Vincent Cocquempot

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Hermine Som Judith Idellette, Vincent Cocquempot, Abdel Aitouche,
Fault detection using PDE-based observer in transport flow,
ISA Transactions,
septembre 2023,
ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2023.07.041.
(https://www.sciencedirect.com/science/article/pii/S0019057823003488)
Abstract: This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach.
Keywords: Fault detection; PDE-based observer; Transport flow element; Hyperbolic systems of balance laws; Leakage detection

Article en accès libre, téléchargeable gratuitement jusqu'au 3 novembre 2023 :
https://authors.elsevier.com/c/1hlr54LK~nJDW

Miguel Angel Bermeo-Ayerbe, Vincent Cocquempot, Carlos Ocampo-Martinez, Javier Diaz-Rozo,
Remaining useful life estimation of ball-bearings based on motor current signature analysis,
Reliability Engineering & System Safety, Volume 235, 2023,
109209, ISSN 0951-8320,
https://doi.org/10.1016/j.ress.2023.109209. (https://www.sciencedirect.com/science/article/pii/S0951832023001242)
Abstract: Remaining useful life (RUL) is the crucial element in predictive maintenance, helping to reduce significant costs in factories and avoiding production downtime. This work contributes to a non-intrusive condition monitoring to estimate the RUL of the most critical component in an electromechanical system, which does not depend on previous historical run-to-failure data. Although most of the approaches characterize the behavior of the mechanical components from a vibration analysis, this work is focused on monitoring the characteristic frequencies from the torque oscillations that are transmitted via the three-phase stator currents. In this way, several features can be extracted by processing the current signals. Modeling the behavior of the features in a healthy stage, a health indicator is proposed that measures how well a new sample fits the healthy model. This indicator is processed to ensure an indicator with a monotonically increasing trend. Therefore, a procedure is proposed to estimate the RUL by calculating multiple exponential regressions at each sampling time, considering only incremental samples. Based on a defined failure threshold and exponential regressions, a time-to-failure (TTF) non-parametric distribution is updated online, as more samples are processed, the most likely TTF is revealed over time and used to estimate RUL along with its confidence bounds. The proposed approach has been validated with three experiments performed on a run-to-failure ball-bearing testbed, lasting 65 h, 30 h and 180 h. As a result, the methodology achieved high accuracy in anticipating bearing failures 50 h, 26 h, and 100 h before failure; with an accuracy of 93.78%, 89.49% and 64.31%, respectively. A comparative assessment with reported approaches was carried out using the PRONOSTIA-FEMTO datasets, demonstrating the suitable performance of the proposed approach to converge faster to the real RUL with high accuracy.
Keywords: Prognostics; Remaining useful life; Non-intrusive load monitoring; Motor current signature analysis; Electromechanical system

Azar AT, Smait DA, Muhsen S, Jassim MA, AL-Salih AAMM, Hameed IA, Jawad AJM, Abdul-Adheem WR, Cocquempot V, Sahib MA, Kamal NA, Ibraheem IK.
A New Approach to Nonlinear State Observation for Affine Control Dynamical Systems.
Applied Sciences. 2023; 13(5):3300.
https://doi.org/10.3390/app13053300
Abstract :  In this work, a Nonlinear Higher Order Extended State Observer (NHOESO) is presented to replace the Linear Extended State Observer (LESO) used in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. In the NHOESO, the standard LESO is completed with a two-term smooth nonlinear function with saturation-like characteristics. The proposed novel NHOESO enables precise observation of the generalized disturbances with higher-order derivatives. The stability of the NHOESO is examined with the aid of the Lyapunov method. A simulation of an uncertain nonlinear Single-Input–Single-Output (SISO) system with time-varying external disturbances confirms that the proposed NHOESO copes well with the generalized disturbance, which is not true for other ESOs.
Keywords : Generalized disturbance; Lyapunov method; state estimator; model uncertainty; nonlinear systems; active disturbance rejection control.

Open access  :https://www.mdpi.com/2076-3417/13/5/3300

Abdel Karim Abdel Karim, M. Amine Atoui, Virginie Degardin, Pierre Laly, Vincent Cocquempot
Bus network decomposition for fault detection and isolation through power line communication
ISA Transactions, Elsevier, accepté 15 janvier 2023
Abstract : This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line communication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data.

M. Amine Atoui, Vincent Cocquempot,
Explainable root cause and pathway analysis with robust and adaptive statistics
Computers in Industry, Elsevier
Volume 144, 2023,103770,
ISSN 0166-3615, https://doi.org/10.1016/j.compind.2022.103770. (https://www.sciencedirect.com/science/article/pii/S016636152200166X)

Abstract: Accurate detection of faults is desired to reduce risks and costs. The identification of the propagation path of faults and the system’s variables responsible of faulty operating conditions is also paramount. This paper presents a new alternative to a well-known Bayesian network-based approach to detect and identify root causes in multivariate processes. It deals with the complexity generated by the use of Bayesian network in terms of structure and decision making. The new strategy is straightforward and based on statistical foundations. The new approach revives the interest of Mason, Young and Tracy (MYT) decomposition of quadratic statistics and alleviates considerably the complexity of their upgrades. Comparison with previous approaches and performance evaluation using the Tennessee Eastman process demonstrate the feasibility and interest of the new proposal.
Keywords: Process monitoring; Root cause identification; Bayesian belief network

Dernier ouvrage paru en Novembre 2020

https://www.springer.com/gp/book/9789811590726
Dongsheng Du, Shengyuan Xu, Vincent Cocquempot. Observer-Based Fault Diagnosis and Fault Tolerant Control for Switched Systems. Springer Nature. Springer Singapore, 323, pp.255, Studies in Systems, Decision and Control, 978-981-15-9072-6. ⟨10.1007/978-981-15-9073-3⟩. ⟨hal-02945799⟩