Anomaly Prediction Method for Complex Scenarios Based On Multi-Modal Causal Intervention

Abstract

Anomaly prediction is vital for ensuring the safe operation of pump station facilities. However, traditional models are often hampered by spurious correlation stemming from unobservable confounding factors, thereby degrading detection accuracy. To address this, this paper proposes the Causal Intervention with Front-door Adjustment Model (CIFAM) to eliminate spurious correlation in complex features. Initially, CIFAM integrates video and signal data, utilizing graph attention networks and clustering to construct a multi-level structural attribute graph that represents dynamic operational dependencies. The model incorporates causal dilated convolution to capture long-term memory while preventing information leakage, alongside a random edge dropping strategy to simulate causal intervention. By applying the front-door adjustment criterion, CIFAM extracts robust intermediate representations to block interference paths. Furthermore, contrastive learning and Adaptive Instance Normalization (AdaIN) are employed to decouple features, bolstering model robustness in complex scenarios. Experimental results demonstrate that CIFAM achieves state-of-the-art performance across multiple industrial benchmarks, specifically reaching 91.1% accuracy and a 90.6% Micro-F1 score on the OURS dataset. The model exhibits exceptional robustness and sample utilization efficiency. Furthermore, with minor modifications, this algorithm can be generalized to various monitoring scenarios, such as power generator set monitoring, semiconductor production lines, and UAV-assisted monitoring for oil pipelines or high-voltage power line maintenance.