Dynamic System Fault Diagnosis Under Sparseness Assumption

Abstract

Dynamic system fault diagnosis is often faced with a large number of possible faults. The purpose of this paper is to propose an efficient method for such situations. To avoid intractable combinatorial problems, sparse estimation techniques appear to be a powerful tool for isolating faults, under the assumption that only a small number of possible faults can be simultaneously active. However, sparse estimation is often studied in the framework of linear algebraic equations, whereas model-based fault diagnosis is usually investigated for dynamic systems modeled with state equations involving internal states. The main contribution of this paper is a link between these two formalisms through efficient and reliable algorithms, mainly relying on advanced analyses of residuals generated with the Kalman and Kitanidis filters. Based on these results, it becomes straightforward to solve fault diagnosis problems by applying well known sparse estimation techniques, in the framework of general time varying state-space systems involving unknown inputs.

Electric Cable Insulator Damage Monitoring by Lasso Regression

“…A similar result regarding the updated filter error was presented in [19], in a case where the Kalman innovation does not exist because of the involvement of unknown inputs. In what follows, direct proof of (20) and ( 21) is presented.…”

“…To efficiently solve such a particular parameter estimation problem, sparse regression techniques will be applied [16][17][18]. To increase the numerical efficiency, sparse regression will be used in association with some particular transformations of dynamic systems [19]. To our knowledge, this proposed solution is the only electrical measurement-based method for the efficient detection, location, and quantification of localized insulator failures.…”

MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis

“…Studies utilizing machine learning and cyber-physical systems (CPS) for collaborative diagnostics, 1 the FWA-BPFD model combining fireworks algorithms with neural networks, 28 and multi-group fault detection and diagnosis (FDD) schemes, 29 provide incremental advancements but often overlook the comprehensive interrelationships between variables and the integration of multimodal data. Dynamic system fault diagnosis using sparse estimation technologies demonstrates potential in managing numerous fault possibilities, 30 yet the integration of knowledge graphs, which could enhance model scalability, is notably absent. Existing research typically focuses on fault diagnosis of single devices or components, with scant consideration given to system-level fault correlation analysis.…”