Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.

In industrial systems, ensuring operational reliability hinges on effective fault detection and root cause analysis. Traditional data-driven fault detection, employing AI to construct prediction models, often falters in accurately identifying true root causes. This limitation arises when variables with high prediction residuals are merely symptomatic rather than direct causes. We present Deep Root Cause Analysis (DRA), a hierarchical neural network. The first model predicts time series and identifies anomalies with high residuals, while the second regresses these residuals from the first model, generating a saliency map that highlights potential root causes that contribute to the residuals. DRA exhibits promising capabilities in providing detailed insights, overcoming the shortcomings of traditional deviation-based approaches. Experiments conducted on the Tennessee Eastman process dataset showcase DRA’s superiority in detecting various process faults and pinpointing their root causes, thereby enhancing precision in complex industrial settings.

Today, voluminous multivariate time series data collected from sensors provides tremendous benefit for understanding of modern industrial systems such as power plants, wind turbines and aircrafts. However, the dynamic and complex nature of these systems, as well as the lack of prior knowledge impose challenges in perceiving different system behaviors from the time series data. To handle these issues, time series clustering has become one of the key analysis techniques. Nevertheless, the data nonlinearity, varying lengths and high dimensions of industrial time series could hinder the quality of clustering. To deal with these challenges, we propose Deep Time Series Sketching (DTSS) model. This model is a representation learning model based on temporal convolutional networks that perform on a sliding window basis along time series to learn the windows’ embeddings. The sequence of embeddings is then fed into embedding sketching to obtain its sketch. Such sketch is a descriptor of the whole time series and will be fed into K-means for clustering. Our model is a novel end-to-end hybrid model that incorporates both local and global contextual features. It is able to project multivariate time series with varying lengths into the same latent space. Moreover, we show that our model is able to perform early clustering as it can assign real-time label without seeing the whole time series. We test our model on both benchmark and real world industrial datasets, and experiments show that our proposed method outperforms popular time series clustering baselines.