Fault-tolerant Filter Research Articles

Fault-Tolerant Cubature Kalman Filter for Engineering Estimation Control Systems.

The cubature Kalman filter (CKF) overcomes the limitations of the Kalman filter in strong nonlinear systems, which has been widely used in many fields. However, in practical engineering, the abnormal measurement information obtained by the sensor causes the measurement noise covariance to change, which may deteriorate the filtering performance and even cause the filter failure. The fault-tolerant filter can deal with the state estimation problem for the systems with abnormal measurements. The key of the fault-tolerant filter is to forcefully correct filter innovation by using a fading factor. The fault-tolerant filter technology has been extensively applied in many practical systems, but it is still lack of reasonable theoretical analysis. To this end, the measurement noise model is established and the magnitude of the noise deviation is analyzed. The filtering performance under abnormal measurement is analyzed by three mean squared errors (MSEs), which are the ideal MSE, the filter calculated MSE and the true MSE. In order to solve the influence of sampling approximation deviation of CKF on fault detection, an improved fault detection algorithm is proposed. The performance of fault-tolerant CKF is analyzed from two views. The first view is about comparing the filter calculated MSEs of CKF and of fault-tolerant CKF, the second view is about comparing the relative closeness of the filter calculated MSE to the true MSE for the two algorithms. Numerical examples further verify these conclusions.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given. Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years. The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response (FIR) components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules. Finally, a simple solution of Double modular redundancy (DMR) based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising. This approach is expanded in this short to display how parallel filters can be protected using error correction codes (ECCs) in which each filter is comparable to a bit in a standard ECC. “Advanced error recovery for parallel systems,” the suggested technique, can find and eliminate hidden defects in FIR modules, and also restore the system from multiple failures impacting two FIR modules. From the implementation, Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation. Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

Adaptive Hybrid Robust Filter for Multi-Sensor Relative Navigation System

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.

Fault-tolerant H∞ filtering for fuzzy networked control systems with quantisation effects

ABSTRACTThe issue of memory filter design is investigated for a class of discrete-time Takagi–Sugeno (T-S) fuzzy networked control systems with time-varying delay and quantisation effects. The main objective of this paper is to formulate an memory filter that can guarantee the resulting filtering error system to be finite-time bounded. Further, by considering the communication limitations, the state signals are quantised before transmission through a logarithmic quantiser and the quantisation error is considered in the filter design. In particular, the quantisation effects are dealt by employing the generalised sector bound approach. Based on Lyapunov stability theory, novel sufficient constraints are established in the form of linear matrix inequalities, to ensure the finite-time boundedness of the augmented filtering error system. Then, a new fault-tolerant memory filter is formulated and also the filter gain parameters are attained by solving the derived LMI based constraints. Finally, a numerical example is furnished to support the efficacy and applicability of the developed filter design.

Відмовостійкий алгоритм ідентифікації бортової математичної моделі авіаційного двигуна ТВ3- 117 в складі його системи автоматичного управління

The subject matter of the article is TV3-117 aircraft engine and methods for monitoring and diagnosing its technical condition. The goal of the work is development of fault-tolerant algorithms for identification of the onboard mathematical model of the TV3-117 aircraft engine as part of its automatic control system in flight modes. The following tasks were solved in the article: recovering of lost information by an auto-associative neural network in case of a single sensor failure, recovering of lost information by an «optimal» auto-associative neural network in case of single sensor failures of the on-board control and diagnostic system, recovering of lost information by an auto-associative neural network and an on-board control and diagnostic system from the gas temperature registration sensor in front of the turbine compressor in case of failure. The following methods used are – substantiation of expediency of Kalman-filtration use in TV3-117 aircraft engine automatic control system, determination of Kalman filter transfer function, determination of algorithm of detection and localization of channel failure of two-channel sensor, determination of frequency characteristics of TV3-117 aircraft engine automatic control system, proof of equality of unit coefficient fault-tolerant filter unit . Conclusions: The frequency properties of the automatic control system of the aircraft engine TV3-117 are investigated, the equality of the unit of the gain of the developed fault-tolerant filtering unit and the absence of additional phase shifts introduced by the possible net delay due to the peculiarities of the implemented algorithms mathematical model. The absence of their influence on the stability of aircraft engine TV3-117 automatic control system is proved. Approbation of the developed algorithms showed that the average relative error in dynamics does not exceed 0.15 %, and in statics at the maximum expense – decreases to 0.01 % that corresponds to modern requirements of accuracy of algorithms of identification on a contour of dosing needle . Keywords: aircraft engine, neural network, Kalman filtering, failure detection and localization

A fault-tolerant attitude estimation method for quadrotors based on analytical redundancy

Inertial sensors and velocity measurement sensors are commonly used for attitude estimation of quadrotors. In the previous fault-tolerant attitude estimation methods, the two types of sensors' faults are treated separately. When one sensor is detected, the other sensor is assumed to be always healthy. In this paper, the situation is improved by introducing the rolling & pitching moment model of quadrotors. The rolling & pitching moment model, x-axis gyro, y-axis gyro and velocity measurement sensors are fused, forming an analytical redundancy based fault-tolerant filter. The real flight experiment data are used for validation, showing that the faults of x-axis gyro, y-axis gyro and velocity measurement sensors can all be detected and isolated, and the state estimation accuracy is improved compared with the traditional method when gyros are faulty.

New fault-tolerant federated Kalman filters for integrated navigation

PurposeThe purpose of this paper is to achieve accurate integrated navigation results for the unmanned aerial vehicle (UAV) systems even in the presence of possible navigation faults in the subsystems of the federated Kalman filter.Design/methodology/approachThe federated Kalman filter is modified from two aspects to get accurate navigation results under abnormity. First, time-variant vector distribution coefficients trading off the navigation accuracy and the observability degree of each state component are computed to replace the traditional scalar coefficients. Second, a fault-tolerant filter is proposed as the local navigation filter.FindingsSimulations for the navigation of a UAV system show that the proposed method can be applied for accurate navigation purpose even in the presence of subsystem navigation faults.Originality/valueNew fault-tolerant federated Kalman filters for integrated navigation are presented to achieve accurate navigation solutions.

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs subjected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability.

Error Correction for Parallel FIR Filters Using Hamming Codes

In this paper ,we propose a error correction for parallel FIR filters using Hamming code in which single parallel FIR filter is taken as a bit in ECC technique. In many complex circuits, reliability plays a crucial role and it requires fault tolerant filter implementations. Now a days, technology grows up, the complex system use many filters which operates simultaneously. Consider an example in which same parallel filter is applied to different inputs. To achieve fault tolerance, an ECC technique uses the presence of parallel filters are considered. The ECC technique provides protection where more number of parallel filters are used by using the case study, the effectiveness in error correction and circuit design cost is evaluated.

Fault Tolerant Parallel Filters Based on Error Correction Codes

Digital filters are widely used in signal processing and communication systems. In some cases, the reliability of those systems is critical, and fault tolerant filter implementations are needed. Over the years, many techniques that exploit the filters' structure and properties to achieve fault tolerance have been proposed. As technology scales, it enables more complex systems that incorporate many filters. In those complex systems, it is common that some of the filters operate in parallel, for example, by applying the same filter to different input signals. Recently, a simple technique that exploits the presence of parallel filters to achieve fault tolerance has been presented. In this brief, that idea is generalized to show that parallel filters can be protected using error correction codes (ECCs) in which each filter is the equivalent of a bit in a traditional ECC. This new scheme allows more efficient protection when the number of parallel filters is large. The technique is evaluated using a case study of parallel finite impulse response filters showing the effectiveness in terms of protection and implementation cost.

Fault-tolerant filter design with quantized measurements

In this paper, a novel approach is proposed for designing fault-tolerant filters for linear discrete-time systems whose measurements undergo quantization. Two types of faults are considered: internal faults which are modelled by uncertainties in state space parameters; and actuator faults such as offset and stuck which are modelled as an unknown disturbance on the states. The proposed filter consists of two robust filters which operate in two different zones. These zones are defined based on the effects of the quantization on the residual signal between the quantized measurement and the one generated by the filter. Using an LMI-based approach with prescribed H∞ performance, stability LMIs are derived for the proposed dual-zone filter and finally the results are validated through simulation examples.

A Reliable Multi-Sensor Navigation System for an Unmanned Aerial Vehicle Realized through Integration of SINS with GPS, CNS and Doppler Radar

Contemporary importance of the unmanned aerial vehicle (UAV) both for military and civilian applications has prompted vigorous research related with guidance, navigation and control of these vehicles. The potential civilian uses for small low-cost UAVs are various like reconnaissance, surveillance, rescue and search, remote sensing, traffic monitoring, destruction appraisal of natural disasters, etc. One of the most crucial parts of UAVs missions is accurate navigation of the vehicle, i.e. the real time determination of its position, velocity and attitude. Generally highly accurate Strap down Inertial Navigation Systems (SINS) are too heavy to be flown on UAVs. Moreover highly accurate SINS are also highly expensive. Therefore the low-cost and low weight MEMS based SINS with a compromised precision are the viable option for navigation of UAVs. The errors in position, velocity, and attitude solutions provided by the MEMS based SINS grow unboundedly with the passage of time. To contain these growing errors, integrated navigation is the resolution. Complementary characteristics SINS and external non-inertial navigation aids like Global Positioning System (GPS), Celestial Navigation System (CNS) and Doppler radar make the integrated navigation system an appealing and cost effective solution. The non-inertial sensors providing navigation fixes must have low weight and volume to be suitable for UAV application. In this research work GPS, CNS and Doppler radar are used as external navigation aids for SINS. The navigation solutions of all contributing systems are fused using Federated Kalman Filter (FKF). Three local filters are employed for SINS/GPS, SINS/CNS and SINS/Doppler integration and subsequently information from all three local filters is fused to acquire a global solution. Moreover adaptive and fault tolerant filtering scheme has also been implemented in each local filter to isolate or accommodate any undesirable error or noise. Simulation for the presented architecture has validated the effectiveness of the scheme, by showing a substantial precision improvement in the solutions of position, velocity and attitude.

Efficient Arithmetic-Residue-Based SEU-Tolerant FIR Filter Design

Finite impulse response (FIR) filters are commonly used in many signal processing systems. In some cases, such as space systems, FIR filters are exposed to radiation sources that cause errors. A number of techniques can be used to protect against those errors. One of them is the use of an arithmetic residue replica of the FIR filter. This replica has a lower cost than the original filter and therefore reduces the overhead compared with replicating the filter. However, some errors may not be detected by the arithmetic residue replica. In this brief, schemes that avoid undetected errors are presented. The proposed techniques are implemented and evaluated showing that they can detect all single errors affecting register bits. The results also show that their implementation requires only a small overhead in circuit complexity for the existing residue-based fault-tolerant FIR filters.

Study on Fault-Tolerant Method of Mobile Robot Integrated Navigation

As for the SLAM/DR/MEMS integrated navigation system of mobile robot constructed on the self-made SaecROB-H mobile robot, there exist mutant faults which are likely to occur, such as environment feature recognition error and data association error during mobile robot SLAM procedure, or dead reckoning failure caused by wheel slipping and so on. According to these mutant faults, mutant fault detecting and fault-tolerant filtering method was presented in this paper based on finite memory on-line prediction. At last, simulation was carried out and the results show that the method can improve the stability of mobile robot integrated navigation efficiently.

Fault-tolerant image filter design using particle swarm optimization

This article describes a mixed constrained image filter design with fault tolerance using particle swarm optimization (PSO) on a reconfigurable processing array. There may be some faulty configurable logic blocks (CLBs) in a reconfigurable processing array. The proposed method with PSO autonomously synthesizes a filter fitted to the reconfigurable device with some faults in order to optimize the complexity and power of the circuit, and the signal delay in both the CLBs and the wires. An image filter for noise reduction is experimentally synthesized to verify the validity of our method. By evolution, the quality of the optimized image filter on a reconfigurable device with a few faults is almost same as that on a device with no faults.

Channel Fault Tolerant Target Tracking in Multi-hop Wireless Sensor Networks Based on Particle Filtering

In this paper, a novel channel fault tolerant particle filtering is proposed for target tracking in a fading multi-hop wireless sensor network. The quantized binary data from local sensors need to be relayed through multi-hop transmission in order to reach a fusion center. Each relay node employs a binary relay scheme where the relay output is inferred from the channel impaired observation received from its source node. The observation likelihood function of each particle is deduced with the consideration of physical channel. The fading channels between sensors and the fusion center are incorporated in the tracking algorithm under particle filtering framework. Assuming that the fusion center has knowledge of the fading channel gains and has only the fading channel statistics, we propose respectively the channel fault tolerant target tracking algorithms. The improved tracking accuracy and robustness to channel imperfection of the proposed channel fault tolerant filtering are shown through extensive computer simulations.

Weiss–Weinstein Lower Bounds for Markovian Systems. Part 2: Applications to Fault-Tolerant Filtering

Characterized by sudden structural changes, fault-prone systems are modeled using the framework of systems with switching parameters or hybrid systems. Since a closed-form mean-square optimal filtering algorithm for this class of systems does not exist, it is of particular interest to derive a lower bound on the state estimation error covariance. The well known Crameacuter-Rao bound is not applicable to fault-prone systems because of the discrete distribution of the fault indicators, which violates the regularity conditions associated with this bound. On the other hand, the Weiss-Weinstein lower bound is essentially free from regularity conditions. Moreover, a sequential version of the Weiss-Weinstein bound, suitable for Markovian dynamic systems, is presented by the authors in a companion paper. In the present paper, this sequential version is applied to several classes of fault-prone dynamic systems. The resulting bounds can be used to examine fault detectability and identifiability in these systems. Moreover, it is shown that several recently reported lower bounds for fault-prone systems are special cases of, or closely related to, the sequential version of the Weiss-Weinstein lower bound

Fault-tolerant compression filters by time-propagated measurement fusion

For fault-tolerant real-time filtering, an efficient two-filter architecture is proposed. In the two-filter architecture, a host filter is operated permanently. A compression filter (CF) is intermittently initialized and operated during prescribed time intervals to handle measurements susceptible to soft faults. New CFs are derived based on time-propagated measurement concept. The derived de-correlated full error-state CF and correlated partial error-state CF facilitate a number of different ways of ordering out-of-normal-sequence measurements, provide efficient state estimates with consistent error covariance information, and make fault detection and isolation convenient.