• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.47-52
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• 2010

Many industrial accidents have occurred over the years in the manufacturing and construction industries in Korea. However, as the service industry has increased continuously, the share of the accident rate in the service industry was 39.07% in 2009, while the manufacturing industry share was 33.73%. The service industry share overtook the manufacturing industry share for the first time. Therefore, this research considers prevention of industrial accidents in the service industry as well as manufacturing and construction industries. This paper describes a procedure and a method to estimate efficient accident rate forecasting and estimated zero accident time in the service industry in order to prevent industrial accidents in the transportation, storage, and telecommunication divisions. This paper proposes a model using an analytical function for the sake of very efficient accident rate forecasting. Accordingly, this paper has develops a program for accident rate forecasting, zero accident time estimating, and calculation of achievement probability through MFC (Microsoft Foundation Class) software Visual Studio 2008 in the transportation, storage, and telecommunication divisions. In results of this paper, ARIMA (Auto Regressive Integrating Moving Average) is regarded as a very efficient forecasting model for the transportation, storage, and telecommunication division. In testing this model, value minimizing the Sum of Square Errors (SSE) was calculated as 0.2532. Finally the results of this paper are sure to help establish easy accident rate forecasting and strategy or method of zero accident time in the service industry for prevention of industrial accidents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.45-53
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• 2006

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the $x^2$ reliability model, which can capture the increasing nature of the failure occurrence rate per fault. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using real data set, SYS2(Allen P.Nikora and Michael R.Lyu), for the sake of proposing shape parameter of the $x^2$ distribution using the degree of freedom, was employed. This analysis of failure data compared with the $x^2$ model and the existing model using arithmetic and Laplace trend tests, Kolmogorov test is presented.