MAFCD: Multi-level and adaptive conditional diffusion model for anomaly detection

In the real-world Internet of Things (IoT) systems, a variety of Internet-connected sensory devices, spanning from chemical processing equipment to material handling machinery and server machines are typically monitored with multivariate time series. Anomaly detection in these systems is pivotal for identifying potentially dangerous or unsafe conditions and implementing timely preventive measures. However, the complex contextual dependencies and diversified patterns inherent multivariate time series, such as seasonal fluctuations and trends in industrial processes, present significant challenges for existing anomaly detection methods, which strike a balance between fidelity and diversity in multivariate time series analysis. To address these issues, a novel Multi-level and Adaptive Conditional Diffusion model, called MAFCD, is proposed for anomaly detection across various industrial devices. The architecture of MAFCD is built upon a conditional diffusion model framework, guaranteeing both high-fidelity and diversity in generated multiple time series samples through adaptive fusion strategy and multi-level feature information. In particular, the model offers real-time anomaly occurrences by dynamically adjusting fusion weights across multiple features. Moreover, to enhance model stability, anomaly recognition results undergo weighted aggregation using exponential and symbolic ensemble function through multi-step sampling. Empirical evaluation across four public datasets and its application in an ethylene oxide production process demonstrates the superior performance and practical utility of the proposed MAFCD, underscoring its robust generalization ability.