Batch Process Fault Diagnosis Based on Fuzzy Petri Nets

Abstract

The fuzzy Petri net (FPN) is a new kind of Petri nets that is well developed in recent years. It is difficult to model and simulate the fault diagnosis of batch process because of the alternation between the discrete states and the continuous process. This paper concerned with a new method of complex batch process fault diagnoses based on fuzzy Petri nets. We use Petri nets to model batch processes, and use fuzzy probability to deal with the uncertain information in fault diagnoses and calculate the dependability in fault diagnoses. A simulation of the fault diagnosis system is illustrated with a batch process example, the conclusion of which shows that this method is feasible.

Evaluation of the Clinical Efficacy of Orthodontic Retention of Third Molars After Extraction of First or Second Molars

“…3.1 Promote tissue regeneration at the gap point: the extraction of the first molar will lead to the problem of bone absorption after tooth loss, Mao Yanmin and other scholars [3]The study confirmed that orthodontic movement of adjacent molars to close the missing space of the mandibular first molar does not cause severe loss of molar bone height and may form an increase in bone height. The amount of bone resorption in the missing tooth area does not indicate the treatment risk, and bone grafting is not required before this treatment, which is instructive for clinical practice, 3.2 Preserved healthy teeth: the first molars sprout early, have many pits and grooves, and have complex structure, and are prone to dental pulp disease and periodontal disease.…”