Model-based Fault Detection Research Articles

Model-based fault detection algorithm for liquid hydrogen refueling system using CUSUM method

The global focus on the role of hydrogen energy in achieving carbon neutrality is increasing, particularly in transportation. Establishing and operating hydrogen refueling stations for fuel cell electric vehicles (FCEVs) are gaining prominence. This study proposes a model-based fault detection algorithm to enhance safety at large-capacity liquid hydrogen (LH2) refueling stations. First, the LH2 refueling system is modeled using Aspen HYSYS, estimating the heat transfer coefficient of the storage tank to meet the normal evaporation rate (NER) specification of 0.9 % per day. Second, diverse fault scenarios are identified via a Hazard and Operability Study (HAZOP), and simulation data are generated for the normal and fault scenarios. Finally, a fault detection algorithm utilizing the cumulative summation (CUSUM) is developed, with its threshold determined by risk levels analyzed in HAZOP. This allowed for tighter fault detection as risk levels increased. The algorithm successfully identified faults for all 11 scenarios.

False Data Injection Attack Detection, Isolation, and Identification in Industrial Control Systems Based on Machine Learning: Application in Load Frequency Control

The integration of advanced information and communication technology in smart grids has exposed them to increased cyber attacks. Traditional model-based fault detection systems rely on mathematical models to identify malicious activities but struggle with the complexity of modern systems. This paper explores the application of artificial intelligence, specifically machine learning, to develop fault detection mechanisms that do not depend on these models. We focus on operational technology for fault detection, isolation, and identification (FDII) within smart grids, specifically examining a load frequency control (LFC) system. Our proposed approach uses sensor data to accurately identify threats, demonstrating promising results in simulated environments.

Diagnosing Incipient Faults for a Faster Adoption of Sustainable Aerospace Technologies

Next-generation aircraft require the development and integration of a deal of innovative green technologies to meet the ambitious sustainability goals set for aviation. Those transformational efforts are associated with a tremendous increase in the complexity of the onboard systems and their multiphysics-coupled behaviors and dynamics. A critical aspect relates to the identification of the coupled faults resulting from the integration of those green technologies, which introduce damage identifiability issues and demand new approaches for the efficient and accurate identification of non-nominal fault conditions. Model-based fault detection and identification (FDI) methodologies have been proven essential to identify onboard the damages affecting the systems from physical signal acquisitions, but existing methods are typically computationally expensive and fail to capture incipient coupled faults, which prevents their adoption and scaling with the increasing complexity of novel multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed, and the exact identification of the incipient fault condition was achieved one order of magnitude faster than with standard algorithms.

Model based fault detection diagnosis and classification in shell and tube heat exchanger using intelligent techniques

A method for Model-Based Approach for Fault Detection and Diagnosis has been put forth in this research. The purpose of this study is to build a heat exchanger model and to locate, categorize faults in the heat exchanger system. One of the known methods for fault control in industrial processes is fault detection and diagnosis (FDD). Both linear and non-linear systems can benefit from the FDD technique; this work describes fault identification and diagnosis for the non-linear system, the heat exchanger. A realistic mathematical model of the heat exchanger with its set of inputs and outputs has therefore been constructed, as the fault detection of the heat exchanger is related to the mathematical model for its operation. Once the mathematical model has been realized using the formulated equation, the process passes on to fault detection. Residual, which defines the failure symptoms of the system, is used to detect faults. In order to prevent failures and defective circumstances from occurring in existing systems, observer design is employed as one of the most powerful and reliable approaches residual generation. Fault diagnosis is carried out using a Fuzzy logic controller (FLC), which is designed based on the residuals obtained, and fault classification is made using the FLC via a fixed threshold. The paper's primary goals are to reduce errors and enhance non-linear systems' overall performance in industrial processes. According to the findings of the simulation, errors in sensors, processes, and actuators can be classified according to a set threshold, and they can be controlled using a traditional PID controller.

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

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.

Real-Time Simulation and Experimental Implementation of Luenberger Observer-Based Speed Sensor Fault Detection of BLDC Motors

Abstract Modern control systems’ dependability, safety and efficiency have all been improved by studying fault-tolerant control systems (FTCS). FTCS techniques can typically be active or passive controls. The fault detection and diagnosis (FDD) method is used in this study’s active control branch to identify probable faults that could develop in the speed Hall sensors of brushless DC motors (BLDC). FDD methodologies can be categorised into two types, depending on the available data and the process involved: model-based methods and data-based methods. The proposed approach in this study explores the implementation of the Luenberger observer methodology as part of the model-based approach. The chosen methodology was practically implemented and subjected to experimental evaluation. The proposed observer relies on the residual signal, which displays the difference between the plant’s observed and estimated speed signals and serves as a failure alert for the entire system. Given the increasing demand for BLDC motors in various industrial control applications, including medical fields, automation and robotics, this particular motor was selected as a benchmark to thoroughly evaluate and validate the proposed method. The primary contribution of this paper lies in the real-time application of model-based sensor fault detection methods to BLDC motors. The efficiency of the suggested method is showcased through extensive MATLAB simulations, where the obtained results confirm the successful detection of faults with a high level of responsiveness. As a result, the project was successfully implemented in real-time, and the experimental results exhibited a close correlation with the simulated outcomes. This consistency between simulation and practical implementation validates the accuracy and reliability of the proposed methodology for detecting faults in the BLDC motor speed sensor. The results underscore the heightened reliability and safety attained by promptly and accurately detecting sensor faults during the operation of the motor.

Model-based fault detection in photovoltaic systems: A comprehensive review and avenues for enhancement

Solar photovoltaic (PV) systems have become a vital renewable energy source, witnessing rapid global demand. Nevertheless, these systems are susceptible to faults and anomalies that can deteriorate performance and yield significant consequences. Hence, this paper is dedicated to reviewing recent advancements in monitoring, modeling, and fault detection methods for PV systems. It encompasses diverse PV system types, including grid-connected, stand-alone, and hybrid configurations, and delves into the latest data acquisition and monitoring techniques. The review also discusses various performance modeling approaches, including empirical, analytical, and numerical models, highlighting the significance of model validation and calibration. Furthermore, it provides a comprehensive analysis of model-based fault detection techniques. Overall, this paper underscores the pivotal role of fault detection in PV systems and offers a thorough comprehension of available techniques vital for enhancing system management and maintenance.

A fault detection method for pitch actuators faults in wind turbines

The paper presents a model-based fault detection method for pitch actuators faults using the normalized gradient method to estimate the parameters of the pitch actuator. One major difficulty is that the input signal to the parametric estimation method must be a persistent excitation. To circumvent this, a chattering term is added to the pitch control law and the usual low-pass filters are not used for the parametrization in the normalized gradient method (thus acceleration information is used). In order to verify the proposed method, simulations are conducted within a Hardware in the Loop (HiL) platform using the wind turbine simulation software FAST (Fatigue, Aerodynamics, Structures, and Turbulence).

Fault detection of new and aged lithium-ion battery cells in electric vehicles

In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging. Battery aging primarily affects capacity and resistance, becoming more pronounced in the later stages of a battery lifespan. By incorporating aging effects into our fault diagnosis scheme, our proposed approach prevents false or missed alarms for the aged battery cells. The aging effect of battery, capacity fading and resistance growth, are considered unknown parameters. An adaptive observer is employed to design a fault detector, considering unknown parameters in the battery model. The adaptive observers are designed for two different scenarios: In the first scenario, it is presumed that aging effects remain constant over time due to their slow rate of change. Then, it is assumed that aging effects are time-varying. Therefore, the fault detection scheme can detect faults of new battery cells as well as aged cells. Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack, to demonstrate the performance of the proposed approach in more real-world scenarios. The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.

Model-Based Sensor Fault Detection and Diagnosis in Closed-Loop Power Converters for Electric Vehicles

Electric Vehicle (EV) involves electric motors equipped with closed loop powered conversion systems which use the energy from storage batteries for vehicle propulsion and rely on sensor measurements as feedback to the controller. Therefore, sensor fault detection and diagnosis are an essential process that ensures performance and safety of EVs. From the National Renewable Energy Laboratory (NREL) study it is confirmed that electrical vehicle’s fault percentages vary, however, sensor faults constitute approximately 20-30% of total faults in them. Thus, sensor fault detection is critical for effective EV operation and requires the utmost focus from EV manufacturers and operators. Model-based techniques and Kalman Filter (KF) make it possible to detect faults on sensors in closed-loop EV power converter. It involves the use of mathematical model, sensor data and KF to identify fault before it becomes a threat and guarantees proper function of the vehicle. In this article, a state-space model of a CUK converter is developed, and an LQR closed-loop control system is designed for the power converter. A sensor fault detection method was developed for the system which is then simulated and tested under sensor bias fault conditions for both open and closed-loops control for determining the difference between them. The results showed a distinctive feature extracted between these two systems in the presence of system disturbances and noise applied to the sensor readings. The second section of simulation results in a closed-loop configuration validated the mathematical equations used for sensor fault detection, demonstrating their resistance to false alarms caused by system or component faults in addition to its ability to diagnose faults.

Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

Rethinking Model-based Fault Detection: Uncertainties, Risks, and Optimization based on a Multilevel Converter Case Study

Rethinking Model-based Fault Detection: Uncertainties, Risks, and Optimization based on a Multilevel Converter Case Study

T–S Fuzzy Model-Based Fault Detection for Inductively Coupled Power Transfer Systems With Coil Misalignment

T–S Fuzzy Model-Based Fault Detection for Inductively Coupled Power Transfer Systems With Coil Misalignment

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.

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

Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics

Over the next decades, solar energy power generation is anticipated to gain popularity because of the current energy and climate problems and ultimately become a crucial part of urban infrastructure. Furthermore, in order to facilitate the rapid deployment of solar production in sustainable cities, solar technologies must be digitalized and smart. This will increase their reliability and ensure their optimal performance. This work fits into this perspective and focuses on the effectiveness of processing data using the Digital Twin (DT) framework as a state-of-art information technology for urban distributed solar PV systems. Accordingly, the present paper deals with the DT of five Building-added Photovoltaic (BAPV) systems installed at the solar village in Benguerir city, Morocco. The 3D models were generated in the Rhinoceros platform, the solar AC electrical production of each photovoltaic system is calculated by the ladybug-plugin-integrated statistical model, within the grasshopper environment. Additionally, data assessment and visualization are processed through Python. Then, a hybrid model-based and data-driven fault detection and diagnosis (FDD) approach is proposed to identify and isolate anomalies for decentralized solar PV systems at the urban scale using monitoring and inspection techniques, namely Remote Sensors (RS) and real-time solar production monitoring system. The application of the DT concept for complex dynamic systems has shown its effectiveness in ensuring optimal operating conditions for the energy systems by measuring the spatiotemporal energy performance of distributed solar PV systems at an urban scale, evolving with the changing environment, and identifying degradation based on anomalies occurrence; Only then when the physical models need to be amended. Therefore, the main goal behind this work is to advance knowledge regarding research on condition monitoring of solar systems in cities while considering issues related to anomalies like the degradation phenomenon in PV modules over time. The findings of this study have proved that the state of health and monitoring of PV systems could be successfully detected and efficiently monitored using the combination of real time monitoring and remote aerial sensing.

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.

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.

FREEDOM: Validated Method for Rapid Assessment of Incipient Faults of Aerospace Systems

Model-based fault detection and isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in flight and evaluations of numerical models of the equipment. This results in an intensive computational procedure that can be addressed only grounding the aircraft. We introduce an original methodology to sensitively accelerate FDI by reducing the computational demand to identify the health status of the aircraft. Our scheme FREEDOM (Fast Reliability Estimate and Incipient Fault Detection of Multiphysics Aerospace Systems) proposes an original combination of a novel two-step compression strategy to compute offline a synthesized representation of the dynamical response of the system and uses an inverse Bayesian optimization approach to infer online the level of damage determined by multiple fault modes affecting the equipment. We demonstrate and validate FREEDOM against numerical and physical experiments for the case of an electromechanical actuator employed for secondary flight controls. Particular attention is dedicated to simultaneous incipient mechanical and electrical faults considering different experimental settings. The outcomes validate our FDI strategy, which permits to achieve the accurate identification of complex damages outperforming the computational time of state-of-the-art algorithms by two orders of magnitude.