Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have developed independently. In general, both techniques have reduction of variability as their objective, although they seek to accomplish these objective in different ways. Integration of SPC and EPC is finding wide recognition and is successfully used in continuous process industries. A simple case of an integral controller is presented here to show the application of MMSE controller to stochastic process. In order to keep the system on target, the actual adjustment in terms of MMSE using integral control can be implemented. This paper demonstrates that SPC, already used for process monitoring in the parts industries, and EPC, now used in process industries, may be integrated in various type industry for more effective control of processes.

In this paper, damage assessment technology based on statistical pattern recognition technology was developed for maintenance of structure and the performance of the developed technology was verified by vibration test. The damage assessment technique uses the improved Mahalanobis distance theory, which is a statistical pattern recognition technique, and developed to take account of the variability between the measured data. In order to verify the damage evaluation performance of the developed technology, a cable damage test was conducted for a cable-stayed bridge. Experimental results show that the developed damage assessment technology has the capability of extracting information that can determine the location of damage due to cable damage.

In this paper, real-time damage assessment technology was developed for detection the damage of bridges in real time and the performance of the developed technology was verified by vibration test. Real-time damage assessment technology was developed by combining statistical pattern recognition technology and simulation technology. In order to verify the developed technology, the earthquake response acquisition experiment was conducted according to the cable damage of the model cable-stayed bridge. As a result, it was confirmed that the developed real-time damage assessment technology can provide information on the location of damaged cable.

This paper aims to assess the structural damage caused by fatigue and random impact load using a statistical pattern recognition technology. For experimental studies, a model of cable-stayed bridge was built, and a shaker was to inflict load on the model. Data for damage detection were obtained from the signals of the model on which repetitive load and earthquake load were imposed. Applying the statistical pattern recognition technology which constitutes a control chart by using Mahalanobis distance, a commonly used method for the measurement of statistical distance, we preliminarily assessed structural damage. On the basis of the damage assessment, we developed a Improved Mahalanobis Distance(IMD) to be applicable to a cable-stayed bridge which was damaged by random impact load. Then, we evaluated its performance for the assessment of structural condition. The evaluation showed that the control chart.