Model-based Fault Detection Algorithm Research Articles

Active fault-tolerant control for the dual-valve hydraulic system with unknown dead-zone

This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified.

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle.

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

Simultaneous Fault Detection and Control Design for Linear Fractional-Order Systems

The focus of this paper is the analysis and design scheme of simultaneous fault detection and control (SFDC) for linear continuous-time fractional-order systems assumed to be affected by sensor, actuator and process faults as well as disturbances. In essence, this simultaneous design unifies both the control and the detection modules into a single unit that is called the controller/detector unit. This unit is designed such that it generates two signals, namely the residual and the control signals. The system can be stabilized using the control signal, and the residual signals can detect the fault based on model-based fault detection and isolation algorithms. The SFDC module should be designed so that the effects of faults and disturbances on the residual signals are maximized and minimized, respectively. To this end, the SFDC problem is formulated as the mixed $$H_{ - } /H_{\infty }$$ optimization problem. Stability and fault detection are both considered through certain performance indices, and new sufficient conditions in the form of linear matrix inequalities are obtained. Finally, some simulation examples are given to illustrate the effectiveness of the proposed design method.

Fault Detection Based on a Bio-Inspired Vibration Sensor System

A novel vibration sensor based on a bio-inspired nonlinear structure with quasi-zero stiffness characteristic is developed for the real-time measurement of absolute vibration motion. With this bio-inspired vibration sensor, the problems of error accumulation and real-time performance induced by traditional measurement method using accelerometer can be effectively eliminated. In order to construct a comparatively exact model of the bio-inspired vibration sensor, an adaptive compensation method is applied to the estimation of the structure parameter. Through taking full advantage of the bio-inspired vibration sensor in real-time measurement of absolute vibration motion, a model-based fault detection algorithm is proposed to cope with the real-time detection problem of weak fault with fast time-varying characteristic which cannot be exactly identified by existing frequency-based and wavelet-based fault detection methods. Theoretical analysis and experimental results demonstrate that the fault detection algorithm based on this bio-inspired vibration sensor is effective and efficient, compared with the existing ones and thus has a great potential in many real practical applications.

A data driven approach for detection and isolation of anomalies in a group of UAVs

The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV’s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller

This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.

Parallel model based fault detection algorithm for electronic parking brake system

This paper describes a parallel model-based fault detection algorithm for an electronic parking brake (EPB) system, which consists of an electronic control unit with built-in current sensor and braking force sensor. For the EPB system to supply sufficient parking force to a vehicle, the parking force sensor is of utmost importance. If a fault occurs in this sensor, sufficient parking force may not be supplied, thereby seriously threatening the safety of the vehicle. Thus, a fault detection method is required for the parking force sensor of the EPB system to improve the safety of vehicles. For this purpose, a highly reliable fault detection method is needed to detect abnormal fault signals, which cannot be detected by the existing on-line sensor monitoring fault detection methods. This paper proposes a novel parallel model-based fault detection algorithm for the EPB system, which compares the physical sensor data with the mathematical model, the fuzzy model, and the neural network model at the same time. In order to reduce false alarms, the magnitude of thresholds and the operation counts are changed adaptively. When the proposed parallel model-based fault detection algorithm detects severe failures of the force sensor, it warns the driver in advance to prevent accidents due to the failures. The proposed algorithm is verified by hardware-in-theloop simulations in various situations.

Why Risk-Based Multivariate Fault Detection and Diagnosis?

A novel risk-based fault detection method has been developed. The proposed method provides a dynamic process risk indication based on the probability of happening a fault and its consequence. In this method instead of generating an alarm based on residuals or signals an alarm is activated only when the calculated risk of operation exceeds the acceptable threshold. This is an important concept as it can funnel the attention and effort of operators to the faults which poses the most operational or safety risk. Application of this new risk-based approach provides early warning of the fault as well as the associated risk with the fault. Methodologies were developed to apply the concept with model based fault detection algorithm as well as multivariate history based fault detection techniques. In this paper we show the model based approach by combining Kalman filter with the risk based approach. The history-based approach was demonstrated using principal component analysis (PCA). This method has more power in discerning between operational changes and abnormal conditions which have potential to cause accidents.

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.

Model-based fault-tolerant control for an automotive air suspension control system

This paper presents a new fault-tolerant control (FTC) algorithm for an automotive air suspension control (ASC) system. The FTC algorithm proposed in this paper provides the fault detection, diagnosis, and management of a closed-loop air suspension control system. A new model-based fault detection and isolation algorithm for the height sensors, which are critical but vulnerable components of the ASC system, is also proposed. The height sensor fault is detected on the basis of the geometric relationships of the suspension and is isolated by implementing the analytical redundancy of the height sensors using roll and pitch angle estimates derived by a Kalman filter. An adaptive threshold is designed and applied in order to enhance the robustness of the fault detection and isolation against model uncertainties. The effectiveness of the proposed model-based FTC algorithm is verified via simulation and actual vehicle tests.

Model Based Fault Detection for Advanced ESC System

This paper describes a model based fault detection algorithm for an Advanced ESC System which consists of Hydraulic Control Unit (HCU) with built-in wheel pressure sensors. Advanced ESC System can be used for various value-added functions such as Stop & Go Function and Regenerative Brake Function. Therefore, HCU must have a reliable fault detection. Due to the huge amount of sensor signals, existing specific sensor based fault detection of HCU cannot guarantee the safety of vehicle. However, proposed algorithm dose not require the sensors. When model based fault detection algorithm detects severe failures of the HCU, it warns the driver in advance to prevent accidents due to the failures. For this purpose, a mathematical model is developed and validated in comparison to actual data. Simulation results and data acquired from an actual system are compared with each other to obtain the information needed for the fault detection process.

Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.

Passive Robust Fault Detection of Dynamic Processes Using Interval Models

Model-based fault detection relies on the use of a model to check the consistency between the predicted and the measured (or observed) behavior of a system. However, there is always some mismatch between the modeled and the real process behavior. Then, any model-based fault detection algorithm should be robust against modeling errors. One possible approach to take into account modeling uncertainty is to include all the uncertainty in system parameters using an interval model that allows generating an adaptive threshold. In this paper, the use of interval models in robust fault detection considering three schemes (simulation, prediction, or observation) is presented and discussed. The main contribution is to present a comparative study that allows identifying the benefits and drawbacks of using each scheme. This study will provide a guideline for the use of the proposed fault detection schemes in real applications. Finally, an intelligent servoactuator, proposed as a benchmark in the context of European Research Training Network DAMADICS, is used to illustrate the application and the comparative study of these interval-based fault detection schemes.

Active Robust Fault Estimation on a Composite Beam with Integrated Piezoceramics

The problem addressed here is the application of model-based fault detection algorithm to active control of flexible structures. The modeling of mechanical structures with the finite element (FE) method leads to high order models which must be reduced. This step introduces important uncertainties which make necessary the use of robust fault detection and diagnosis (RFDD) algorithms. Here, we propose to apply a RFDD method using the H2 and H∞ polynomial estimation to estimate the input fault signal. To illustrate the efficiency of this method, it is applied to a composite beam with piezoelectric patches as sensors and actuators.

SUPERVISORY FAULT-TOLERANT CONTROL WITH APPLICATION TO THE IFATIS TWO-TANKS BENCHMARK

This paper reports the main ideas of a new technique for real-time fault-tolerant control (FTC) and demonstrates the effectiveness of this technique on a two-tanks process benchmark. The proposed technique relies on the operating data produced by the plant and on the control objective expressed quantitatively by a performance functional. The resulting control system architecture is of a supervisory type with the main feature that it achieves real-time fault tolerant control without on-line model-based fault detection and isolation (FDI) algorithms. The research activity has been performed within the framework of the research project Intelligent Fault Tolerant Control in Integrated Systems-IFATIS- funded by the European Union.

LINEAR OPTIMIZATION OF PARAMETERS IN DYNAMIC THRESHOLD GENERATORS

A model-based fault detection algorithm for linear systems with uncertain parameters is treated. An error system, bilinear in the uncertainties, generates the residual. The residual is compared to a threshold, which is generated by a linear system with the unknown uncertainty upper bounds as parameters. These unknown uncertainty upper bounds can be substituted by design parameters and this article suggests an algorithm to choose design parameter values such that the threshold is larger than the residual when no fault is present. This parameter design algorithm is applied to a sensor fault detection algorithm for a jet engine.

Model-based fault diagnosis in continuous dynamic systems

Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.

Fault Detection Via Continuous-Time Parameter Estimation

In this paper, recursive parameter estimation algorithms are used to only estimate the parameters of a given element in a global plant model. Continuous-time models of the plant are mainly considered. The use of the plant a priori information reduces the computational effort. The technique appears suitable for model-based fault detection algorithms.