Quantitative information measurement and application for machine component classification codes

Information embodied in machine component classification codes has internal relation with the probability distribution of the code symbol. This paper presents a model considering codes as information source based on Shannon＇s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.