Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI

Tripathy, Sarthak Manas; Dix, Marcel; Kotriwala, Arzam; Klöpper, Benjamin; Prabhune, Ajinkya

doi:10.1109/bigcomp54360.2022.00051

Cited by 8 publications

(3 citation statements)

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“…The approach is to perturb the data points of the transformed anomalous window for several iterations and check the new perturbed or permuted time series window against the original anomaly detection model for the prediction outcome [25]. This approach detects the normal points in case of maximum prediction drop from an anomalous window and observes and analyzes the features contributing to such change.…”

Section: ) Custom Local Explainer(cle)mentioning

confidence: 99%

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Shapelet-Based Sensor Fault Detection and Human-Centered Explanations in Industrial Control System

Lim,

Kim,

Lee

2023

IEEE Access

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With the development of information and communication technology, industrial control systems (ICSs) that operate in a closed environment are now operating in a smart environment, and external threats are increasing. To predict failure and respond to threats, anomaly detection and fault detection using artificial intelligence (AI) are being introduced, but the issue of the reliability of AI prediction is emerging. In the case of anomaly detection, the operator must check thousands of sensors. In addition, since AI predictions are not always accurate, there are practical operational constraints, so this paper proposes shapelet-based anomaly detection and automatic failure sensor descriptions. When an abnormal situation occurs, the operator can immediately know which sensors caused the problem and how different the sensors appear from the normal patterns. This paper verified it with the HIL-based Augmented ICS Security Dataset (HAI) and Secure Water Treatment (SWaT) Dataset, widely used in the ICS field. In the case of the HAI Dataset, it was confirmed that 95.12% of the failed sensors could be analyzed by extracting and inspecting only 4% of the total sensors. In the case of the SWaT Dataset, only 7% of the total sensors were extracted and inspected, confirming that 84% of the failed sensors could be analyzed. We expect that intuitive explanations and anomaly detection will enable more effective technology operations in industrial environments.

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Section: ) Custom Local Explainer(cle)mentioning

confidence: 99%

“…SimEx aims to compare the anomalous window with all the normal training windows and to find the most similar match [25]. After matching similar data, a comparison with the feature level is performed to determine the difference from similar data.…”

Section: ) Similarityexplainer(simex)mentioning

confidence: 99%

Shapelet-Based Sensor Fault Detection and Human-Centered Explanations in Industrial Control System

Lim,

Kim,

Lee

2023

IEEE Access

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“…The XAI methods characterised above can be applied to various types of data, including time series, which can be treated as tabular data, particularly when derived variables characterising the time series are generated [11,24]. However, in addition to the general XAI methods, other solutions dedicated to time series have been developed.…”

Section: Related Workmentioning

confidence: 99%

Contextual Explanations for Decision Support in Predictive Maintenance

Kozielski

2023

Applied Sciences

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Explainable artificial intelligence (XAI) methods aim to explain to the user on what basis the model makes decisions. Unfortunately, general-purpose approaches that are independent of the types of data, model used and the level of sophistication of the user are not always able to make model decisions more comprehensible. An example of such a problem, which is considered in this paper, is a predictive maintenance task where a model identifying outliers in time series is applied. Typical explanations of the model’s decisions, which present the importance of the attributes, are not sufficient to support the user for such a task. Within the framework of this work, a visualisation and analysis of the context of local explanations presenting attribute importance are proposed. Two types of context for explanations are considered: local and global. They extend the information provided by typical explanations and offer the user greater insight into the validity of the alarms triggered by the model. Evaluation of the proposed context was performed on two time series representations: basic and extended. For the extended representation, an aggregation of explanations was used to make them more intuitive for the user. The results show the usefulness of the proposed context, particularly for the basic data representation. However, for the extended representation, the aggregation of explanations used is sometimes insufficient to provide a clear explanatory context. Therefore, the explanation using simplification with a surrogate model on basic data representation was proposed as a solution. The obtained results can be valuable for developers of decision support systems for predictive maintenance.

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Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels

Melakhsou

Batton‐Hubert

2023

Communications in Computer and Information Science

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Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI

Cited by 8 publications

References 0 publications

Shapelet-Based Sensor Fault Detection and Human-Centered Explanations in Industrial Control System

Shapelet-Based Sensor Fault Detection and Human-Centered Explanations in Industrial Control System

Contextual Explanations for Decision Support in Predictive Maintenance

Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels

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