Model-based Fault Diagnosis Scheme Research Articles

A Combined Model-Based and Data-Driven Fault Diagnosis Scheme for Lithium-Ion Batteries

A Combined Model-Based and Data-Driven Fault Diagnosis Scheme for Lithium-Ion Batteries

A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in Electric Vehicles.

In recent years, electric vehicles powered by lithium-ion batteries have developed rapidly, and the safety and reliability of lithium-ion batteries have been a paramount issue. Battery management systems are highly dependent on sensor measurements to ensure the proper functioning of lithium-ion batteries. Therefore, it is imperative to develop a suitable fault diagnosis scheme for battery sensors, to realize a diagnosis at an early stage. The main objective of this paper is to establish validated electrical and thermal models for batteries, and address a model-based fault diagnosis scheme for battery sensors. Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault. To verify the estimation effect of the proposed scheme, various types of faults are utilized for simulation experiments. Battery experimental data are used for battery modeling and observer-based fault diagnosis in battery sensors.

Model-Based Fault Diagnosis Scheme for Current and Voltage Sensors in Grid Side Converters

Model-Based Fault Diagnosis Scheme for Current and Voltage Sensors in Grid Side Converters

High-Gain Observer-Based Fault Detection Scheme for Short-Circuit Switch Faults in Grid-Connected PV Systems by using Optimizers

This paper proposes a model-based fault diagnosis scheme for grid-connected photovoltaic systems with distributed maximum power point tracking (MPPT) strategy. Specifically, short-circuit switch faults in optimizers (boost dc/dc converters in series connection for MPPT purposes) are considered in this study. Depending on the application, SCFs can be more or less detrimental to the stability and integrity of systems. In systems with high penetration, disturbances in voltage or frequency can be generated that can, in the worst case, cause instabilities in the system. Hence, a fast fault detection strategy is mandatory. It is worth noting that, unlike other works reported to date, the control action of the optimizers presents singularities under this fault scenario, making the fault diagnosis task difficult. For this, a decoupled subsystem from i) dc/ac converter dynamics and ii) irradiance changes are obtained for fault and disturbance isolation purposes. Then, a dedicated high-gain observers bank is proposed for a residual generation. In this way, fault time detection is achieved in 8 switching periods. Finally, a numerical evaluation has been carried out to validate the ideas proposed in this paper. For this, three series-connected optimizers interconnected to a three-phase 220V 60 Hz grid through a neutral point clamped (NPC) inverter with a first-order L filter were considered in the simulation.

A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

Fault diagnosis of a nonlinear hybrid system using adaptive unscented Kalman filter bank

In this paper, a model-based fault diagnosis scheme of a nonlinear hybrid system using an adaptive unscented Kalman filter (AUKF) bank is proposed. The hybrid system is an amalgamation of discrete dynamics and continuous states. Fault diagnosis for simultaneous occurrences of multiple faults such as leakage fault, clogging fault, sensor fault, and actuator fault on a benchmark three-tank system are simulated. The residual signal based output generates some discrete modes that guarantee the uniqueness of the concerning fault. The efficacy of the proposed scheme is compared with that of the adaptive extended Kalman filter (AEKF) bank on the same system to prove its better response over AEKF.

Fast Adaptive Fault Accommodation in Floating Offshore Wind Turbines via Model-Based Fault Diagnosis and Subspace Predictive Repetitive Control

As Floating Offshore Wind Turbines (FOWTs) operate in deep waters and are subjected to stressful wind and wave induced loads, they are more prone than onshore counterparts to experience faults and failure. In particular, the pitch system may experience Pitch Actuator Stuck (PAS) type of faults, which will result in a complete loss of control authority. In this paper, a novel fast and adaptive solution is developed by integrating a model-based Fault Diagnosis (FD) scheme and the Subspace Predictive Repetitive Control (SPRC). The FD role is to quickly detect and isolate the failed pitch actuator. Based on the fault isolation results, a pre-tuned adaptive SPRC is switched online in place of the existing one, whose initial values of the parameters has been tuned offline to match the specific faulty case. After that, SPRC employs subspace identification to continuously identify a linear model of the wind turbine over a moving time window, and thereby formulate an adaptive control law to alleviate the PAS-induced loads. Results show that the developed architecture allows to achieve a considerable reduction of the PAS-induced blade loads. More importantly, the time needed to reduce the PAS-induced loads are significantly shortened, thus avoiding further damage to other components during the adaption time and allowing continued power generation.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.

Fault modelling and detection in power generation, transmission and distribution systems

The study discusses a model-based fault diagnosis scheme for detection of faults in power systems. The fault models in power generating units (PGUs) and transmission and distribution networks (T&DN) have been derived in state space form. An observer-based fault detection filter (FDF) has been used to detect these faults. Observer-based FDF is used because of its simplicity, reliability and ease of implementation. A single machine infinite bus power system is considered wherein a synchronous generator is connected to a T&DN through a bus system. Actuator and sensor faults in PGU, and symmetric faults in T&DN have been modelled and simulated in Matlab/Simulink. The simulation results show that a fault, whenever occurs in the aforementioned parts of power systems, is detected using the knowledge of the faults models proposed in this work.

Adaptive Extended Kalman Filter Based Fault Detection and Isolation for a Lithium-Ion Battery Pack

Abstract To monitor the battery system, a well-designed battery management system with a set of current and voltage sensors is demanded to properly track the battery properties. It is imperative to design a reliable and robust diagnostic scheme in case of the employed sensors faults occurred. This paper presents a model-based fault diagnosis scheme to detect and isolate the faults of the current and voltage sensors applied in the series battery pack based on an adaptive extended kalman filter, and the robustness of the proposed diagnostic strategy is ensured. The diagnostic scheme is validated in the Matlab/Simulink, and the simulation results show the effectiveness of the proposed strategy in detecting and isolating various fault scenarios using the real-world driving cycles.

Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles

The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.

Robust model-based sensor fault monitoring system for nonlinear systems in sensor networks.

A new model-based sensor fault diagnosis (FD) scheme, using an equivalent model, is developed for a kind of Multiple Inputs Multiple Outputs (MIMO) nonlinear system which fulfills the Lipschitz condition. The equivalent model, which is a bank of one-dimensional linear state equations with the bounded model uncertainty, can take the place of a plant's exact nonlinear model in the case of sensor FD. This scheme shows a new perspective whereby, by using the equivalent model, it doesn't have to study the nonlinear internal structure character or get the exact model. The influence of the model uncertainty on the residuals is explained in this paper. A method, called pretreatment, is utilized to minimize the model uncertainty. The eigenstructure assignment method with assistant state is employed to solve the problem of perfect decoupling against the model uncertainty, disturbance, system faults, the relevant actuator faults, or even the case of no input from the relevant actuator. The realization of the proposed scheme is given by an algorithm according to a single sensor FD, and verified by a simulation example. Depending on the above, a sensor fault monitoring system is established by the sensor network and diagnosis logic, then the effectiveness is testified by a simulation.

Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types

This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.

A model-based fault diagnosis scheme for wheeled mobile robots

In this paper, a fault diagnosis scheme for wheeled mobile robots is presented. In the fault detection module, a nonlinear observer is designed based on the mobile robot dynamic model. A fault is detected when at least one of the residuals exceeds its corresponding threshold. After the fault is detected, the fault isolation module is activated to isolate three types of fault: right wheel fault, left wheel fault, and other changing dynamic parameter faults. Three simulation examples are performed to show the effect of each fault to the tracking performance of mobile robot in a real situation. The simulation results demonstrate the effectiveness of our proposed approach for fault detection and isolation in wheeled mobile robots.

A unified model-based fault diagnosis scheme for non-linear discrete-time systems with additive and multiplicative faults

In this paper, a unified model-based fault diagnosis (MFD) scheme that deals with both multiplicative actuator and additive system faults is designed. For a class of uncertain non-linear discrete-time systems, this MFD scheme is capable of not only detecting both additive and multiplicative actuator faults but also identifying the fault type. Faults are detected by using a novel fault detection observer consisting of two online approximators in discrete-time (OLAD) and a robust adaptive term. Upon detection, a fault diagnosis scheme is introduced to determine the fault type by monitoring the input residual generated via the first OLAD output. Upon performing the diagnosis online, the appropriate OLAD is activated in the observer and the other OLAD is switched off. Thereafter, by using both the parameter update law of the active OLAD and user-selected failure threshold, an online time-to-failure scheme is introduced. In the case of multiplicative faults, boundedness of the detection residual and parameter estimation errors is shown while in the case of additive faults, the asymptotic convergence of the detection residual and parameter estimation errors is guaranteed due to the robust adaptive term. Finally, a simulation example is used to demonstrate the proposed fault diagnosis scheme.

Unsymmetrical fault diagnosis in transmission/distribution networks

In this paper, we design a model-based fault diagnosis scheme capable of real time detection of symmetrical and unsymmetrical faults in the transmission/distribution network of a sample power system comprising a single-machine connected to an infinite bus-bar. A generic state space linear model of the system is first derived, and then a fault diagnosis scheme is designed, based on sliding mode observer (SMO) theory. Accuracy and real-time fault detection capabilities of the proposed fault diagnosis scheme are illustrated through simulation studies.

Robust automatic tuning of diagnosis methods via an efficient use of costly simulations

The robust tuning methodology developed in this paper aims at adjusting automatically the hyperparameters of fault-diagnosis procedures for complex case studies. The strategy should make an efficient use of computer simulations of these case studies, which will usually be computationally expensive. To this end, Kriging-based optimization is called upon. Robustness to environmental disturbances is achieved by continuous minimax optimization, and handled through an iterative relaxation procedure. This strategy is applied to the automatic tuning of a model-based fault-diagnosis scheme for a realistic aerospace application.

Model-Based Fault Diagnosis of a NOx Aftertreatment System

The Lean NOx Trap (LNT) is an aftertreatment device used to attain a reduction in nitrogen oxide emissions for Diesel and lean burn engines. The LNT is typically used as a storage device, capturing NOx during lean engine operation. The trap can be regenerated by controlling the exhaust air-fuel ratio to create a rich gas mixture. Under rich conditions, the stored NOx is released and catalytically converted. This way, tailpipe emissions can be significantly reduced by properly modulating the lean (storage) and rich (regeneration) periods. To maintain the LNT operate with high conversion efficiency, an optimized control of the regeneration scheduling is required. In addition, LNT systems require fault diagnostic schemes to detect and isolate faults, typically related to sulfur and thermal damages. This paper deals with the design and validation through simulation of a model-based fault diagnosis scheme for a LNT system. The mathematical model of the subsystem, based on the physics of the processes involved, consist of time- varying nonlinear ODEs. The proposed diagnostic approach is based on the generation of residuals using system models and through comparison of the predicted and measured value of selected variables, including the AFR, catalyst output temperature and the NOx concentration at the output of the LNT. The paper focuses on detection and isolation of controller faults and LNT parametric faults related to sulfur and thermal damage. The model utilized in the diagnostic scheme, which includes sulfur poisoning and thermal deactivation processes, has been experimentally validated from data collected on a Diesel LNT system and integrated with a quasi-steady engine and vehicle simulator to estimate tailpipe emissions during standard driving cycles.

A Model-Based Fault Detection and Diagnosis Scheme for Distributed Parameter Systems: A Learning Systems Approach

In this note, fault detection techniques based on nite dimensional results are extended and applied to a class of innite dimensional dynamical systems. This special class of systems assumes linear plant dynamics having an abrupt additive perturbation as the fault. This fault is assumed to be linear in the (unknown) constant (and possibly functional) parameters. An observer-based model estimate is proposed which serves to monitor the system's dynamics for unanticipated failures, and its well posedness is summarized. Using a Lyapunov synthesis approach extended and applied to innite dimensional systems, a stable adaptive fault diagnosis (fault parameter learning) scheme is developed. The resulting parameter adaptation rule is able to \sense the instance of the fault occurrence. In addition, it identies the fault parameters using the additional assumption of persistence of excitation. Extension of the adaptive monitoring scheme to incipient faults (time varying faults) is summarized. Simulations studies are used to illustrate the applicability of the theoretical results.