐지에 사용된다. 이 신호의 장점은 상대적으로 낮은 민감도에 비해 단지 마이크로폰을 이용하여 다수의 결함을 확인할 수 있는 것이다. 결함요소 추출을 위한 웨이브렛 변환(WPT)과 불량의 분류를 위한 인공신경망 회로(ANN)이 본 논문에서 사용되었다. 결과적으로, 비정상 작동 소음이 기계요소의 결함 진단에 효율적으로 사용될 수 있다. Abstract: Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

Condition Monitoring of an LCD Glass Transfer Robot Based on Wavelet Packet Transform and Artificial Neural Network for Abnormal Sound

This paper identifies a cause of pick up noise in a laser printer and a relationship between pick up noise and major components related to pick up noise. A pick up sound is affected by many components such as spring force, spring constant, and friction coefficient. Objective evaluation for the pick up sound is difficult because of back ground sound such as operating sound. Especially, a sound between a friction pad and a paper in the process of printing has become an essential issue in an aspect of quality evaluation. However the existing criteria for determining the above sound have solely relied on human`s subjective judgments; which highlights the requirement to objectify these criteria. In this paper, the standard of existing pick up noise is established by finding the tonality, which is a psychoacoustic parameter, of noticeable limit sound level. Based on the findings of the method, the study has found factors which cause pick up noise and suggests the substitution of following components of printers such as spring constants, spring force, and the quality of friction pads. As a result, it is confirmed that the proposed pick up noise index has usefulness to classify whether existence of pick up noise with an objective evaluation and not to occur the noise based on design optimized combination of laser printer components.

Identification of Pick up Sound for Laser printers Based on Psychoacoustic Parameters

This paper presents experimental results of source identification for a non-minimum phase system. Generally, a causal linear system may be described by matrix form. The inverse problem is considered as a matrix inversion. Direct inverse method cannot be applied for a non-minimum phase system, because the system has ill-conditioning. Therefore, in this study the SVD inverse technique is introduced to execute an effective inversion. In a non-minimum phase system, its system matrix may be singular or near-singular and has very small singular values. These very small singular values have information about a phase of the system and ill-conditioning. Using this property we could solve the ill-conditioned problem of the system and then verify it for the practical system (cantilever beam). The experimental results show that the SVD inverse technique works well for a non-minimum phase system. This inverse technique can be applied to the estimation of the magnitude of impact force, which becomes often a cause of damage to a mechanical system.

Damage Identification of a Mechanical Structure with a Non-Minimum Phase Based on Singular Value Decomposition

Health monitoring of the reciprocating compressor in a refrigera tor is important for economic mass production. Traditionally, refrigerator production companies have used the auditory system of a skilled person for health monitoring. However, a skilled pe rson cannot check all the compressors on a production line. If sampled compressor is judged as faul ty, thousands of compressors which are manufactured at that time and place are re g ded as faulty; thus, money is lost. In order to save costs, it is necessary to check all the compress ors in the production line. To do so, we installed a controlled vibration measurement system and real time signal analysis to check faulty or healthy compressors. The signal processing tools used for feature ex raction for pass or fail of the compressor are spectral density analysis, weighted Wigner-Vill distribution, time domain analysis and wavelet analysis, and signal processing for pattern analysis f or pass or fail in a neural network. The result of the application has been very successful, and currently the s stem is working very well on the production line. This paper presents the whole history for health moni toring of the reciprocating compressor in a refrigerator. The work is in cooperat ion with LG Electronics Company. Introduction In a mass production line, health monitoring is very important to save on manufacturing cost. In the health monitoring of the reciprocating compressor of a refrigerator , skilled person discriminates faulty compressors depending on his or her experience. In most cases, t his method is useful since it is easy. However, sometimes, a wrong decision may result, depending on the person’s emotional condition. What is needed is a computer-based health monitoring system f or more robust diagnosis of the faulty compressor. Many research papers for condition monitoring have been published together with the rapid development of computer engineering and digital signal processing techni qu s [ 1-10]. These results are widely applied to the production line. The digital signal processing tools used for the these jobs vary from simple Fourier transform, power spectra densit y analysis [ 1] and statistical analysis of time series [ 2] to cepstrum [ 3], adaptive signal processing [4], bilinear time frequency [ 5], wavelet transform [ 6], time averaging method [ 7], higher-order time frequency [ 8], higher order spectra [ 9] and artificial neural network [ 10]. Most of the tools have been used for feature extraction of the fault condition. The artificial neural network (ANN) has been used for pattern analysis using these feature extractions. In this paper, statistical data of t ime series, power spectra density (PSD) analysis and Wigner-Ville analysis are used for feature extr action for the faulty compressor. The ANN is used for pattern analysis of the condition of the compressor. The input data of the ANN are the results of feature extraction obtained from power spectra density anal sis. The target of the ANN is the decision of a skilled person who has a great deal of experience egarding the condition of the compressors. The number of sample compressors in this research is 140 : 135 sample compressors are healthy and 5 sample compressors are faulty. Key Engineering Materials Online: 2003-07-15 ISSN: 1662-9795, Vols. 245-246, pp 391-398 doi:10.4028/www.scientific.net/KEM.245-246.391 © 2003 Trans Tech Publications Ltd, Switzerland All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications Ltd, www.scientific.net. (Semanticscholar.org-13/03/20,20:46:18) 2 Title of Publication (to be inserted by the publisher) Artificial Neural Network (ANN) Theory ANN’s simulate a biological neural system very loosely. They ar massively parallel, and they can process not only crisp but also noisy or incomplete data. There is an e xtensive literature on ANN. In this paper the main attention is paid to the back-propagation neural netw orks [11], which are mostly used in the analysis of mechanics problems. The main goal of back-propag ati n neural networks is mapping of input, i.e. vector x ∈RN into output, i.e., vector y ∈RM. This can be written in short 1 1 × × → M N y x (1a)

Sang-Kwon Lee

Papers

Development of Vibration Index for the Objective Evaluations of Idle Vibration Quality in a Passenger Car

Condition Monitoring of an LCD Glass Transfer Robot Based on Wavelet Packet Transform and Artificial Neural Network for Abnormal Sound

Identification of Pick up Sound for Laser printers Based on Psychoacoustic Parameters

Damage Identification of a Mechanical Structure with a Non-Minimum Phase Based on Singular Value Decomposition

Health Monitoring of Reciprocating Compressor in Refrigerator Based on the Artificial Neural Network