• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.1-11
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• 1980

For an ordinary engineer or researcher there are too diversified branches or even disciplines which have their own jargon to complicate an easy access, Nevertheless in many cases an isomorphism or even identity of notions exist to escape our grasp when expressed in different discipline or context, In this paper the masterwork of Box and Jenkins is introduced to accustom a few terms of statisticiens, to be followed by the technique of smoothing filtering of Wiener and Kalman - Bucy. The advantages of a transform (for example Hadamard) technique are explaned as well as authors personal philosophical views.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.160-165
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• 1993

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identfy cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modelling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Data System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequencyand damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered

• Journal of the Korean Society for Precision Engineering
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• v.1 no.1
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• pp.34-49
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• 1984

The stability of machine tool systems is analyzed by considering the machining process as a stochastic process without decomposing into machine tool structural dynamics and cutting processes. In doing so the time series analysis technique developed by Wu and Pandit is applied systematically to the relative vibration between cutting tool and work- piece measured under actual working conditions. Various characteristic properties derived from the fitted ARMA(Autoregressive Moving Average) Models and those from raw data directly are investigated in relation with the system stability. Both damping ratio and absolute value of the characteristic roots of the AR part of the most significant dynamic mode are preferred as stability indicating factors to the other pro-perties such as theoretical variance .gamma. (o) or absolute power of the most dominant dynamic mode. Maximum aplitude during a certain interval and variance estimated from raw data are shown to be very sensi- tive to the type of the signal and the location of measurement point although they can be obtained rather easily. The relative vibration signal is also analyzed by FFT(Fast Fourier Transform) Analyzer for the purpose of comparison with the spectrums derived from the fitted ARMA models.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.174-182
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• 2002

System identification and controller formulation are essential in dynamic process control. In system identification, data for system identification are obtained, and then they are analyzed so that the system model of the process is built, identified, and diagnosed. In controller formulation, the control equation is derived based on the result of the system identification. There has been much theoretical research on system identification and controller formulation. These theories are very useful when they are appropriately applied. To our regret, however, these theories are not always effectively applied in practice because the engineers and the operators who manage the process often do not have the necessary understanding of required time series analysis methods. On the other hand, because of widespread use of statistical packages, system identification such as estimating ARMA models can be done with little understanding of time series analysis methods. Therefore, it might be said that the most theoretically difficult part in practice is the controller formulation. In this paper, lists of control equations are proposed as a useful tool for practitioners to use. The tool supports bridging the gap between theory and practice in dynamic process control. Also, for some models, the generalized control equations are obtained.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.115-125
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• 2001

We consider the threshold autoregressive moving average(TARMA) process and find a sufficient condition for strict stationarity of the proces. Given region for stationarity of TARMA(p,q) model is the same as that of TAR(p) model given by Chan and Tong(1985), which shows that the moving average part of TARMA(p,q) process does not affect the stationarity of the process. We find also a sufficient condition for the existence of kth moments(k$\geq$1) of the process with respect to the stationary distribution.

• Journal of Korean Society for Quality Management
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• v.15 no.1
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• pp.47-54
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• 1987

It is common that the process capability fluctuates as time passes, but concentrates to the mean value. To keep up process capability with given limits is vital to stability of process. Various control charts, especially ${\sigma}-chart$, have been used for analyzing process capability, but It sometimes can not give distinct answer. So this paper introduces another analyzing method by ARMA (autoregressive moving average) which is originally developed for forecasting, and demonstrates the analyzing methodology through a case study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.25
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• pp.75-82
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• 1992

Standard acceptance sampling plans models the production pricess as a sequence of independent identically distributed Beruoulli random variables. However, the quality of items sampled sequentially from an ongoing production process of ten exhibits statistical dependency that is not accounted for in standard acceptance sampling plans. In this paper, a dependent production process is modelled as an ARMA process and as a two-state Markov chain. A simulation study of each is performed. A comparison of the probability of acceptance is done for the simulation method and for the approximation method.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.557-587
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• 2005

This paper elucidates the limiting Gaussian distribution of a class of rank order statistics {$T_N$} for two sample problem pertaining to empirical processes of the squared residuals from two independent samples of GARCH processes. A distinctive feature is that, unlike the residuals of ARMA processes, the asymptotics of {$T_N$} depend on those of GARCH volatility estimators. Based on the asymptotics of {$T_N$}, we empirically assess the relative asymptotic efficiency and effect of the GARCH specification for some GARCH residual distributions. In contrast with the independent, identically distributed or ARMA settings, these studies illuminate some interesting features of GARCH residuals.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.2
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• pp.31-37
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• 1982

A practical and robust control scheme is suggested for MIMO disciete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, maybe successfully applicable to structured dynamic controllers for plants whose paiameters are slowly timevaiying. The identiflcation of the process paiameters is undertaken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with respect to a standard quadratic criterion.