Walk Before You Can Run: Sampling-Rate-Aware Sequential Knowledge Transfer for Multirate Process Anomaly Detection

Anomaly detection plays a vital role in monitoring industrial processes. Despite the extensive development of deep anomaly detection approaches, many assume uniform sampling rates for process variables, yet multirate phenomena are common in practice. The resultant large-scale missing data and varying dynamics of variables with different sampling rates pose a challenge to conventional anomaly detection methods. To this end, this paper developed a SAmpling-Rate-aware sequential Knowledge trAnsfer (SARKA) model for detecting the anomalies in multirate industrial processes. First, the raw multirate dataset is chunked into multiple data blocks, such that samples in each block correspond to a specific sampling rate and can be more readily processed by deep neural networks. Then, a Sampling-rate aware Slow Variational Auto-Encoder (S2VAE) model is developed, in which a sampling-rate-aware slowness principle is devised and integrated to enable both data block- and instance-wise personalized dynamic features characterization. Besides, to alleviate the scarce sample problem in the low-sampling-rate data block due to the multirate phenomena, a Sequential Knowledge Transfer (SKT) strategy is devised to convey the knowledge from the high-rate data to facilitate low-rate data modeling and improve the overall monitoring performance. Experimental results from a real-world coal mill in a thermal power plant multirate process demonstrate the effectiveness of the proposed method.Note to Practitioners—Due to the challenges resulting from large-scale missing data and complex varying dynamics, multirate process anomaly detection is a crucial task in modern industries. To tackle the challenge, this paper presents SARKA, which consists of two modules, i.e., S2VAE and SKT. Among them, S2VAE serves as the base model, which integrates a novel devised slowness principle under the probabilistic framework for improved sampling-rate-aware dynamic features characterization. Besides, SKT is elaborated to bridge the multiple S2VAE base models by conveying the modeling knowledge from those high-rate data to low-rate data, as the amount of the low-rate data is generally scarce to train a valid base model. Therefore, the base model can sufficiently capture both the static and dynamic features of the multirate process, and the SKT further enhances the modeling of the low-rate data, yielding improved performance on the overall multirate process.