• Title/Summary/Keyword: Machine diagnosis

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Fault Diagnosis in a Virtual Machine using CORBA (CORBA를 이용한 가상기계에서의 고장진단에 관한 연구)

  • 서정완;강무진;정순철;김성환
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.10a
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    • pp.109-114
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    • 1997
  • As CNC machine tool is one of core elements of manufacturing system, it is much important that it remains without troubleshoots. As a virtual machine is a recent alternative using IT for optimal utilization of CNC machine tool, it is a computer model that represents a CNC machine tool. But a virtual machine is still conceptual. So, in this paper, it is proposed that a virtual machine be a realistic model in the fault diagnosis module. For this purpose, the fault diagnosis system of machine tool using CORBA and fault diagnosis expert system has been implemented. Using this system, we have expections to diagnose exactly and prompty without the restriction of time or location, to reduce MTTR(Mean Time To Repair) and finally to increase the availability of manufacturing system.

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Fault Detection and Diagnosis based on Fuzzy Algorithm in the Injection Molding Machine Barrel Temperature (사출 성형기 Barrel 온도에 관한 퍼지알고리즘 기반의 고장 검출 및 진단)

  • 김훈모
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.11
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    • pp.958-962
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    • 2003
  • We acquired data of injection molding machine in operation and stored the data in database. We acquired the data of injection molding machine for fault detection and diagnosis (FDD) continuously and estimated the fault results with a fuzzy algorithm. Many of FDD are applied to a huge system, nuclear power plant and a computer numerical control(CNC) machine for processing machinery. But, the research of FDD is rare in injection molding machine compare with computer numerical control machine. We appraise the accuracy of the FDD and the limit of the application to the injection molding machine. We construct the fault detection and diagnosis system based on fuzzy algorithm in the injection molding machine. Data of operating injection molding machine are acquired in order to improve the reliability of detection and diagnosis.

Remote Fault Diagnosis and Maintenance System for NC Machine Tools (공작기계용 원격 고장진단 및 보수 시스템)

  • 신동수;현웅근;정성종
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.1
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    • pp.19-25
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    • 1998
  • Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.

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A Case Study on Diagnosis and Checking for Machine-Tools with an OAC (개방형 컨트롤러를 갖는 공작기계에 적합한 진단 및 신호점검사례)

  • 김동훈;송준엽;김경돈;김찬봉;김선호;고광식
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2004.10a
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    • pp.292-297
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    • 2004
  • The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

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Diagnosis Model for Remote Monitoring of CNC Machine Tool (공작기계 운격감시를 위한 진단모델)

  • 김선호;이은애;김동훈;한기상;권용찬
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2000.11a
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    • pp.233-238
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    • 2000
  • CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

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Feasibility Study of Google's Teachable Machine in Diagnosis of Tooth-Marked Tongue

  • Jeong, Hyunja
    • Journal of dental hygiene science
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    • v.20 no.4
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    • pp.206-212
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    • 2020
  • Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

Simplified Machine Diagnosis Techniques Using ARMA Model of Absolute Deterioration Factor with Weight

  • Takeyasu, Kazuhiro;Ishii, Yasuo
    • Industrial Engineering and Management Systems
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    • v.8 no.4
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    • pp.247-256
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    • 2009
  • In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

A Study on an Internet-based Remote Diagnosis System for Machine Tool Failures (인터넷 기반의 공작기계 고장 원격 진단시스템에 관한 연구)

  • Kang, Dae-Chon;Kang, Mu-Jin
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.9
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    • pp.75-81
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    • 1999
  • In order to remain competitive, a manufacturing company needs to maintain the optimal condition of its manufacturing system. Machine tools as an important element of a manufacturing system consist of complex mechanical as well as electronic components. Therefore, diagnosing the troubles of machine tools is a tricky process which requires a lot of experience and knowledge. Since providing machine tool users with necessary services at the right time is very difficult and expensive, a remote diagnosis system is to be regarded as a good alternative, with which users can diagnose and fix the machine troubles. This paper presents a framework for a remote machine tools diagnosis system by combining the world wide web technology and backward reasoning expert system.

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Rejection Study of Mearest Meighbor Classifier for Diagnosis of Rotating Machine Fault (회전기계 고장 진단을 위한 최근접 이웃 분류기의 기각 전략)

  • 최영일;박광호;기창두
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2000.11a
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    • pp.81-84
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    • 2000
  • Rotating machine is used extensively and plays important roles in the industrial field. Therefore when rotating machine get out of order, it is necessary to know reasons then deal with the troubles immediately. So many studies far diagnosis of rotating machine are being done. However by this time most of study has an interest in gaining a high recognition But without considering error $rate^{(1)(2)(3)}$ , it is not desirable enough to apply h the actual application system. If the manager of system receives the result misjudging the condition of rotating machine and takes measures, we would lose heavily. So in order to play the creditable diagnosis, we must consider error rate. T h ~ t is. it must be able to reject the result of misjudgment. This study uses nearest neighbor classifier for diagnosis of rotating $machine^{(4)(8)}$ And the Smith's rejection $method^{(1)}$ used to recognize handwritten charter is done. Consequently creditable diagnosis of rotating machine is proposed.

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Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques

  • Vununu, Caleb;Moon, Kwang-Seok;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.134-143
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    • 2017
  • Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.