A feedback-based sensor uncertainty detection scheme

This paper proposes a MIMO detection scheme reducing the computational complexity and providing comparable performance to the QRD-M detection scheme for MIMO-OFDM systems. The proposed scheme checks the channel condition per subcarrier and then selects detection scheme either ordered DFE scheme or adaptive QRD-M detection scheme according to that information. The post-detection SNR, which judges channel condition, depends on Moore-Penrose pseudo-inverse matrix G. The proposed scheme selects the ordered DFE detection scheme in case of good channel condition and the adaptive QRD-M detection scheme in case of bad channel condition. The adaptive QRD-M detection scheme selects the number of survival symbols M in accordance with the channel condition at the each layer. Compared with the QRD-M (M=16) detection scheme, the proposed detection scheme reduces the computational complexity about 67 percents and achieves similar performance.

Current studies on modulation and detection schemes in molecular communication mainly focus on the scenarios with static transmitters and receivers. However, mobile molecular communication is needed in many envisioned applications, such as target tracking and drug delivery. Until now, investigations about mobile molecular communication have been limited. In this paper, a static transmitter and a mobile bacterium-based receiver performing random walk are considered. In this mobile scenario, the channel impulse response changes due to the dynamic change of the distance between the transmitter and the receiver. Detection schemes based on fixed distance fail in signal detection in such a scenario. Furthermore, the intersymbol interference (ISI) effect becomes more complex due to the dynamic character of the signal which makes the estimation and mitigation of the ISI even more difficult. In this paper, an adaptive ISI mitigation method and two adaptive detection schemes are proposed for this mobile scenario. In the proposed scheme, adaptive ISI mitigation, estimation of dynamic distance, and the corresponding impulse response reconstruction are performed in each symbol interval. Based on the dynamic channel impulse response in each interval, two adaptive detection schemes, concentration-based adaptive threshold detection and peak-time-based adaptive detection, are proposed for signal detection. Simulations demonstrate that the ISI effect is significantly reduced and the adaptive detection schemes are reliable and robust for mobile molecular communication.

Adaptive Fault Detection and Fault-Tolerant Control of NASA Generic Transport Aircraft Model

This paper proposes a MIMO-OFDM scheme reducing the computational complexity and providing comparable performance to QRD-M detection scheme in IEEE 802.11n. The proposed scheme checks the channel condition per subcarrier and then selects either ordered DFE scheme or adaptive QRD-M detection scheme according to that information. The post-detection SNR which judges channel condition depends on Moore-Penrose pseudo-inverse matrix G. The proposed scheme selects the ordered DFE detection scheme in case of good channel condition and the adaptive QRD-M detection scheme in case of bad channel condition per subcarrier. The adaptive QRDM detection scheme selects the number of variable survival symbols M in accordance with the channel condition at each layer. Compared with the conventional QRD-M (M=16) detection scheme, the proposed detection scheme reduces the computational complexity about 67 percents and achieves similar performance.

Suspension operation reliability is one of the most significant performance indexes that concerns maneuvering stability and drive safety. In this paper, in order to guarantee good suspension reliability, an adaptive fault detection and isolation scheme is proposed for quarter-car active suspension systems with parametric and nonlinear uncertainties. To realize fault diagnosis for active suspensions, an adaptive fault detection estimator and several fault isolation estimators are designed to generate state residuals. Corresponding adaptive thresholds are developed to help judge the occurrence and type of possible faults. In the process of constructing state estimators, the uncertain parameter is updated online so that good sensitivity of fault detection can be achieved. Illustrative simulation is carried out to validate the effectiveness of the fault diagnosis scheme, and results indicate that the proposed method is sensible to sudden faults and maintain robustness to model uncertainties appearing in suspension systems.

In this paper, we shall show that an unlimited number of additive single faults can be isolated under mild conditions if a general isolation scheme is applied. Multiple faults are also covered. The approach is algebraic and is based on a set representation of faults, where all faults within a set can occur simultaneously, whereas faults belonging to different fault sets appear disjoint in time. The proposed fault detection and isolation (FDI) scheme consists of three steps. A fault detection (FD) step is followed by a fault set isolation (FSI) step. Here the fault set is isolated wherein the faults have occurred. The last step is fault isolation (FI) of the faults occurring in a specific fault set, i.e. equivalent with the standard FI step.

Fast and selective DC fault detection and location schemes are needed for fault protection of DC Grids. In this paper, the frequency domain characteristics of DC voltage besides the current limiting reactors are analyzed and used to put forward a new fault detection method for DC grids. Hilbert-Huang transform is applied on the DC voltage waves to extract the transient frequency and transient amplitude of the DC voltage. The influence of fault type, fault distance and fault resistance of DC faults and the noise on the frequency domain transient characteristics on voltage waves are then studied. Support vector machine learns the mapping between the transient frequency and transient amplitude of the DC voltage and desired outputs, fault type and fault location through training process. Then, a fast and accurate fault detection scheme based on frequency-domain characteristics of DC voltage is proposed by SVM for DC fault detection and location in MTDC systems. This fault detection scheme requires no telecommunication, has anti-interference ability and good sensitivity and selectivity for detection of long-distance fault and high resistance grounding fault. Based on the four-terminal HVDC system built by PSCAD/EMTDC, simulations under different fault conditions are carried out to verify the effectiveness of the proposed DC fault detection scheme.

This paper proposes an adaptive iteration detection scheme for a combination of STBC and SM scheme in LTE systems. The hybrid space-time block code system is a combination of STBC and SM scheme. It obtains both diversity gain and spectral efficiency gain. The conventional combination of STBC and SM scheme can't achieve good BER performance of STBC symbols due to interference from other symbols. The iterative V-BLAST detection scheme improves BER performance but has complex iterative detection process. The proposed scheme applies the adaptive iterative detection scheme by channel condition and can reduce complexity of LTE systems. LTE systems require high speed. Therefore, this scheme can be applied effectively in LTE systems.

Currently, most of the researches in molecular communication (MC) domain focus on the static MC scenarios. However, some envisioned important MC applications require mobile MC system. The investigation on mobile MC, especially the signal detection of mobile MC is limited. This work considers the problem of signal detection for mobile MC scenarios where the receiver nano-machine performs random movement. Due to the random movement of the receiver, the channel impulse response (CIR) changes over time which makes the received signal stochastic and complicated. This further complicates the signal detection in mobile MC and leads to that the state-of-the-art signal detection schemes for static MC scenarios fail for the mobile MC scenarios. To solve this issue, an adaptive detection scheme has been proposed by our group previously, based on dynamic estimation of the stochastically varying distance between the transmitter and receiver and the reconstruction of CIR in each interval. However, its computational complexity is high. Limited capability of current nano-machines desire low-complexity detection algorithm. In this work, we further propose an adaptive detection scheme for mobile MC with a low computational complexity by utilizing the local convex property of the CIR. With on-off keying (OOK) modulation, the signal of symbol "1" presents local convex property while that of symbol "0" presents local concave property. The convexity extent varies with the stochastic distance. A simple indicator, local maximum convexity is proposed which adapts to the stochastic distance. By comparing the adaptive indicator with an adaptive threshold within each symbol interval, the signal is detected without the need to estimate the stochastically changing distance or to reconstruct the CIR. Therefore, the computational load is effectively reduced. Numerical simulations are performed to evaluate the proposed scheme. The results show that the proposed scheme achieves good detection accuracy with low computational complexity and it could be a promising detection scheme for mobile MC scenarios.

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

This paper presents switch fault detection and compensation control schemes for a modified dc-dc converter that relocates a diode location. The proposed fault detection and compensation control schemes are applicable to various energy generation systems that include energy storage. A fault detection index has been introduced to detect an open switch fault in the modified converter structure, and a compensation control method based on a mixed switching strategy has been developed. The fault-tolerant control method along with the detection scheme can make a system return to work even under a switch fault. The proposed schemes have been tested and verified both by the simulations and by experiments using a TMS320F28335 DSP. The results will significantly improve the reliability of alternative energy generation systems against open switch faults.

A reconstruction based adaptive fault detection scheme for distribution system containing AC microgrid

This paper propose a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose power system failures are simulated by using PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co- on modified and standart IEEE-14 and standard IEEE-39 bus considering networks faults which are difficult to detect.

An approach for nonlinear unknown input observer (NUIO) design is proposed for a class of nonlinear systems representable by a Takagi—Sugeno (TS) fuzzy system. The proposed NUIO design for TS fuzzy systems is carried out for two cases: (1) the premise variables do not depend on the unmeasured state variables; and (2) the premise variables depend on the unmeasured state variables. Sufficient conditions for the existence of NUIOs are derived, and a linear matrix inequality (LMI)-based design strategy is presented for NUIO design purposes. The proposed NUIO design approach is then applied to solve actuator fault detection and isolation problems for nonlinear systems described by TS fuzzy systems. To this end, a system structure with two groups of inputs where one group of inputs is treated as unknown inputs is developed. Based on the system structure, a bank of NUIOs are then designed using the developed NUIO design approach in order to investigate the following fault diagnosis problems. (1) How can the NUIOs be used for detecting faults? (2) Under what conditions is it possible to isolate single and/or multiple faults? (3) What is the maximum number of faults that can be isolated simultaneously? (4) How can multiple fault isolation be achieved? In this article we present a NUIO-based fault-detection scheme for problem (1), give sufficient conditions for problem (2), determine the maximum number of faults that can be isolated for problem (3), and propose a fault-diagnosis scheme using a bank of NUIOs to solve problem (4). As an illustrative example, Lorenz’s chaotic system with multi-inputs is chosen to show the effect of the designed NUIOs and the proposed fault detection and isolation scheme. Simulation results show that accurate state estimation is achieved and actuator faults can be detected and isolated successfully.

In this paper fault detection and classification scheme using K nearest neighbor (KNN) has been presented for AC/DC transmission line with DFIG (doubly fed induction generator). The power system model consist of 1000 MW synchronous generator, 510 MW wind far, three phase transmission line of 500 kV and 30km length and HVDC line of 1000 MW. The model has been implemented with the corresponding parameters in MATLAB and simulated for different scenario. The raw current and voltage acquired for the sensors have been processed by performing discrete wavelet transform to the derive the discriminatory attributes with maximum disparity between the healthy and faulty cases. Following feature extraction, the required tasks of fault detection and classification scheme in A C and HVDC lines have been achieved using KNN. Further, the efficacy of the proposed KNN based scheme has been validated for varying fault scenarios pertaining to wide variation in fault resistance, inception angle of fault and fault location. For all tested cases, the proposed scheme is able to achieve a fault detection and classification accuracy of 1 00%.