A Decoupled Parameters Estimators for in Nonlinear Systems Fault diagnosis by ANFIS

This paper presents a new and efficient Adaptive Neural Fuzzy Inference Systems approach for satellite’s attitude control systems (ACSs) fault diagnosis. The proposed approach formulates the fault modelling problem of system component into an on-line parameters estimation The learning  ability of the adaptive neural fuzzy inference system allow as to decoupling the effect of each fault from the estimation of the others.  Our solution provides a method to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite.DOI:http://dx.doi.org/10.11591/ijece.v2i2.22

A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

A new method of fault detection and fault tolerant control is proposed in this paper for mechanical systems and aeronautical systems. The faults to be estimated and diagnosed are malfunctions occurred within the control loops of the systems, rather than some static faults, such as gearbox fault, component cracks, etc. In the proposed method two neural networks are used as on-line estimators, the fault will be accurately estimated when the estimators are adapted on-line with the post fault dynamic information. Furthermore, the estimated value of faults are used to compensate for the impact of the faults, so that the stability and performance of the system with the faults are maintained until the faulty components to be repaired. The sliding mode control is used to maintain system stability under the post fault dynamics. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimators are guaranteed to converge to the fault to be diagnosed, while the entire closed-loop system stability is guaranteed with all variables bounded. The main contribution of this paper to the knowledge in this field is that the proposed method cannot only diagnose and tolerant with constant fault, also diagnose and tolerant with the time-varying faults. This is very important because most faults occurred in industrial systems are time-varying in nature. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results are compared with two existing methods that can cope with constant faults only, and the superiority is demonstrated

Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented

Fault-Tolerant Control of Autonomous Ground Vehicle under Actuator and Sensor

Unmanned ground vehicles have a wide range of potential applications including autonomous driving, military surveillance, emergency responses, and agricultural robotics, etc. Since such autonomous vehicles need to operate reliably at all times, despite the possible occurrence of faulty behaviors in some system components, the development of fault-tolerant control schemes is a crucial step in ensuring reliable and safe operations. In this research, a fault-tolerant control scheme is developed for a nonlinear ground vehicle model with possible occurrence of both actuator faults in the form of loss of effectiveness (LOE) and sensor bias faults. Based on the vehicle and fault models under consideration, the unknown fault parameters are estimated online using adaptive estimation methods. The estimated fault parameters are used for accommodating the fault effect to maintain satisfactory control performance even in the presence of faults. Real-time algorithm implementation and demonstration using the Qbot2e ground robot by Quanser are conducted to show the effectiveness of the fault-tolerant control algorithm

Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach

In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches

Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes

Real-Time Monitoring and Fault Diagnostics in Roll-To-Roll Manufacturing Systems

A roll-to-roll (R2R) process is a manufacturing technique involving continuous processing of a flexible substrate as it is transferred between rotating rolls. It integrates many additive and subtractive processing techniques to produce rolls of product in an efficient and cost-effective way due to its high production rate and mass quantity. Therefore, the R2R processes have been increasingly implemented in a wide range of manufacturing industries, including traditional paper/fabric production, plastic and metal foil manufacturing, flexible electronics, thin film batteries, photovoltaics, graphene films production, etc. However, the increasing complexity of R2R processes and high demands on product quality have heightened the needs for effective real-time process monitoring and fault diagnosis in R2R manufacturing systems. This dissertation aims at developing tools to increase system visibility without additional sensors, in order to enhance real-time monitoring, and fault diagnosis capability in R2R manufacturing systems. First, a multistage modeling method is proposed for process monitoring and quality estimation in R2R processes. Product-centric and process-centric variation propagation are introduced to characterize variation propagation throughout the system. The multistage model mainly focuses on the formulation of process-centric variation propagation, which uniquely exists in R2R processes, and the corresponding product quality measurements with both physical knowledge and sensor data analysis. Second, a nonlinear analytical redundancy method is proposed for sensor validation to ensure the accuracy of sensor measurements for process and quality control. Parity relations based on nonlinear observation matrix are formulated to characterize system dynamics and sensor measurements. Robust optimization is designed to identify the coefficient of parity relations that can tolerate a certain level of measurement noise and system disturbances. The effect of the change of operating conditions on the value of the optimal objective function – parity residuals and the optimal design variables – parity coefficients are evaluated with sensitivity analysis. Finally, a multiple model approach for anomaly detection and fault diagnosis is introduced to improve the diagnosability under different operating regimes. The growing structure multiple model system (GSMMS) is employed, which utilizes Voronoi sets to automatically partition the entire operating space into smaller operating regimes. The local model identification problem is revised by formulating it into an optimization problem based on the loss minimization framework and solving with the mini-batch stochastic gradient descent method instead of least squares algorithms. This revision to the GSMMS method expands its capability to handle the local model identification problems that cannot be solved with a closed-form solution. The effectiveness of the models and methods are determined with testbed data from an R2R process. The results show that those proposed models and methods are effective tools to understand variation propagation in R2R processes and improve estimation accuracy of product quality by 70%, identify the health status of sensors promptly to guarantee data accuracy for modeling and decision making, and reduce false alarm rate and increase detection power under different operating conditions. Eventually, those tools developed in this thesis contribute to increase the visibility of R2R manufacturing systems, improve productivity and reduce product rejection rate.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146114/1/huanyis_1.pd