• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.43-52
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• 2001

Condition monitoring plays a vital role since it sustains the reliable operation of industrial plant and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature, and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, we constructed a rotor system where various types of functional machine failures occurred frequently in industry were induced. Characteristics of the machine failure were monitored simultaneously by the on-line measurement of vibration, wear and temperature. Result showed that these parameters responded differently to the induced functional machine failure. The availability of each parameter on effective condition monitoring was discussed in this work.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1601-1607
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• 2010

This paper presents FMEA (Failure Mode Effect Analysis) for maintenance of mail sorting machine which is for automatic sorting of mail. We suggest the update method of regular diagnosis item and period for maintenance of mail sorting machine using the risk priority number which is calculated by severity, occurrence, and detection of failure mode of mail sorting machine, and shows FMEA adoption example of letter sorting machine. This paper also describes the current maintenance system and status of mail sorting machine in the domestic postal logistics environment, and FMEA adoption step. The proposed maintenance using FMEA will be adapted for more easy and efficiency maintenance of mail sorting machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.90-93
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• 2002

In view of reliability assessment, the failure mode analysis by performance tests for machine totals is researched in this study. First, the error analysis with circular movement test data is studied. The various errors and their origins are analyzed by error equations and related parts are investigated. Second, This paper deals with analysis of vibration testing fur machine tools spindle. The various frequency components are classified by FFT and order analysis. The simple measuring devices and error evaluation programs for tests are also developed.

• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.111-121
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• 1997

This study intends to develop a fixture planning module as a part of the planning system for cutting. The fixture module uses machine learning method to reuse previous failure results so that the system can reduce the repeated failures. Machine learning is one of efforts to incorporate human reasoning ability into a computerized system. A human expert designs better than a novice does because he has a wide experience in a specific area. This study implements the machine learning algorithm to have a wide experience in the fixture planning area as a human expert does. When the fixture planner finds a setup failure for the suggested operations by a process planner, it makes the process planner store its attributes and other information for the failed setup. Then the process planner applies the learned knowledge when it meets a similar case so that the planner can reduce possibility of setup failure. Also the system can teach a novice user by showing a failed setup with a modified setup.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.435-439
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• 2002

In view of reliability assessment, the failure mode analysis by performance tests for machine tools is researched in this study. First, the error analysis with circular movement test data is studied. The various errors and their origins are analyzed by the error equations and then related parts and failure modes are investigated. Second, This paper deals with analysis of vibration testing for machine tools spindle. The various frequency components are classified by fourier transform and order analysis. The simple measuring devices and web-based analysis programs for each test are also developed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.1
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• pp.8-13
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• 2010

The shaft surface machining is a popular machine for studying descale machine design and process in automobile industry. In this study, the descale design machine of cutting shaft surface was conducted for the detection of a tool failure in surface process. Induction harden surface is used as analyzing function to detect a sudden change in cutting process level. A preliminary stepped workpiece which had a hard condition was cut by the surface tool and a tool process obtained cutting force machine. At machine failure, the results were suddenly increased and the detailed surfaces were extremely obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1003-1010
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• 2013

In this paper, TCP/IP based Machine-to-Machine (M2M) communication uses CDMA/GSM network for data communication. This communication method is widely used by offshore moored buoy for data transmission back to the system server. Due to weather and signal coverage, the TCP/IP M2M communication often experiences transmission failure and causing data losses in the server. Data losses are undesired especially for meteorological and oceanographic analysis. This paper discusses a software approach to minimize M2M data losses by handling transmission failure and re-attempt which meant to transmit the data for recovery. This implementation was tested for its performance on a meteorological buoy placed offshore.