Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying; Kammammettu, Sanjula

doi:10.1109/ssci.2017.8285166

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…One of the key advantages of ICA is its ability to model non-Gaussian data. Unlike other linear transformation techniques, such as Principal Component Analysis (PCA), which assumes that the components are Gaussian, ICA does not make any distributional assumptions [39]. This flexibility enables ICA to handle real-world data exhibiting non-Gaussian variations and complex dependencies.…”

Section: Independent Component Analysismentioning

confidence: 99%

Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis

et al. 2023

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Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on I2d, I2e, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems.

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Section: Independent Component Analysismentioning

confidence: 99%