Model-based Fault Detection Scheme Research Articles

Learning-Based Faulty State Estimation Using SOH-Coupled Model Under Internal Thermal Faults in Lithium-Ion Batteries

Estimating the state of charge (SOC), state of health (SOH), and core temperature under internal faults will significantly improve the battery management system’s (BMS’s) autonomy and accuracy in range prediction. This paper presents a neural network (NN) based state estimation scheme that can estimate the SOC, core temperature, and SOH under internal faults in lithium-ion batteries (LIBs). First, we propose a model-based internal fault detection scheme by employing a SOH-coupled electro-thermal-aging model (ETA) of the LIB. Then, a nonlinear observer is used to estimate the proposed SOH-coupled model’s healthy states for a residual generation. The fault diagnosis scheme compares the output voltage and surface temperature residuals against the designed adaptive threshold to detect thermal faults. The adaptive threshold effectively alleviates the false positives due to degradation and model uncertainties of the battery under no-fault conditions. Upon fault detection, we employ an additional NN-based observer in the second step to learn the faulty dynamics. A novel NN weight tuning algorithm is proposed using the measured voltage, surface temperature, and estimated healthy states. The convergence of the nonlinear and NN-based observer state estimation errors is proven using the Lyapunov theory. Finally, numerical simulation results are presented.

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

Improved fault detection based on kernel PCA for monitoring industrial applications

The conventional Kernel Principal Component Analysis (KPCA) -based fault detection technique requires more computation time and memory storage space to analyze large-sized datasets. In this context, two techniques, Spectral Clustering (SpC) and Random Sampling (RnS), are developed to reduce the dataset size by retaining the more relevant observations while preserving the main statistical characteristics of the original dataset. These two techniques and others use the training dataset from two different industrial processes, Tennessee Eastman (TEP) and Cement Plant (CP) to be reduced and provided to build the Reduced KPCA (RKPCA) model-based fault detection scheme. The obtained results show the effectiveness of the proposed techniques in terms of some fault detection performance indices and computation costs.

Model-based fault diagnosis of sliding gates electro-mechanical actuators transmission components with motor-side measurements

This paper presents a model-based fault detection and isolation scheme for the transmission components of Electro-Mechanical Actuators, applied to the actuation of sliding gates. The most important failures are investigated by a Failure Mode, Effects, and Criticality Analysis procedure. Following Failure Mode, Effects, and Criticality Analysis, the components selected for the development of the diagnostic algorithm are the nylon gear and pinion of the Electro-Mechanical Actuator, and the rack of the gate. The proposed diagnostic algorithm is able to isolate two out of the three types of faults. The overall procedure is validated by experimental results.

An Open-Circuit Fault Detection and Localization Scheme for Cascaded H-Bridge Multilevel Converter Without Additional Sensors

In this article, a new model-based fault detection and localization scheme is proposed for cascaded H-bridge (CHB) multilevel converter to localize the open-circuit faults without needing any additional sensors. The grid current is estimated from the gird voltage, switching states, and dc bus voltages, and compared with the measured grid current. Any significant deviations in the estimated and actual grid currents of the CHB trigger an open-circuit (OC) fault. Once the fault is detected, modulation scheme is switched from phase-shifted unipolar SPWM to phase-shifted bipolar sine pulse width modulation (SPWM) to localize the OC faults. At selected time instants, the change in measured grid current for a specific period is calculated and compared with the estimated change in grid current from the system model. The abovementioned comparisons are used to localize the OC faults in the CHB. The proposed method is validated on a scale-down CHB prototype developed in the laboratory environment, and experimental results are presented to demonstrate the effectiveness of the proposed method.

Efficient model-based DC fault detection and location scheme for multi-terminal HVDC systems with voltage source converter

Efficient model-based DC fault detection and location scheme for multi-terminal HVDC systems with voltage source converter

Stator-Winding Incipient Shorted-Turn Fault Detection for Motor System in Motorized Spindle Using Modified Interval Observers

As an important part of the power source, the motorized spindle is considered as the components in the high-end computer numerical control (CNC) machine tools, which is required as special attention to avoid expensive production shutdown due to the appearance of massive failures. Therefore, it is necessary to detect the incipient fault of the motorized spindle by establishing an appropriate model. This article presents a scheme for detecting the stator-winding incipient shorted-turn fault of the motor system in the motorized spindle. As the cornerstone of the model-based fault detection scheme, the electromagnetic coupling motor model in the motorized spindle with the stator-winding incipient shorted-turn fault under dq coordinate is given. In the presence of electromagnetic disturbance, two aspects can make the stator-winding shorted-turn fault of the motor in motorized spindle detected earlier. First, the modified interval observer based on the established mathematical model of the motor in the motorized spindle has more design freedom than the Luenberger interval observer, which can make the residual interval generated in the optimization process have better performance and a wider range of applications. Second, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance is introduced in the process of residual interval generation, which can make it have better electromagnetic disturbance robustness and incipient fault sensitivity. The effectiveness of the proposed scheme is illustrated by numerical simulation and is further validated by lab experiments on a 9.8-kW motorized spindle test rig.

Fault detection and isolation of control moment gyros for satellite attitude control subsystem

Abstract Control moment gyros, as one of the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location, and identify their severity can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this paper, a model-based fault detection, isolation, and identification scheme is proposed and evaluated. Firstly, an adaptive threshold fault detection algorithm is proposed, using Unscented Kalman filters in conjunction with residual and innovation sequences. Secondly, a fault isolation and identification algorithm is proposed using a binary grid search method to adapt joint estimation Kalman filter’s covariance matrix once a fault is detected. Extensive Monte Carlo simulations are conducted to evaluate the performance of the proposed schemes for a 3-axis stabilized satellite with a four-single-gimbal control moment gyro cluster. Results show superior performance of the proposed methods with faster tracking and more accuracy.

A Fast Phase Variable &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$abc$&lt;/tex-math&gt; &lt;/inline-formula&gt; Model of Brushless PM Motors Under Demagnetization Faults

This paper addresses a simple and computationally efficient dynamic model for brushless permanent magnet (PM) motors under PM demagnetization faults. The impact of stator slots and spatial disposition of the windings are taken into account to improve the accuracy of a lumped-parameter $abc$ model. This model is suitable for tracking the dynamic behavior of fault components, as it can be accompanied by any control scheme and/or be used for model-based fault detection schemes. In addition, parameters of this model can be automatically tuned by an optimization process in the offline phase of the healthy machine. Finite-element simulations are carried out for proving the effectiveness of the proposed model. Verification is done in both time and frequency domains for four PM motors with different rotor and stator configurations. The simulation results are experimentally verified.

Threshold Tuning-Based Wearable Sensor Fault Detection for Reliable Medical Monitoring Using Bayesian Network Model

As the medical body sensor network (BSN) is usually resource limited and vulnerable to environmental effects and malicious attacks, faulty sensor data arise inevitably which may result in false alarms, faulty medical diagnosis, and even serious misjudgment. Thus, faulty sensory data should be detected and removed as much as possible before being utilized for medical diagnosis-making. Most available works directly employed fault detection schemes developed in traditional wireless sensor network (WSN) for body sensor fault detection. However, BSNs adopt a very limited number of sensors for vital information collection, lacking the information redundancy provided by densely deployed sensor nodes in traditional WSNs. In light of this, a Bayesian network model-based sensor fault detection scheme is proposed in this paper, which relies on historical training data for establishing the conditional probability distribution of body sensor readings, rather than the redundant information collected from a large number of sensors. Furthermore, the Bayesian network-based scheme enables us to minimize the inaccuracy rate by optimally tuning the threshold for fault detection. Extensive online dataset has been adopted to evaluate the performance of our fault detection scheme, which shows that our scheme possesses a good fault detection accuracy and a low false alarm rate.

Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines

Abstract This paper presents model-based fault detection, fault isolation, and fault-tolerant control schemes focused on blade pitch systems in floating wind turbines. Fault detection, isolation, and accommodation techniques are required to achieve high power capture efficiency and structural reliability in floating wind turbines. Faults in blade pitch systems should be detected at an early stage to prevent catastrophic failures. To detect faults of the blade pitch systems, a Kalman filter is designed to estimate the blade pitch angle of the system. The fault isolation algorithm is based on inference methods and capable of determining the fault type, location, magnitude and time. The fault-tolerant controller based on a reconfiguration block with a virtual sensor and shutdown mode controls the floating wind turbine to avoid unexpected external loads. The proposed methods are demonstrated in case studies with stochastic wind and wave conditions that considering different types of faults, such as biases and fixed outputs in pitch sensors and stuck pitch actuators. The simulation results show that the proposed methods can detect and isolate multiple faults effectively at an early stage. Additionally, the effectiveness of the fault-tolerant control systems for different load cases for single and multiple fault conditions is verified by numerical simulations.

The merits of exergy-based fault detection in petrochemical processes

The complexity and multi-domain nature of petrochemical (PC) plants make the application of conventional model-based fault detection and isolation (FDI) techniques a challenging endeavour. Although hybrid FDI schemes aim to address this shortfall, many are simply a combination of data-driven techniques that exclude physical system information. In this work, a hybrid approach to FDI of a PC process is proposed that is based on an exergy-data abstraction. Data from an actual system is abstracted to system exergy, based on physical knowledge of the system and then used as a diagnostic metric for the FDI scheme. In this paper, it is shown why energy-based approaches are lacking when considering petrochemical processes. After presenting a novel method for the real-time, automatic calculation of chemical exergy in Aspen HySys® the applicability of exergy-based fault detection is investigated. Application of the exergy-based fault detection scheme shows a marked improvement over the energy-based approach with perfect detectability and isolability of the considered process faults. The exergy-based fault detection technique shows merit in comparison to the energy-based detection scheme. Additionally, and more importantly, exergy-based characterisation allows the use of more sophisticated model-based fault detection schemes to petrochemical processes.

A distributed fault detection and isolation algorithm based on Moving Horizon Estimation

This paper presents a new distributed iterative model-based fault detection and isolation scheme based on moving horizon estimation (MHE). The system is partitioned into sub-units and local MHE algorithms are used to estimate both the local states and the possible faults, modeled as additive signals on the inputs and/or the outputs. A preliminary convergence analysis of the algorithm is reported and a simulation example is discussed to witness the performance of the approach.

Distributed Interacting Multiple Filters for Fault Diagnosis of Navigation Sensors in a Robotic System

In this paper, a distributed interacting multiple model-based fault detection and isolation (FDI) scheme is presented for FDI of navigation sensors composed of inertial (accelerometers and gyroscopes) and camera sensors in a robotic system in which noisy and erroneous measurements of microelectro mechanical system (MEMS)-based inertial sensors are fused with a photogrammetric camera. Multiple models are employed to describe different scenarios of hard faults (failures) in the sensors where the models are different in each scenario because inertial sensor drifts (as soft or partial faults) are also modeled and augmented to the motion state parameters. Having several faulty modes due to the possibility of single and multiple failures in the sensors, it is proposed in this paper to decompose the system to interacting observable subsystems with reduced size and decoupled model sets. The system and the corresponding model set are decomposed using a graph theoretic decomposition approach. Distributed interacting multiple Kalman and extended Kalman filters are then designed for the purpose of FDI. Experimental results based on data from a 3-D MEMS inertial measurement unit and 3-D camera system are used to demonstrate the efficiency of the method.

Resilient Control for Networked Control Systems Subject to Cyber/Physical Attacks

In this paper, we investigate a resilient control strategy for networked control systems (NCSs) subject to zero dynamic attacks which are stealthy false-data injection attacks that are designed so that they cannot be detected based on control input and measurement data. Cyber resilience represents the ability of systems or network architectures to continue providing their intended behavior during attack and recovery. When a cyber attack on the control signal of a networked control system is computed to remain undetectable from passive model-based fault detection and isolation schemes, we show that the consequence of a zero dynamic attack on the state variable of the plant is undetectable during attack but it becomes apparent after the end of the attack. A resilient linear quadratic Gaussian controller, having the ability to quickly recover the nominal behavior of the closed-loop system after the attack end, is designed by updating online the Kalman filter from information given by an active version of the generalized likelihood ratio detector.

Robust Fault Detection, Isolation, and Accommodation of Current Sensors in Grid Side Converters

The integration of modern renewable energy sources is enabled by the grid side converter (GSC) based on power electronic technology. The operation of the GSC is properly regulated by the GSC controller according to sensor measurements (i.e., of the grid voltage, of the line currents, and of the voltage at the dc-link). However, in case of current sensor faults, the operation of the GSC and of the entire renewable system can be critically affected and catastrophic failures may occur if the sensor fault is not accommodated on time. This paper proposes a model-based Fault Detection, Isolation, and Accommodation (FDIA) scheme, which enables the GSC to overcome faults that may occur on the associated current sensors. The sensor fault detection and isolation scheme has been designed based on analytical redundancy relations, while the accommodation of the faults is based on an adaptive estimation scheme. The proposed FDIA scheme has been applied on a modern GSC and the effectiveness of the scheme has been tested under several multiple current sensor faults and under several grid conditions. Furthermore, the FDIA scheme enables the GSC to operate properly (without causing failures or damages to its components) under current sensor faults and thus, the reliability of the GSC is enhanced.

Robust model-based fault diagnosis of mechanical drive train in V47/660 kW wind turbine

In this study, a robust fault diagnosis scheme for V47/660 kW wind turbine is proposed. A comprehensive mathematical model for mechanical drive train and gearbox dynamic of V47/660 kW wind turbine operating in Manjil wind farm, Gilan province, Iran, is developed based on which the model-based Fault Detection and Isolation (FDI) scheme is designed. The proposed FDI scheme detects various critical and common sensor faults, actuator faults and components faults. A mixed Unknown Input-Proportional Integral Observer (UI-PIO) method and the parameter estimation method are used for fault detection and isolation. The robustness of the residuals to disturbances is also addressed. Simulation results using experimental data are presented to demonstrate the effectiveness of the proposed fault diagnosis system.

Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults

This paper studies a model-based fault detection and isolation (FDI) scheme for robot manipulators with actuator and sensor faults. Without adding redundant joint sensor measurements, the multiple faults simultaneously occurring in the actuators and sensors of the manipulator are detected, estimated and isolated. Considering Lipschitz-like nonlinearities and modeling uncertainties, a nonlinear adaptive observer is presented to estimate the faulty parameters with a pre-specified estimation error bound. The performances of the proposed FD scheme, including the robustness of observers to the uncertainties, the accuracy of fault estimation and the rapidity of fault diagnosis, are rigorously analyzed. The proposed approach is verified by a simulation of Integrated Motion Inc. (IMI) two-link, revolute, direct-drive robot manipulator.

Model-based fault detection, estimation, and prediction for a class of linear distributed parameter systems

This paper addresses a new model-based fault detection, estimation, and prediction scheme for linear distributed parameter systems (DPSs) described by a class of partial differential equations (PDEs). An observer is proposed by using the PDE representation and the detection residual is generated by taking the difference between the observer and the physical system outputs. A fault is detected by comparing the residual to a predefined threshold. Subsequently, the fault function is estimated, and its parameters are tuned via a novel update law. Though state measurements are utilized initially in the parameter update law for the fault function estimation, the output and input filters in the modified observer subsequently relax this requirement. The actuator and sensor fault functions are estimated and the time to failure (TTF) is calculated with output measurements alone. Finally, the performance of detection, estimation and a prediction scheme is evaluated on a heat transfer reactor with sensor and actuator faults.

Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter

This paper presents an effective model-based sensor fault detection and isolation (FDI) scheme for a series battery pack with low computational effort. The large number of current and voltage sensors in the battery pack, make it of high computational complexity. The major purpose of sensor FDI is to guarantee the healthy operations of the battery management system (BMS), and thus to prevent the battery from over-charge and over-discharge. In the voltage sensors fault scenarios, the most possibly being over-charged and over-discharged cells are these two cells with the maximum and minimum voltage respectively. Within the proposed scheme, these two cells are monitored in real time to diagnose the pack current sensor fault, or a voltage sensor fault of these two cells, while the rest cells are monitored offline with a long time interval, guaranteeing other voltage sensors working normally. For the scheme implementation, adaptive extended Kalman filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. Then the residuals are evaluated by a statistical inference method that determines the presence of the fault. Finally, the effectiveness of the proposed sensor FDI scheme is experimentally validated with a series battery pack under the UDDS driving cycles.