Adaptive Fault Detection and Isolation for Active Suspension Systems With Model Uncertainties

This paper presents an adaptive-based fault detection and isolation scheme for a general class of robot manipulators, with characterizing the isolability conditions. The proposed algorithm consists of a nonlinear adaptive fault detection estimator and a bank of fault isolation estimators to determine the types of faults, which may be incipient or abrupt, while the fault parameter function may be time-varying. To demonstrate its effectiveness, the method is applied to a two-link robot manipulator and the simulation results are presented and discussed.

A fault detection and isolation scheme is addressed for a class of linear distributed parameter systems (DPS) described by partial differential equations (PDE). In contrast to using ordinary differential equations (ODE) for describing DPS, in this paper, a filter based observer based on the linear PDE representation is proposed with an output measurement. A fault is declared active when the magnitude of the detection residual exceeds a predefined threshold. Upon detection, an actuator and a sensor fault isolation estimators are activated to identify the fault type when their isolation residual is below a predefined threshold and the other is above the threshold. When both actuator and sensor fault isolation estimator residuals are above their isolation thresholds, a state fault is considered to have occurred. Upon isolation, the magnitude of the fault parameter is identified. Finally, the performance of the fault detection and isolation scheme is demonstrated on a heat reactor system which is represented by linear parabolic PDEs.

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

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

Distributed sensor fault diagnosis for a formation of multi-vehicle systems

Active suspension systems are of great significance to high speed trains (HST), which are aimed to provide satisfactory ride comfort as well as to guarantee excellent operation stability, safety and reliability. As a commonly encountered kind of faults in HST, intermittent faults (IFs) should be diagnosed accurately so as to maintain the performance of active suspension systems. In this paper, a vertical dynamical model of the active suspension system is built up, where it takes the unknown track irregularity disturbance, harsh operation noise and measurement noise into consideration. Based on this model, a fault detection and isolation scheme is proposed to detect all the appearing and the disappearing time of IFs in the front (rear) bogie and isolate them. A set of novel multidimensional residuals are generated by using a sliding-time window technique, based on a geometric approach. Then two hypothesis tests are presented to determine the appearing time and the disappearing time, respectively. Finally, extensive simulation results on an active suspension system are provided to illustrate the validity of the proposed scheme.

Effective fault detection and isolation can improve the safety, reliability and efficiency of the district heating system. In order to detect and locate the sensor, actuator and component faults in the district heating system with faster response speed and higher accuracy, a two-level fault detection and isolation scheme, consisting of upper-level classifier for system faults and lower-level classifier for sub-faults, is developed based on convolutional neural networks. In consideration of the difficulty of obtaining the operation data of a real district heating system under various faulty states, a test benchmark model of an integrated energy based district heating system is built from the system level, which contains the renewable energy based water boilers, transmission networks, and heat load substations to examine the effectiveness of the proposed scheme. To improve the model reality, the dynamics of sensors and actuators, models of heat exchangers, and thermal inertia are considered in the district heating system, and Gaussian noises are added in the raw data signals. Nine kinds of system faults including sensor, actuator, component faults, along with three sub-fault types of bias, drift, complete failure, are investigated in the benchmark system. The performance of the proposed two-level fault detection and isolation scheme is evaluated under different data windows and Gaussian noises in the district heating system, and is compared with other data-driven methods including k-nearest neighbor, random forest and back propagation neural networks. Experimental results show that the two-level fault detection and isolation scheme can detect the faults in the district heating system accurately and robustly, and the proposed scheme has the potential to become an effective solution to real-time monitoring of faults in the district heating system.

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) of a satellite. The goal is to determine the occurrence of a fault in any one of the multiple thrusters that are employed in the attitude control subsystem of a satellite, and further to localize which PPT is faulty. In order to accomplish these objectives, a multilayer perceptron network embedded with dynamic neurons is proposed. Based on a given set of input-output data collected from the electrical circuit of the PPTs, the dynamic network parameters are adjusted to minimize the output estimation error. A Confusion Matrix approach is used to measure the effectiveness of our proposed dynamic neural network-based fault detection and isolation (FDI) scheme under various fault scenarios.

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, 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.

Suspension system is used to fulfil the criteria of ride comfort and road handling. In this paper, a quarter car active & semi-active suspension systems are designed using Matlab/Script software. Comparison of active & semi-active suspension systems are done using robust control theory for the control targets suspension deflection, body acceleration and body travel. H infinity controller is selected to compare the two suspensions using time domain analysis. Finally the simulation result prove the effectiveness of the active suspension system by decreasing the body acceleration & sustaining the suspension deflection and body travel outputs.

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.

In this paper we present and compare different fault diagnosis algorithms using space space models for nonlinear dynamic systems. Most fault diagnosis and isolation algorithms for dynamic systems, which can be modeled using a set of state space equations, have relied on the system being linear and the noise and disturbances being Gaussian. In such cases, optimal filtering ideas based on Kalman filtering are utilized in estimation followed by a residual analysis, for which whiteness tests are typically carried out. Linearized approximations (e.g., Extended Kalman filters) have been used in the nonlinear dynamic systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. The particle filter is combined with the innovation-based fault detection techniques to develop a fault detection and isolation scheme. The advantage of particle filters is that they are capable of handling any functional nonlinearity and system or measurement noise of any distribution. An improvement on using a single Extended Kalman filter matched to a particular model is to use the the Interacting Multiple Model estimator, which consists of a number of EKFs running in parallel. Such a multiple model estimator can handle the abrupt changes in the system dynamics, which is essential for fault diagnosis. Here, we compare the fault detection performances of these algorithms on different nonlinear systems.