Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

Tidriri, Khaoula; Chatti, Nizar; Verron, Sylvain; Tiplica, Téodor

doi:10.1016/j.arcontrol.2016.09.008

Cited by 309 publications

(162 citation statements)

References 121 publications

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“…On the other hand, recent advances in sensor technology [9] have enabled significant growth of data collection and analysis, leading researchers to focus on data-driven methods. The paper proposes the combination of a data-driven approach with a model-based approach [10] [11] to build a modular approach in order to help designers to generate models from collected data, based on a set of patterns representing standard functions of the real manufacturing processes. The core elements of a digital model are implemented by the fusion of sensor-based data, physical-based patterns and datadriven models.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Semeraro

Lezoche

Panetto

et al. 2019

IFIP Advances in Information and Communication Technology

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The digital transformation of collaborative networked manufacturing enterprises requires building and applying digital models representing the set of resources and processes knowledge. Modelling such digital copy of the physical system to perform real-time validation and optimization is quite complex and thus needs a big amount of data and some modelling patterns representing the operational semantics of the modelled elements. Generally, the modelling action has a specific application type. For this reason, the core challenge of the digital transformation modelling is to create a modular "digital model", namely a decomposable and re-composable model, towards different applications. The authors propose an approach based on the combination of data-driven and modeldriven approaches, to identify and formalize modelling patterns, that combine for developing a modular executable model of the studied system.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Semeraro

Lezoche

Panetto

et al. 2019

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…With the development of the chemical industry, chemical processes have become increasingly complex, including the study of diverse uncertainties as well as non‐linear problems . Many potential or minor failures may cause unimaginable consequences.…”

Section: Introductionmentioning

confidence: 99%

Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

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In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F1‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.

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“…The diagnostics aims at identifying the health state of the system and can be considered the first step for the prognostic process, which in turn aims at identifying the remaining useful life (RUL) of the system. Different methodologies and approaches for the diagnostic process have been proposed in many engineering fields and they can be separated into two main classes: data-driven approaches and model-based approaches [3]. In most applications, the development of a numerical model which can be exploited for diagnostics is a difficult task; therefore, data-driven approaches are preferred.…”

Section: Introductionmentioning

confidence: 99%

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

Casoli¹,

Bedotti²,

Campanini³

et al. 2018

Energies

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Condition monitoring has been an active area of research in many industrial fields during the last decades, particularly in fluid power systems. This paper presents a solution for the fault diagnosis of a variable displacement axial-piston pump, which is a critical component in many hydraulic systems. The proposed methodology follows a data-driven approach including data acquisition and feature extraction and is based on the analysis of acceleration signals through the theory of cyclostationarity. An experimental campaign was carried out on a laboratory test bench with the pump in the flawless state and in faulty states. Different operating conditions were considered and each test was repeated several times in order to acquire a suitable population to verify data repeatability. Results showed the capability of the proposed approach of detecting a typical fault related to worn slippers. Future works will include tests in order to apply the approach to a wider set of faults and the development of a classifier for accurate fault identification.

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Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

Cited by 309 publications

References 121 publications

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Chemical process fault diagnosis based on enchanted machine‐learning approach

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

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