In the real-time monitoring system of stepping explosion-proof electric cylinders, data collected by sensors is like raw materials continuously flowing into a factory. Only through fine processing can the huge value contained in the data be excavated.
When sensors capture data such as displacement, pressure, and temperature of the stepping explosion-proof electric cylinder, this data is quickly transmitted to the controller via a communication module.
As the "primary processing plant" for data, the controller performs preliminary cleaning and preprocessing on the raw data. It removes noise interference, fills possible missing values, and marks and corrects abnormal data. For example, when the data collected by the displacement sensor shows obvious jumps, the controller uses data interpolation algorithms to correct the abnormal data based on the trend of preceding and following data, ensuring the accuracy and continuity of the data. The preprocessed data, like screened high-quality raw materials, lays a solid foundation for subsequent in-depth analysis.
Subsequently, the processed data is transmitted to the host computer software for storage. The host computer software is equipped with a large-capacity database that can long-term store massive historical data. These historical data not only record the operating status of the
stepping explosion-proof electric cylinder but also serve as valuable resources for analyzing equipment performance and predicting failures. To manage and store this data efficiently, the database typically adopts specific data structures and storage methods, such as time-series databases, which can store and query data efficiently in chronological order to meet the high requirements of the real-time monitoring system for data storage and retrieval.
Data analysis technology is the "core tool" for excavating data value. By applying various data analysis algorithms and models, valuable information can be extracted from historical data to achieve precise evaluation of the stepping explosion-proof electric cylinder's equipment status and fault prediction. For instance, statistical analysis methods can calculate statistical features such as the mean, variance, maximum, and minimum of each operating parameter of the electric cylinder. Analyzing these features helps understand the equipment's operational stability and changing trends. Machine learning algorithms like support vector machines and neural networks can be used to establish fault prediction models for the electric cylinder. Through training on large amounts of normal operation data and fault data, the model can identify potential patterns and features in the data. When monitored data exhibits features similar to fault patterns, the model will predict possible equipment failures and issue early warning signals.
In fault prediction, data analysis technology plays a critical role. By analyzing the long-term operation data of the stepping explosion-proof electric cylinder and combining it with the equipment's working principles and structural characteristics, an accurate fault prediction model can be established. For example, by monitoring the pressure and displacement data of the electric cylinder and analyzing their variation laws under different working conditions, when a sudden pressure increase and abnormal displacement are detected, the fault prediction model can determine potential faults such as jamming or overload in the electric cylinder and promptly issue a warning to remind maintenance personnel to inspect and maintain it. Compared with traditional post-failure maintenance, this data analysis-based fault prediction method can detect equipment faults in advance, effectively avoid production losses caused by sudden equipment failures, and significantly improve equipment reliability and production efficiency.
