Multiple sensor fault diagnosis by evolving data-driven approach

El-Koujok, Mohamed; Benammar, Mohieddine; Meskin, Nader; Al-Naemi, M.; Langari, Reza

doi:10.1016/j.ins.2013.04.012

Cited by 35 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of Chessa's algorithm, Elhadef proposed some improved algorithms-adaptive Distributed Self-Diagnosis Protocol (DSDP) and mobile DSDP. 14 Similar methods of regularly checking nodes' behavior in WSNs to detect abnormal nodes were discussed in You et al 15 and El-Koujok et al 16 Although the test-based algorithms can improve the detection accuracy, they also bring challenges in terms of the communication cost and computational complexity.…”

Section: Introductionmentioning

confidence: 98%

An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

Yang

Zhang

Qian

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

In wireless sensor networks, time synchronization is an important issue for all nodes to have a unified time. The wireless sensor network nodes should cooperatively adjust their local time according to certain distributed synchronization algorithms to achieve global time synchronization. Conventionally, it is assumed that all nodes in the network are cooperative and well-functioned in the synchronization process. However, in cognitive radio wireless sensor networks, the global time synchronization process among secondary users is prone to fail because the communication process for exchanging synchronization reference may be frequently interrupted by the primary users. The anomaly nodes that failed to synchronize will significantly affect the global convergence performance of the synchronization algorithm. This article proposes an anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks. The proposed method adopts the statistical linear correlation analysis approach to detect anomaly nodes through the historical time synchronization information stored in local nodes. Simulation results show that the proposed method can effectively improve the robustness of the synchronization algorithm in distributed cognitive radio sensor networks.

show abstract

Section: Introductionmentioning

confidence: 98%

An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

Yang

Zhang

Qian

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…FDI techniques for decoupling disturbances are presented in [9,13,[19][20][21]. On the other hand, several FDI schemes with robustness against modeling errors have been proposed [22][23][24][25]. However, fewer FDI schemes have the capability of working in the presence of disturbances and modeling errors simultaneously unless some restrictive assumptions are made concerning the model uncertainties, [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Structured Residuals Design Techniques for actuator and sensor Fault Detection and Isolation in a Permanent Magnet DC Motor

Antić

Rosić

Djurović

et al. 2023

Preprint

View full text Add to dashboard Cite

The reliability of actuators and sensors is an important topic in many electric motor drives. A comparison of structured residual synthesis methods for actuator and sensor fault detection and isolation (FDI) in a permanent magnet (PM) DC motor, applying two different approaches to the design of primary residuals, is presented in the paper. The first method involves a standard approach and is based on system transfer matrix synthesis. The second, more convenient approach involves the synthesis of primary residuals based on the analysis of subsystems that describe the observed system. Both methods are applicable for linear and stationary systems but were successfully applied to a laboratory system that shows nonlinear and non-stationary properties and is affected by a constant disturbance, thanks to the proposed technique of residual translation with the previous design of internal residuals. A real-time experiment performed on a laboratory setup, which consists of a DC motor, an amplifier designed in the form of a linear electronic circuit, and a Compact RIO 9075 real-time processor, is used to illustrate the drawbacks and advantages of the analyzed approaches.

show abstract

“…Nonlinear phenomena widely exist in various systems, e.g., photovoltaic cell models [1], lithium-ion batteries [2], and stirred-tank reactors [3]. On the identification of nonlinear systems, Ghani et al presented a new method for photovoltaic cells, their method is based on the examination of the power voltage data from which a system of five residual equations are derived and solved via the multi-variable Newton-Raphson method [4].…”

Section: Introductionmentioning

confidence: 99%

Iterative Parameter Identification for Fractional-Order Block-Oriented Nonlinear Systems with Application to a Battery Model

Wang

Meng

2021

Preprint

View full text Add to dashboard Cite

This paper investigates parameter and order identification of a class of block-oriented nonlinear systems. By using the hierarchical identification principle, the system is divided into two subsystems, which are a linear block system and a nonlinear block system. For the purpose of solving the difficulty of estimating two sets of parameter vectors, the over-parameterization method and the key item separation technique are used, respectively. Therefore, a two-stage over-parameterization gradient-based iterative algorithm and a key term separation two-stage gradient-based iterative algorithm are derived. The simulation results indicate that the proposed algorithms are effective. Finally, the proposed method is evaluated through a battery model. The results show well agreement with the real system outputs.

show abstract

Multiple sensor fault diagnosis by evolving data-driven approach

Cited by 35 publications

References 13 publications

An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

Comparison of Structured Residuals Design Techniques for actuator and sensor Fault Detection and Isolation in a Permanent Magnet DC Motor

Iterative Parameter Identification for Fractional-Order Block-Oriented Nonlinear Systems with Application to a Battery Model

Contact Info

Product

Resources

About