Toshiro Terano

Diagnosis of Engine Trouble by Fuzzy Logic

Abstract This paper consists of two parts. The first one is the principal component analysis of engine trouble. The authors investigate the reports of engine trouble of 120 large merchant ships over three years, and 89 items such as the location of failure, kind, damage, detection method, estimation, diagnosis, counterplan and economical loss are analyzed. As a result, it becomes clear what kinds of trouble are most effectively avoided, and what kinds of performance are expected for the safety system. The second part is the development of a new algorithm of diagnosis by fuzzy logic. The reason why the authors adopt fuzzy set theory is that the human perception and decision, which are very subjective and fuzzy, play the leading part in case of trouble. In this safety system, the signal from the mechanical sensors and that from the human sensory are processed together. For this purpose, a new inverse operation of fuzzy relation is developed. The results of diagnosis are indicated on C.R.T. as short sentences, which tell the operator clearly or vaguely the locations, causes and countermeasures of the trouble.

Modeling of Blast Furnace Conditions Through Factor Analysis

Abstract Successful static and/or dynamic models have been constructed for most industrial processes. But for a blast furnace, modeling is known to be very difficult because of complexity of internal conditions. The present research aims at constructing a practical model of the blast furnace by statistical multivariate analysis of operational data without analyzing complex internal reactions in the furnace. Factor analysis indicates that five dominant factors originally composed of 22 variables can closely represent the furnace condition. The models for these factors can successfully represent the medium frequency variations with the time constant of about two days.

Structural Model of Misoperation

Abstract In this report, general factors which cause human errors during plant-operation are chosen and these factors are classified into some groups by K.J. Method. Using the above results, a questionnaire is made in order to know the importance and the relation of these factors. The replies submitted to the questionnaire are analyzed by DEMATEL Method and the major factors of misoperation are summarized in the form of the structural model. Two case studies are mentioned, one of which is the misoperation in transportation and the other is in hot strip mill. From these results, the ways how to prevent misoperation are discussed.

CONDITION MONITORING OF MARINE ENGINES BY MEANS OF FACE PATTERN METHOD

The face pattern method is considered to be preferable for marine engine monitoring because it is suitable for understanding correlations of multi-variables at a glance and for noticing their sensitive changes in details. The state variables in abnormal states of a marine turbine plant system and a diesel one are displayed on a cathode ray tube by the face pattern method. Order from NSFI as No. 22482.

Failure Diagnosis of Plants in Operation

Abstract When plants are running normally, the ordinary process controllers, which have been developed till now, act excellently, but they are almost useless in an abnormal condition. Usually, simple automatic trip systems are equipped only for emergency use. These emergency control systems are so poor that the human operator is indispensable even in fully automated plants. On the other hand, if we equip the plant with many fault detectors, the plant may frequently make unnecessary halts due to noise or troubles in detectors. The purpose of this paper is to develop a new type emergency control system which keeps the plant safe while making the down time minimum. The new system has two major actions. One is the automatic diagnosis finding the place of troubles, either in the plant side (including normal state) or in the detectors’ side. The other action is the prediction of the plant state if it goes into the dangerous state or not. This is very important, because most of the mis-downs of plants are caused by the temporary fluctuation of some controlled variables. The basic idea of the diagnosis is the combination of “maximum likelihood” and “two out of three”. The prediction is done by “simulation”. After building the emergency control system, the authors tested it with a steam plant. This experiment gave fine results. The artificial troubles caused by us were completely diagnosed and predicted.