1. Fault diagnosis algorithm: By collecting and analyzing information such as equipment sensor data and historical maintenance records, model training is carried out by using machine learning and other technologies to improve the accuracy of fault detection and diagnosis.
2. Work order priority algorithm: according to the urgency and importance of the maintenance work order, determine the priority of the maintenance work order to ensure the normal operation of the equipment. The basic idea is to evaluate the impact of work orders on production and service, and determine their priority according to certain rules or sorting algorithms.
3. Fault prediction algorithm: Through the monitoring and analysis of equipment data and usage, the potential fault risk is predicted, and corresponding maintenance measures are taken in advance to avoid equipment failure and ensure the continuity and stability of production.