Analytical Redundancy Method Research Articles

A novel analytical redundancy method based on decision-level fusion for aero-engine sensors

A novel analytical redundancy method based on decision-level fusion for aero-engine sensors

Analytical Redundancy for Variable Cycle Engine Based on Variable-Weights-Biases Neural Network

Due to the complex nature of a variable cycle engine (VCE), which has numerous control variables and working modes across a broad flight envelope, coupled with the whole engine’s degradation, the analytical redundancy method based on component-level models may not provide an accurate estimation of the sensors. Variable-weights-biases neural network (VWB Net) is proposed to construct VCE’s analytical redundancy. Unlike conventional networks whose weights and biases are fixed, VWB Net’s variable-weights and variable-biases are functions of input which greatly increase its nonlinear mapping capability by integrating input information. Variable-biases can also be used to eliminate the error between actual sensor output and estimated value quickly at the terminal node. Compared with the BP network and Dense net, VWB Net has fewer parameters, faster calculation speed, and higher accuracy. Digital simulation results of VCE parameter estimation demonstrate that VWB Net’s average relative errors are under 0.27% with calculation and parameter efficiency at least 166 times higher than that of Dense net. Hardware in the loop simulation further verifies VWB Net’s estimation accuracy and real-time calculation.

Research on the Analytical Redundancy Method for the Control System of Variable Cycle Engine

The safety and reliability of the measuring elements of an aero-engine are important preconditions of the stable operation of the engine control system. The number of control parameters of a variable cycle engine increases by 20%–40% compared to traditional engines. Therefore, it is important to conduct study on the analytical redundancy, design fault diagnosis and isolation of the sensors, as well as the signal reconstruction system, so as to increase the ratability and fault-tolerant capability of the variable cycle engine control system. The analytical redundancy method relies on the accuracy of the mathematical model of the engine. During the service cycle of the engine, it is inevitable that the engine performance will deteriorate, resulting in a mismatch with the model. In this paper, the adaptive model of the variable cycle engine is built with a Kalman filter. Based on this, the strategy of analytical redundancy logic is built and the dynamic adaptive calculation of the threshold is introduced. Simulation results reflect that this method can effectively increase the reliability of sensor fault diagnosis and the accuracy of the analytical redundancy when there is performance degradation of the variable cycle engine.

Fault Diagnosis and Fault-Tolerant Compensation Strategy for Wheel Angle Sensor of Steer-by-Wire Vehicle via Extended Kalman Filter

In order to improve the fault-tolerant performance of wheel angle sensor of steer-by-wire (SBW) vehicle, a fault diagnosis and fault-tolerant compensation (FDFTC) strategy for wheel angle sensor of SBW vehicle via extended Kalman filter (EKF) is proposed in this paper. Firstly, the steering motor model, rack and pinion model, vehicle dynamics model and variable transmission ratio model are established. Then, based on the analytical redundancy method, the EKF algorithm is adopted to obtain the wheel angle estimation signal. On this basis, a sensor FDFTC strategy with improved majority voting scheme is proposed, which is composed of fault diagnosis module and fault isolation compensation module. Finally, combined with Matlab/Simulink and Carsim, the estimation results are analyzed under sinusoidal condition and double lane change condition, and the performance of the proposed FDFTC strategy is further verified under the sensor stuck fault condition, deviation fault condition and noise interference condition. The results indicate that the proposed FDFTC strategy can not only accurately diagnose the fault signal, but also reconstruct the correct wheel angle signal, which is of great significance to improve the safety and reliability of SBW vehicle.

Experimental Evaluation of Data Fusion Algorithm for Residual Generation in Detecting UAV Servo Actuator Fault

Unmanned Aerial Vehicles used by military are generally designed with very high reliability and have multiple redundancy of software and hardware equipment because they are intended to operate in hostile environment. But, relatively low cost UAVs used commercially are not equipped with such systems. Usually, micro UAVs weight less than 2kg are equipped with on-board miniature sensor and operate without any hardware redundancy and thus could reduce their reliability. Some of these commercial UAVs that operate in populated areas will cause damage and fatality if faulty system occurred. Hence there is a need for on-board fault detection and isolation system without degradating the UAV flyability and its cost. Analytical redundancy or model reference method of fault detection algorithms could be implemented as most UAVs are microcontroller controlled. Together, with the availability of miniature sensors could provide an ideal platform for implementing fault detection. In this paper, the development of fault detection through residual generation algorithm is implemented with data fusion from miniature sensors. Some of these sensors are already installed within the autopilot system which reduce the amount of additional sensors needed. Identification of fault in the elevator is simulated experimentally and fault detection rate is monitored. From the implemented algorithm, the data fusion from additional sensors shows improvement in fault detection rate.

EMB 시스템의 모델 기반 센서 고장 검출 알고리즘 개발

The brake-by-wire technology is a new automotive chassis system that allows standard braking operations by electronic components with lighter weights and faster response. The brake-by-wire units such as EMB (Electro-Mechanical Brake) are controlled by electronic sensors and actuators and, thus, the fault diagnosis is essential for implementation. In this study, a model-based fault diagnosis system is developed for the sensors based on the analytical redundancy method. The fault detection algorithm is verified in simulations for various faulty cases. A test bench is built including the EMB unit and the performance of the proposed fault diagnosis system is evaluated through the experiment.

Electro-Mechanical Brake의 클램핑력 제어를 위한 전류 및 힘 센서 고장 검출 알고리즘 개발

EMB (Electro-Mechanical Brake) systems can provide improved braking and stability functions such as ABS, EBD, TCS, ESC, BA, ACC, etc. For the implementation of the EMB systems, reliable and robust fault detection algorithm is required. In this study, a model-based fault detection algorithm is designed based on the analytical redundancy method in order to monitor current and force sensor faults in EMB systems. A state-space model for the EMB is derived including faulty signals. The fault diagnosis algorithm is constructed using the analytical redundancy method. Observer is designed for the EMB and the fault detectability condition is examined based on the residual analysis. The performance of the proposed model-based fault detection algorithm is verified in simulations. The effectiveness of the proposed algorithm is demonstrated in various faulty cases.

A Wavelet Band-Limiting Filter Approach for Fault Detection in Dynamic Systems

This paper presents a wavelet-based analytical redundancy method for the detection of faults in dynamic systems. In the proposed approach, consistency checks are carried out after band-limiting the signals under consideration to specific frequency ranges. For this purpose, the discrete wavelet transform is used to establish the frequency bands of analysis and a finite impulse response filter is employed to check the dynamic consistency of the data within each band. The filter weights can be adjusted by a simple parametric identification procedure on the basis of data acquired under normal operating conditions. The proposed method is illustrated by using experimental fault data from an analog computer, which was adjusted to emulate the dynamic response of a servomechanism, as well as simulated data representing a sensor fault scenario in the operation of a Boeing 747 aircraft. For comparison, a standard Luenberger observer fault detection scheme is also employed. The results show that the wavelet method compares favorably with the observer-based scheme in terms of sensitivity to the fault effect, false alarms, and nondetected faults.

Induction Machine Condition Monitoring Using Neural Network Modeling

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced

Simple virtual attitude sensors for general aviation aircraft

PurposeThis paper aims to present a proposal of a simple analytical redundancy method using for virtual attitude reference system design. The main idea of the project is applied typical on‐board general aviation aircraft equipment for solving pitch and bank angles.Design/methodology/approachThe presented solution is based on satellite navigation and air data computer signals, which are independent from basic attitude and heading reference system. The concept described is related to the kinematics relations and aerodynamic forces formulas.FindingsThe paper shows that the virtual attitude sensors are used by failure detection and reconfiguration subsystem and as a standby attitude reference system for general aviation aircraft equipped with augmentation control system applied for improving safety and efficiency handling qualities of aircraft.Originality/valueThis solution allows improvements in operating properties of already existing and newly designed executive aircraft, as well as establishing a higher level of flight safety.