Industrial systems such as wind turbines and nuclear facilities depend on continuous sensor monitoring to ensure operational safety and efficiency. However, missing data—caused by sensor malfunctions or environmental interference—can compromise real-time analyses, delaying critical maintenance decisions and system optimizations. Existing imputation methods often struggle to efficiently handle large data volumes in a streaming manner, typically requiring full access to historical time series and consuming excessive energy and processing time. To address these limitations, we propose FlowFill, a fast and energy-efficient streaming framework for recovering missing values in industrial sensor data. FlowFill leverages multiple dynamic sketches to capture local temporal patterns and encodes long-term and diverse data characteristics. Operating incrementally, FlowFill updates its sketches with new data samples, ensuring real-time adaptability. An attention mechanism dynamically prioritizes relevant sketches, dynamically weighting their contributions for precise imputations. This framework excels in computational efficiency, resilience to diverse missing patterns, and energy conservation, making it ideal for industrial applications that demand high-speed processing and energy efficiency. Experiments on industrial datasets demonstrate that FlowFill surpasses state-of-the-art methods in both accuracy and efficiency, establishing a new benchmark for streaming imputation in industrial contexts.

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.

Many algorithms for data mining or indexing time series data do not operate directly on the raw data, but instead they use alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to automatically discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length framework. Extensive empirical tests show that our method is simpler, more general and more accurate than previous methods, and has the important advantage of being essentially parameter-free.

Most algorithms for mining or indexing time series data do not operate directly on the original data, but instead they consider alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this paper, we investigate techniques to discover the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length (MDL) framework. Extensive empirical tests show that our method is simpler, more general and significantly more accurate than previous methods, and has the important advantage of being essentially parameter-free.

Given the pervasiveness of time series data in all human endeavors, and the ubiquity of clustering as a data mining application, it is somewhat surprising that the problem of time series clustering from a single stream remains largely unsolved. Most work on time series clustering considers the clustering of individual time series, e.g., gene expression profiles, individual heartbeats or individual gait cycles. The few attempts at clustering time series streams have been shown to be objectively incorrect in some cases, and in other cases shown to work only on the most contrived datasets by carefully adjusting a large set of parameters. In this work, we make two fundamental contributions. First, we show that the problem definition for time series clustering from streams currently used is inherently flawed, and a new definition is necessary. Second, we show that the Minimum Description Length (MDL) framework offers an efficient, effective and essentially parameter-free method for time series clustering. We show that our method produces objectively correct results on a wide variety of datasets from medicine, zoology and industrial process analyses.

An adaptive reinitialization algorithm for the constant modulus algorithm is proposed that relies on the similarities between the constant modulus and the Wiener equalizer and expands the capabilities of a previous algorithm. The proposed algorithm determines adaptively if the CMA will benefit through reinitialization, and if so, it will calculate new equalizer coefficients leading to the global minimum.