Data-driven approaches for impending fault detection of industrial systems: a review

Patil, Amitkumar; Soni, Gunjan; Prakash, Anuj

doi:10.1007/s13198-022-01841-9

Cited by 5 publications

(1 citation statement)

References 113 publications

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“…This work arises from the identification of this critical gap in industrial safety. Although there are solutions that seek to protect against cyberattacks or information security breaches, few directly address the problem of physical sensor sabotage [3,4]. And even fewer do it with a comprehensive approach that combines detection with rapid response.…”

Section: Introductionmentioning

confidence: 99%

Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Sensors

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The Industrial Revolution 4.0 has catapulted the integration of advanced technologies in industrial operations, where interconnected systems rely heavily on sensor information. However, this dependency has revealed an essential vulnerability: Sabotaging these sensors can lead to costly and dangerous interruptions in the production chain. To address this threat, we introduce an innovative methodological approach focused on developing an anomaly detection algorithm specifically designed to track manipulations in industrial sensors. Through a series of meticulous tests in an industrial environment, we validate the robustness and accuracy of our proposal. What distinguishes this study is its unique adaptability to various sensor conditions, achieving high detection accuracy and prompt response. Our algorithm demonstrates superiority in accuracy and sensitivity compared to previously established methodologies. Beyond detection, we incorporate a proactive alert and response system, guaranteeing timely action against detected anomalies. This work offers a tangible solution to a growing challenge. It lays the foundation for strengthening security in industrial systems of the digital age, harmonizing efficiency with protection in the Industry 4.0 landscape.

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Section: Introductionmentioning

confidence: 99%