Abstract:
In recent years, the field of online learning has received considerable attention for addressing the challenges associated with evolving features in fault diagnosis. Exis...Show MoreMetadata
Abstract:
In recent years, the field of online learning has received considerable attention for addressing the challenges associated with evolving features in fault diagnosis. Existing methods often assume that the dynamics of the feature space are predictable. However, in reality, the change of features is unpredictable, and the appearance or disappearance of features can occur arbitrarily. This presents a significant challenge for learning algorithms to adapt and effectively learn from such data. In this paper, we propose a novel online learning method called Kernelized Online Perceptron-based Online Fault Detection (KOGD) to address the challenge of feature appearance or disappearance in industrial systems. Our proposed model treats each variant of features as a distinct feature set and utilizes multiple kernel gradient descent methods to process them individually. This approach enables the detection of both new and disappearing fault signatures. To improve computational efficiency, we introduce a support vector selection mechanism and a strategy to control the number of cores used in the model. This helps reduce the computational complexity associated with kernelization, making our method more scalable and practical. Additionally, we devise a method to optimize the update of the fault detection model by incorporating historical information from the deleted kernelized perceptrons into the newly selected kernelized perceptrons. This integration allows the model to leverage past knowledge and enhance its performance. Through experimental evaluation on the TEP dataset, the proposed method achieves an accuracy of 89.73 %. The experimental results highlight the effectiveness of the proposed method in handling the changing characteristics of industrial systems and achieving efficient fault detection in realtime.
Date of Conference: 16-19 October 2023
Date Added to IEEE Xplore: 16 November 2023
ISBN Information: