Open AccessFailure Prediction of Circular Sawing Machines Based on Condition Evaluation and ARIMA Model
Author(s) -
Yao Chen,
Chengtao Yu,
Mengli Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012144
Subject(s) - autoregressive integrated moving average , reliability engineering , computer science , entropy (arrow of time) , key (lock) , function (biology) , engineering , time series , machine learning , physics , computer security , quantum mechanics , evolutionary biology , biology
As the starting point of cutting, sawing is an important link in the production process. Circular saws have high precision and high sawing efficiency. Once a failure occurs, it will bring considerable losses. This paper presents a fault prediction method for circular saw based on state evaluation and ARIMA model. According to the mechanism of failure analysis, considering different parameter indicators for different failures, we first find out the components of the equipment involved in these parameters, and adopt the entropy weight method and trapezoid-triangular membership function model to determine the health status. The key mechanism of the components in poor condition is predicted using the ARIMA model, and the weighted method is used to predict the failure time.