The study emphasizes three kinds of diagnostic knowledge in engineering diagnosis and examines their structure according to their roles. Some requirements and possible measures to improve the diagnostic knowledge are suggested. Generally, information entropy is used as a measure of uncertainty of a system. The study provides broad diagnostic entropy and utilizes it to characterize the inference process of engineering diagnosis. Then sigmoid model and logarithmic model are used to simulate the process. Several simple examples show that the former is better than the latter. In order to verify it more, computer simulation and real diagnostic practice are discussed and they revealed that the sigmoid model is more precise than logarithm model.