The traditional statistical method is a means of fault prediction based on historical data analysis. It relies on the stability and consistency of data to predict machine tool failures through a large number of statistical samples. However, this approach has limitations when dealing with sudden failures.
In recent years, machine learning methods have made remarkable progress in the field of fault prediction. It predicts failures by learning a large amount of historical data and building corresponding models. Supervised learning methods such as support vector machines and neural networks can predict future failures by training existing data; unsupervised learning methods can analyze machine tool data to discover hidden failure modes.
Preventive maintenance is a commonly used machine tool maintenance method that aims to reduce the failure rate of machine tools and improve the reliability and stability of machine tools through measures such as regular inspection, lubrication and replacement of parts.
Condition monitoring and diagnosis methods identify possible faults by analyzing the working parameters and behavior patterns of machine tools. It can be divided into sensor data based and signal processing based methods. The former uses sensors to monitor the machine tool in real time, record and analyze various parameters; the latter extracts and analyzes the characteristics of the signal to determine whether the machine tool has a fault.
Intelligent maintenance methods include fault diagnosis, predictive maintenance and remote monitoring. Fault diagnosis analyzes machine tool data to identify the cause of the fault and provide maintenance suggestions; predictive maintenance monitors and analyzes machine tool data to predict the time when a machine tool failure will occur and remind maintenance personnel to take appropriate maintenance measures.
Data mining technology can analyze the data of intelligent CNC machine tool automated production line to discover the correlation and abnormal conditions between different links, thereby achieving accurate diagnosis of faults.
Artificial intelligence and expert systems play an important role in intelligent CNC machine tool automated production line fault diagnosis. By leveraging machine learning and artificial intelligence algorithms, expert systems can be built to help solve complex troubleshooting problems.
