来源：http://www.tayfsk.com/ 发布时间：2023-05-30 浏览次数：
The mechanical and electrical systems of industrial welding robots are complex, with large working areas and fast operating speeds, making it difficult to accurately predict all the hazards that may occur under different working conditions. Especially during manual teaching programming or maintenance, any operational errors and unknown system defects may cause equipment damage or even major safety accidents. So how to perform predictive maintenance on welding robots? Shandong CNC welding equipment manufacturer analyzes for you:
Classification of Predictive Maintenance
Predictive maintenance can be divided into two types: device mechanism based and data-driven prediction based. The prediction based on mechanism model is to establish the relationship between equipment failure and mathematical models such as mechanical dynamics, thermodynamics and metrology to predict equipment failure, while the data-driven model is to form an intelligent decision-making model through learning and training a large amount of data.
The former is more suitable for rotating equipment, while data-driven models are more suitable for prediction and control of complex uncertain systems and black box processes. Data-driven models are prediction and control methods based on empirical data statistical relationships or statistical features, and their effectiveness depends on the accuracy and response frequency of input data.
Implementation process of predictive maintenance
Obtain full life data of the target equipment or system through simulation and sensor measurement.
This includes data preprocessing and feature extraction, filtering and organizing the data, identifying working condition information in the data, removing non important variables, and obtaining decay features through feature extraction methods for model training.
Delete attributes that have no useful information for the task, design feature extraction methods for sensor data, establish a computer predictive maintenance model based on sensor data feature extraction, and conduct comparative experiments.
Select appropriate machine learning models and train them using processed full life data to obtain models that can accurately predict equipment failures or system remaining life under different operating conditions.
Based on the simulation of system fault prediction, the feasibility of maintenance and repair strategies can be verified, and the demonstration results can be imported into the expert strategy library as a solution.
Deploy predictive maintenance algorithm models, diagnose faults based on feedback information from condition identification data, and determine maintenance strategies for equipment or systems; Perform multi-dimensional analysis based on on-site working conditions data to predict service life and determine maintenance and upkeep strategies for equipment or systems.
To address the standardization of operation and maintenance in the large-scale application process of welding robots, the necessity of welding robot operation and maintenance regulations is demonstrated by analyzing the current application status, significance, and development prospects of welding robots. At the same time, the shortcomings and problems of welding robots in daily applications are analyzed, highlighting the importance of welding robot operation and maintenance regulations. For more related matters, come to our website http://www.tayfsk.com consulting service