Efficient monitoring of carbon capture and storage (CCS) systems heavily relies on sensor data. However, sensors are susceptible to potential multiple faults, leading to performance degradation and posing a risk of catastrophic failures. Timely detection of sensor faults in CCS systems is crucial for safe and efficient carbon dioxide (CO2) pipeline operation. This paper addresses the challenge of diagnosing multiple sensor faults in CO2 pipelines by introducing a novel approach based on partial-distributed particle filter (PDPF). The novel distributed-filtering framework aims to reduce the computational complexity while identifying multiple faults in highly nonlinear systems. The proposed PDPF architecture comprises a collection of linear local filters and a nonlinear main filter. More specifically, the algorithm segregates nonlinear computations from local filters and assigns them to the main filter. The main filter handles the time updates involving all nonlinear computations associated with the nonlinear system, while the parallel linear local filters, each equipped with a distinct subset of sensor measurements, perform the measurement updates and combine their estimates via information fusion. As for fault detection and isolation, each local filter utilizes a novel kernel density estimation (KDE)-based approach that analyzes the consistency between model predictions and observed behavior, enabling the identification of sensor faults. Compared to existing methods, this approach reduces the computational requirements and is well-suited for highly nonlinear systems experiencing multiple sensor faults. Additionally, performance assessment via numerical simulations confirms its effectiveness and superiority in comparison to state-of-the-art alternative methods.

This paper explores an innovative method for distributed state estimation aimed at reducing computational complexity while detecting sensor faults in natural gas pipelines. The proposed framework utilizes a partial-distributed ensemble Kalman filter (EnKF), comprising linear local filters and a nonlinear main filter. The main filter handles non-linear computations during the time update, while the simultaneous operation of linear local filters manages linear computations during the measurement update. These local filters generate distinct local state estimates based on their specific sensor measurements, which are then transmitted to an information mixer to compute fault-free state estimates. Moreover, a fault diagnosis strategy is developed using local state variances and residuals. Faulty sensors are identified and isolated by comparing these metrics against a threshold. Additionally, an adaptive thresholding approach is incorporated to enhance effective fault identification. The effectiveness of the proposed technique is demonstrated in systems characterized by high nonlinearity and dimensionality, and featuring simultaneous multiple sensor faults, through extensive simulations and comparative analyses.

This article investigates an innovative state estimation technique implemented within an advanced distributed framework, aimed at reducing computational complexity while detecting multiple sensor faults in hydrogen-blended natural gas pipelines. The novel distributed estimation technique is based on the ensemble Kalman filter (EnKF) and is referred to as partial-distributed multisensor fault detection, isolation, and accommodation. The architecture includes a set of local EnKFs and an information fusion center. These local filters operate simultaneously to generate unique local state estimates based on a distinct set of sensor measurements, which are subsequently transmitted to the information fusion center for the computation of fault-free state estimates. To reduce computational complexity, the partially distributed approach segregates nonlinear computations from the local filters and delegates them to the main filter. Additionally, a fault diagnosis strategy is developed based on local state residuals. Since each local filter generates a distinct local state estimate based on its unique set of sensor measurements, comparing the local state residual against a threshold facilitates the identification and isolation of faulty sensors. Furthermore, an adaptive thresholding approach is incorporated to facilitate effective fault identification and isolation. The proposed technique has proven to be effective in highly nonlinear, and high-dimensional systems with simultaneous multiple sensor faults. The effectiveness of the proposed approach is demonstrated through extensive simulations and comparative analyses.

This paper investigates decision fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor network (WSNs), where the sparse Bayesian learning (SBL) algorithm is employed to estimate the channel between the sensors and the fusion center (FC). We present low-complexity fusion rules based on the hybrid combining architecture for the considered framework. Further, a deflection coefficient maximization-based optimization framework is developed to determine the transmit signaling matrix that can improve detection performance. The performance of the proposed fusion rule is presented through simulation results demonstrating the validation of the analytical findings.

This paper presents low-complexity decision rules as well as the pertinent analysis for data fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). The proposed framework considers both unknown and known parameter scenarios, and the spatial correlation arising due to close proximity of the sensors for both the centralized MIMO (C-MIMO) and distributed MIMO (D-MIMO) antenna configurations. The resulting detection performance is characterized by determining the closed-form expressions of probabilities of detection and false alarm for both antenna configurations. The optimal sensor gains are also determined for both the D-MIMO and C-MIMO architectures to further improve the detection performance. Additionally, asymptotic analysis is presented for both antenna configurations to determine the power scaling laws for the mmWave massive MIMO WSN, which lead to an improved sensor battery life without sacrificing the system performance. Furthermore, decision rules are also derived along with the pertinent analysis for a practical scenario with uncertainty in the channel state information (CSI) at the fusion center, wherein CSI of the mmWave massive MIMO channel is estimated using the novel sparse Bayesian learning (SBL) framework. Simulation results are presented to illustrate the performance of the proposed schemes and to validate the analytical results.

Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at the fusion center (FC) for known and unknown parameter detection scenarios, respectively. More explicitly, the GLRT detector also has to estimate the unknown parameter value. Closed-form expressions are derived for the probabilities of detection (PD) and false alarm (PFA) in order to characterize the performance of the proposed schemes. Furthermore, the optimal sensor transmit gains are determined for maximising the detection performance attained. An asymptotic performance analysis is carried out for determining the gain scaling laws for the massive MIMO WSN considered, when the number of antennas tends to infinity. The proposed framework is also extended to the realistic imperfect channel knowledge scenario at the FC, followed by the development of the associated fusion rules and analytical results to characterize the performance. Our simulation results closely tally the theoretical findings.

This paper investigates the uplink asymptotic performance of single-cell multiuser multiple-input multiple-output (MU-MIMO) system with a very large antenna array for time-selective fading channels, resulting from user mobility. To exploit the temporal correlation of the channel, the Kalman filter (KF) is developed for channel estimation, followed by its asymptotic performance analysis. A lower bound on the uplink achievable rate is obtained in the asymptotic limit of a large number of time slots and a finite number of base station (BS) antennas for linear receivers, such as the maximum ratio combiner (MRC) and zeroforcing (ZF) receiver. In addition the pertinent power scaling law is also derived for a large number of antennas. Finally, simulation results are presented to validate the analytical results.

This paper presents a Neyman-Pearson (NP) criterion based optimal distributed detection framework for a massive multiple-input multiple-output (MIMO) wireless sensor network (WSN). Robust fusion rules are determined for the local decisions transmitted by the sensor nodes, considering the availability of both perfect as well as imperfect channel state information (CSI) at the fusion center. Further, the probability of error of the individual sensor decisions, which arises in practical scenarios, is also incorporated in the decision framework. Closed form expressions are derived to characterize the resulting probabilities of detection and false alarm for the system. Simulation results are presented to demonstrate the improved performance of the proposed detectors in comparison to the existing detectors and to validate the theoretical findings.