Fusion of Model-based and Data-based Fault Diagnosis Approaches

Slimani, A.; Ribot, Pauline; Chanthery, Élodie; Rachedi, Nedjemi

doi:10.1016/j.ifacol.2018.09.698

Cited by 34 publications

(9 citation statements)

References 11 publications

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“…For a complex system, different dependencies [116] (e.g., structural dependence, stochastic dependence and economic dependence) exist among components, thus many approaches also are proposed for multi-component systems [117][118][119]. In addition, in order to enhance the efficiency of model-baed approaches, machine learning techniques are employed in many studies [120][121][122][123].…”

Section: Model-based Approachesmentioning

confidence: 99%

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Ran,

Zhou,

Lin

et al. 2019

Preprint

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This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multiobjective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.

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Section: Model-based Approachesmentioning

confidence: 99%

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Ran,

Zhou,

Lin

et al. 2019

Preprint

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“…The interaction between ADRC and ESO inside a closed loop promotes a great synergy. Finally, the work [22] conducted a dedicated study about model-based and data-driven fusion approaches to FDI.…”

Section: Meta-algorithm Inducers Descriptionmentioning

confidence: 99%

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

et al. 2020

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Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.

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“…The current fault diagnosis methods can be summarized into four categories [8,9]: knowledge-based fault diagnosis [10][11][12], model-based fault diagnosis [13][14][15], signalbased fault diagnosis [16][17][18], and hybrid method-based fault diagnosis (a method that combines two or more methods) [19][20][21][22]. Fault diagnosis for machining centres mainly include diagnosis methods based on fault information monitoring, training models, and fault trees.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

Zhang

Liu

et al. 2021

Applied Sciences

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Fault propagation behaviour analysis is the basis of fault diagnosis and health maintenance. Traditional fault propagation studies are mostly based on a priori knowledge of a causality model combined with rule-based reasoning, disregarding the limitations of experience and the dynamic characteristics of the system that cause deviations in the identification of critical fault sources. Thus, this paper proposes a dynamic analysis method for fault propagation behaviour of machining centres that combines fault propagation mechanisms with model structure characteristics. This paper uses the design structure matrix (DSM) to establish the fault propagation hierarchy structure model. Considering the correlation of fault time, the fault probability function of a component is obtained and the fault influence degree of nodes are calculated. By introducing the Copula and Coupling degree functions, the fault influence degree of the edges between the same level and different levels are calculated, respectively. This paper constructs a fault propagation intensity model by integrating the edge betweenness and uses it as an index to analyze real-time fault propagation behaviour. Finally, a certain type of machining centre is taken as an example for specific application. This study can provide as a reference for the fault maintenance and reliability growth of a machining centre.

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Fusion of Model-based and Data-based Fault Diagnosis Approaches

Cited by 34 publications

References 11 publications

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

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