Fault Diagnosis and Prediction of Oil Field Mechanical Recovery Combined with Multi-Source Data Fusion and Machine Learning

Oil field mechanical recovery plays a pivotal role in the oil exploitation process, where the stability and efficiency of the equipment are paramount to controlling costs and maximizing output. However, the diagnosis and prediction of faults pose significant challenges due to the equipment's intricate nature and the demanding conditions of the work environment. In response, this study endeavors to construct a fault diagnosis and prediction system by integrating multi-source data fusion with machine learning techniques. The main goal of this system is to improve the operational efficiency of oil field machinery while reducing the effects of equipment failures on production. Experimental findings reveal that the refined algorithm presented in this research outperforms traditional methods in terms of average accuracy, thereby affirming the model's efficacy and superiority. This advancement not only offers robust support for early fault detection and maintenance strategies in oil field machinery but also paves the way for innovations and improvements in related technological domains.