Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts

Abstract

Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator through a series of relays over a multi-hop link. In this paper, we consider a network with sensor nodes and relay nodes where the relay nodes can forward the estimated values to the remote estimator. An event-triggered remote estimator of state and fault with the corresponding data-forwarding scheme is investigated for stochastic systems subject to both randomly occurring nonlinearity and randomly occurring packet dropouts governed by Bernoulli-distributed sequences to achieve a trade-off between estimation accuracy and energy consumption. Recursive Riccati-like matrix equations are established to calculate the estimator gain to minimize an upper bound of the estimator error covariance. Subsequently, a sufficient condition and data-forwarding scheme are presented under which the error covariance is mean-square bounded in the multi-hop links with random packet dropouts. Furthermore, implementation issues of the theoretical results are discussed where a new data-forwarding communication protocol is designed. Finally, the effectiveness of the proposed algorithms and communication protocol are extensively evaluated using an experimental platform that was established for performance evaluation with a sensor and two relay nodes.

Hybrid Kalman Filtering Algorithm With Stochastic Nonlinearities and Multiple Missing Measurements

“…4). The optimal estimationx k+1|k+1 can be computed by substituting the equations (17), (18) and (33) into (16). 5).…”

“…In recent years, along with the accelerated development of network technology and the spread of computer application, the network phenomena are taken into account in various systems [24]- [27]. Due to the long distance data transmission and unreliability of the communication network, the stochastic disturbances, which are described by the stochastic nonlinearities, may exist in the systems and the sensor measurement of the system may experience the unexpected missing measurements (packet dropout) in the transmission process [28]- [33]. Hence, it is necessary to address the problem of the stochastic nonlinearities and the missing measurements to improve the control performance for the practical systems.…”

Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

“…k . Combining (27), (28) and 30, we derive the minimized upper bound on the fault estimation error covariance as follows…”

“…Very recently, considerable research attention has been paid on the event-triggering fault estimation issue owing to its vital role in the practical engineering. Accordingly, the event-triggering fault estimation problems have been investigated for various systems, such as nonlinear systems with missing measurements [ 27 ], stochastic systems subject to nonlinearities and packet dropouts [ 28 ], and stochastic systems with deception attacks [ 29 ]. However, to the best of the authors’ knowledge, the event-triggering state and fault estimation (ETSFE) problem for nonlinear systems with sensor saturations has not been fully studied, which constitutes the main motivation of this paper.…”

An Event-Triggered Fault Detection Approach in Cyber-Physical Systems with Sensor Nonlinearities and Deception Attacks

“…This is why a health management unit for CPSs should be established for health monitoring and diagnosis. The reliability problems are not new in the NCSs field, in particular in the areas of model-based fault diagnosis approach [8][9][10]. In the model-based fault-detection approach, state observers or filters are usually used to generate residual signals, which are smaller than pre-designated thresholds when no faults exist [11].…”