Research on Equipment Failure Prediction and Type Identification Based on a Multi-Task Random Forest Model

Addressing challenges in industrial equipment operation such as the high difficulty of failure identification, the complexity of failure types, and class imbalance, this paper proposes a multi-task learning-based random forest method for failure prediction. This method adopts a staged modeling strategy: first, constructing a failure detection model to perform binary classification determining whether a failure has occurred; subsequently, building a multi-class classification model on the samples identified as failure to achieve fine-grained identification of failure types. To comprehensively evaluate model performance, this paper introduces multiple evaluation metrics including confusion matrices, ROC curves, Precision-Recall curves, and predictive probability distributions. These metrics provide quantitative analysis of the model's classification accuracy, stability, and output reliability. Experimental results demonstrate that the proposed method achieves high accuracy and robustness in both failure detection and type identification tasks. It exhibits particularly good classification capability when handling failure categories with small sample sizes (underrepresented classes), indicating considerable potential for engineering applications and broad prospects for implementation.