Research on fault prediction algorithm and application optimization of mechanical production system oriented to intelligent oil recovery

In this paper, a fault prediction algorithm based on multi-source data fusion and deep learning and its application optimization framework are proposed for the fault prediction of mechanical recovery system of intelligent oil recovery. Mechanical mining equipment is often in harsh environment, with frequent faults and various types, and the traditional fault prediction method has limitations. In this paper, the vibration, current, temperature and other multi-source data are preprocessed, and the dynamic time warping (DTW) algorithm is used to solve the time migration problem, and the outliers are detected based on the improved local outlier factor (LOF) algorithm. Then, multi-scale features are extracted by wavelet packet transform (WPT) and empirical mode decomposition (EMD), and a mixed feature vector including time domain, frequency domain and statistical features is constructed. On this basis, the LSTM-CNN hybrid model is designed and attention mechanism is introduced to improve the accuracy of fault prediction. In order to solve the problem of high false positive rate caused by fixed threshold, a dynamic threshold adjustment method based on reinforcement learning (RL) is proposed, and an online learning framework of edge-cloud collaboration is designed to realize real-time optimization and adaptive adjustment of the system. Experimental results show that the proposed LSTM-CNN-Attention hybrid model is superior to traditional methods and other deep learning models in key performance indicators such as accuracy, F1 value, false alarm rate and reasoning delay. The dynamic threshold adjustment method effectively reduces the false positive rate and the false negative rate. The case application shows that this method significantly improves the fault early warning time and alarm accuracy, reduces the annual maintenance cost of a single pumping unit, and has good economic benefits and application prospects.