Model-based Fault Diagnosis Method Research Articles

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

The service environment of offshore wind turbine (OWT) support structures is harsh, and it is extremely difficult to replace these structures during their operational lifespan, making their failure a catastrophic event. The structural health monitoring (SHM) of OWT support structures is a crucial aspect of operational maintenance for OWT support structures, aiming to mitigate significant financial losses. This paper systematically summarizes the current monitoring methods and technologies for OWT support structures, including towers and foundations. Through the review of monitoring content and the evolution of monitoring techniques for supporting structures, it delves deeper into the challenges faced by wind turbine monitoring and highlights potential avenues for future development. Then, the current damage identification techniques for OWT towers and foundations are analyzed, exploring various methods including model-based, vibration-based, artificial intelligence and hybrid fault diagnosis methods. The article also examines the advantages and disadvantages of each approach and outlines potential future directions for research and development in this field. Furthermore, it delves into the current damage identification techniques for OWT towers and foundations, discussing prevalent challenges and future directions in this domain. This status review can provide reference and guidance for the monitoring design of OWT support structures, and provide support for the fault diagnosis of OWT support structures.

A Compressed Unsupervised Deep Domain Adaptation Model for Efficient Cross-Domain Fault Diagnosis

As one of the most important artificial intelligence-enabled industrial applications, fault diagnosis is vital in the safe, stable and reliable operation of the equipment. Many existing deep learning-based fault diagnosis methods assume that the distribution of training data is the same as that of testing data, which is almost impossible in practical industrial applications. In addition, most of these fault diagnosis methods are generally memory intensive and computationally expensive. A compressed unsupervised deep domain adaption model-based fault diagnosis method is proposed to overcome the above-mentioned two issues. Firstly, a standard unsupervised domain adaption model is designed to extract the features of training data and testing data, respectively. Then, the maximum mean discrepancy term is introduced to minimize the discrepancy between the extracted features of them. Next, the standard model is compressed through iteratively pruning the redundant convolutional channels. Finally, the obtained compressed model is applied to diagnose faults. The performance of the proposed method is verified on the Case Western Reserve University bearing dataset. Experimental results show that the compressed model can significantly reduce the memory occupation, computational cost and inference time compared with the standard model, but still achieve comparable or even better accuracy on ten transfer diagnostic tasks.

A Faulty Submodule Mathematical Model-Based Localization Strategy for Switch Open-Circuit Fault of Module Multilevel Converter

A faulty submodule (SM) mathematical model-based switch open-circuit fault diagnosis and localization (FDL) method for module multilevel converter (MMC) is investigated in this article, which can diagnosis the faulty switch in time. Initially, the mathematical model is established through the modified switching function of the faulty SM, which can express the real switching state of the faulty SM accurately. Based on the proposed mathematical model, an innovative FDL method for MMC is proposed. The faulty switch is located by comparing the capacitor voltage calculated through the proposed mathematical model with the sampled capacitor voltage value. To enhance the accuracy and anti-interference ability of the model, the capacitance observation method is proposed in the capacitor precharge stage. The proposed FDL method can diagnose and locate faulty switch in any state of the SM, even if the operating state of the faulty SM is the same as that of the normal SM. Single or multiple switch open-circuit faults can be accurately located by this method under either light or heavy load conditions. Finally, the mathematical model and FDL method are verified through simulation and experiment.

Model-based fault diagnosis methods for systems with stochastic process – A survey

Model-based methods are widely used for the fault diagnosis of stochastic dynamic systems by simply using the input–output relationship of the system. Despite encouraging results and progress over past decades, developing a reliable and effective diagnostic method for systems with stochastic processes remains a significant challenge. In this paper, a comprehensive review on this topic is performed. Representative main approaches and results are analyzed and compared, and applications in some specific industrial processes are briefly reviewed. The perspectives on current challenges and potential opportunities of future work are discussed, hoping to shed some light on this active topic.

Fast Detection and Localization of Multiple Leaks in Water Distribution Network Jointly Driven by Simulation and Machine Learning

The leakage control in water distribution networks (WDNs) is of high concern in the water supply industry. One direct and effective way to reduce leakage is to adopt leakage detection and localization methods to guide water utilities to repair broken pipes in time. In order to achieve higher accuracy in the leakage detection process in WDN with multiple leaks, a novel multiple leak detection and localization framework (MLDLF) based on existing pressure and flow measurements is proposed. The MLDLF decomposed the problem into three substages: model calibration, leakage identification, and leakage localization. After using the calibrated hydraulic model to predict pressure values and estimate overall leakage flow in each area in the first stage, the data-driven methods, STLK, including the seasonal and trend decomposition using loess (STL decomposition) and the k-means clustering method, were performed in the identification stage to distinguish different leakage scenarios so as to determine the occurrence time of every leakage event. Finally, combined with the stepwise model–based fault diagnosis method, leakages were located gradually with high computational efficiency. A case study of applying MLDLF to the WDN of L-Town showed that 56.52% of the leakage events were successfully identified and located with the economic score reaching €264,873, indicating the robustness and good applicability of MLDLF in identifying and localizing all types of leaks under multiple leakage scenarios.

Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network

The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Taking into consideration the issues that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for a high-pressure heater system. Firstly, an improved particle swarm optimization algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimize the particle position and velocity update process. The problem that PSO tends to fall into local optimum at the late stage of iterative optimization search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimization process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid kernel principal component analysis (KPCA)-IDEPSO-probabilistic neural network (PNN) model is proposed. Non-linear features are extracted using KPCA for fault monitoring. The IDEPSO algorithm is used to iteratively find the best PNN to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.

Fault diagnosis of SKF-6205 bearing with modified empirical mode decomposition

Rolling element bearings are broadly used in the rotating machines to support static and dynamic loads. In this research, the advance signal processing techniques are use for processing of bearing fault signals. Experimental validation with genuine vibration signals calculated from bearings with seeded defects on bearing elements. The model-based fault diagnosis method has attempted to diagnose incipient fault detection and classification of bearing with data driven approach. Feature extraction technique has been developed with hybrid signal processing technique based on the band pass filter nature of Empirical mode decomposition (EMD), the resonant frequency bands have owed in specific mono component signals called Intrinsic Mode Functions (IMFs). Synchronized resonant frequency band (SRFB) is obtained on based of orthogonal real wavelet using spectral kurtosis. Biorthogonal 5.5 wavelet, a real wavelet has been selected as a suitable wavelet for WPT based on “Maximum Relative Wavelet Energy” and “Maximum Energy-to-Shannon entropy ratio”. Three, Feature extraction techniques like continuous wavelet transform (CWT), wavelet packet transform (WPT) and modified Hilbert Huang Transforms (HHT) are compared on bases of their classification accuracy with different classification algorithm and filters. Various supervised classifiers have been compared through a common platform of Waikato Environment for Knowledge Analysis (WEKA) and concluded the k- nearest neighbour (KNN) as an effective available classifier for rolling element bearing. Further, asymmetric proximity function based KNN (APF-KNN) has out performs with modified feature selection criteria. Feature extraction through modified HHT and APFKNN has been future as a most effectual fault classification method. For testing any unknown data, simplified method has been demonstrated, which make the proposed data driven approach more realistic, faster and automated.

Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method

This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method.

Research on Application of Artificial Neural Network in Fault Diagnosis of Chemical Process

Chemical processes are usually toxic, corrosive, flammable and explosive. If the process fails, the danger is extremely high. Traditional model-based fault diagnosis methods need to establish an accurate mathematical model of the system, while modern engineering processes are usually large in scale and complex, and it is difficult to establish an accurate mathematical model. Artificial neural network has been widely used in chemical process because of its advantages of parallel processing, self-adaptation, robustness, learnability and fault tolerance. Artificial neural networks based on "deep learning" have been successfully applied to fault diagnosis in various chemical processes. This article summarizes the principle and development process of artificial neural networks, and analyzes the research progress and application status of deep neural networks in chemical process fault diagnosis based on cases. Finally, it is pointed out that deep neural network in the field of chemical process fault diagnosis is of great significance in solving the impact of less fault data and system state changes on the fault detection rate, and promoting the industrial application of fault diagnosis models.

Fault Isolation and Identification of a Four-Single-Gimbal Control Moment Gyro On-board a 3-axis Stabilized Satellite

Control moment gyros are known for their applications in attitude stabilization. These actuators are susceptible to malfunction, which results in faults and failures. Therefore, diagnosing the faults can improve the reliability of completing a mission while reducing maintenance costs. Thus, a model-based fault diagnosis method is proposed here. The intended algorithm is an enhanced version of previous work by the author. The enhancement employs a condensed approach to alleviate the delay caused by the filter’s confidence in its estimations. A case-study on a closed-loop controlled satellite is provided along with an extensive Monte Carlo simulation to evaluate the proposed method’s performance. The results show that the enhanced method can achieve superior performance while requiring less computational resources by eliminating extra grid search loops.

The Post-Fault Current Model of Voltage Source Converter and Its Application in Fault Diagnosis

The open-switch fault of voltage source converters (VSC) may change the switching state and distort the current, which is the basis for model-based fault diagnosis methods. Based on the analysis of the switching state, this letter presents a post-fault current model to estimate the current of each phase after one open-switch fault occurs. Combined with the current model-based method, the proposed post-fault model can be used not only to check the accuracy of the diagnosis result but also to detect multiple open-switch faults. The current, voltage, and driving signals used in this model are all from the VSC control system, therefore, this model can be easily implemented without additional sensors or hardware. Finally, the effectiveness of the proposed model is verified by the experimental results.

Robust Fault Diagnosis for Adaptive Structures With Unknown Stochastic Disturbances

Adaptive structures can react to environmental impacts and can significantly improve the load-bearing behavior of, e.g., building structures. For building structures, reliability and safety are major concerns, and thus, a fault diagnosis system, detecting and isolating actuator and sensor faults, is necessary. Adaptive building structures are exposed and adapt to unknown, stochastic wind and surface loads that are difficult to model and can therefore not be considered in model-based fault diagnosis methods, which decreases their performance. For this reason, this article combines the model-based method of parity equation with the data-based method of principal component analysis (PCA) for the fault diagnosis in adaptive structures. The proposed approach characterizes the unknown disturbance in the residual data derived by parity equations using PCA. PCA determines orthogonal directions of decreasing variance in the residual which are decoupled to decrease the sensitivity of the residual to disturbances. Since decoupling of residual directions influences the diagnosability of faults, the number of decoupled principal components is determined such that the diagnosability is not significantly affected. The diagnosability is analyzed by the Kullback–Leibler divergence based on the system model and disturbance characterization. The resulting decoupled residual significantly improves the fault detection performance using a cumulative sum algorithm for change detection. Moreover, the fault input matrix of the system model and the knowledge of the fault time profile yield reliable fault isolation using a Bayes classifier as illustrated in a simulation study.

Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets

Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.

Modeling of HVAC Systems for Fault Diagnosis

Analytical model based fault diagnosis method is seldom adopted for heating, ventilating and air conditioning (HVAC) systems as it is rather difficult to establish and verify an explicit model for HVAC systems. On the other hand, a flexible graphically based software called TRNSYS has been widely accepted in the design and optimization of HVAC systems recently. It makes sense to use TRNSYS to establish and verify an analytical model for fault diagnosis of HVAC systems. In this paper, a TRNSYS model of a HVAC system in multi-zone buildings is firstly established on the basis of practical data. Then, a state-space model of HVAC system is given according to the thermal-mass balance and air flow balance equations, and the consistency between the TRNSYS model and the state-space model is demonstrated. Also, some typical faults in the HVAC system are considered and the consistency between the two models under the faulty cases is verified again. Further, the effects of the faults on energy consumption is analyzed. The results show that some faults such as pipe scaling can significantly increase energy consumption while having no obvious change in room temperature. Based on the established models, some promising and challenging research directions for fault diagnosis of HVAC systems are finally discussed.

Improved Process Diagnosis Using Fault Contribution Plots from Sparse Autoencoders

Development of model-based fault diagnosis methods is a challenge when industrial systems are large and exhibit complex process behavior. Latent projection (LP), a statistical method that extract features of data via dimensionality reduction, is an alternative approach to diagnosis as it can be formulated to not rely on process knowledge. However, LP methods may perform poorly at identifying abnormal process variables due a “fault smearing” effect -variables unaffected by a fault are unintentionally characterized as being abnormal. The effect occurs because data compression permits faulty and non-faulty variables to interact. This paper presents an autoencoder (AE), a nonlinear LP method based on neural networks, as a monitoring method of a simulated nonlinear triple tank process (TTP). Simulated process data was used to train the AE to generate a monitoring statistic representing the condition of the TTP. Sparsity was introduced in the AE to reduce variable interactivity. The AE’s ability to detect a fault was demonstrated. The individual contributions of process variables to the AE’s monitoring statistic were analyzed to reveal the process variables that were no longer consistent with normal operating conditions. The key result in this study was that sparsity reduced fault smearing onto unaffected variables and increased the contributions of actual faulty variables.

Model based fault diagnosis for drillstring washout using iterated unscented Kalman filter

In this study, a model based fault diagnosis method for drillstring washout is proposed, which uses iterated unscented Kalman filter (IUKF) to detect the emergence of drillstring washout and to estimate washout depth and washout rate. To quantify the changes in pressure-loss and annulus outlet flow rate resulting from drillstring washout, pressure-loss factors and flow rate factor are introduced into the governing equations of circulating drilling fluid under normal drilling condition. Pressure-loss factors and flow rate factor are estimated by IUKF with updated measurements, and the emergence of drillstring washout is automatically detected by confirming the changes in pressure-loss factors and flow rate factor using generalized likelihood ratio test (GLRT). After drillstring washout is detected, continuously updated pressure and flow rate measurements are sent to the identification model of drillstring washout to estimate washout depth and washout rate by IUKF. In addition, the performances of fault diagnosis method for drillstring washout using IUKF and unscented Kalman filter (UKF) are contrasted.Numerical simulation indicates that IUKF and UKF have equivalent performance in drillstring washout detection, while IUKF shows a better performance in washout depth estimation. When using IUKF, the average relative errors of estimated washout depth and washout rate are 2.1% and 1.5% respectively. The errors of estimated washout depth and washout rate are 2.8% and 1.49% when using UKF. Robustness analysis indicates that IUKF is more robust than UKF to measurement noise. IUKF and UKF both have the ability to estimate washout depth and washout rate within the noise scope of 0%–0.8%, while IUKF is more accurate than UKF for washout depth estimation.

Model-based fault diagnosis of Lithium-ion battery using strong tracking Extended Kalman Filter

The failure of battery could lead to serious inconvenience, performance deterioration, accelerated aging and costly maintenance. It is imperative to design a reliable and robust fault detection scheme to detect the underlying faults and improper operation. In this paper, a model based fault diagnosis method is proposed based on multiple equivalent circuit models (ECMs) and strong tracking extended Kalman filter (ST-EKF), where the multiple ECMs are designed to capture different failure modes. The ST-EKF is utilized to estimate the terminal voltages and generate residual signals online. Simulation experiments are used to validate the effectiveness of the proposed algorithm in detecting multiple battery fault scenarios.

Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles

The condition monitoring and fault diagnosis of the lithium-ion battery system are crucial issues for electric vehicles. The shocks, blows, twists, and vibrations during the electric vehicle driving process may cause the insulation fault. In order to ensure the safety of the drivers and passengers, a real-time monitor to detect the insulation state between the high voltage and ground is required. However, the conventional battery management system only provides very simple and coarse-grained measurements to detect the insulation resistance. In this work, a model-based insulation fault diagnosis method is proposed. Firstly, the equivalent circuit model for insulation fault diagnosis is established using a high-fidelity cell model. Then, the recursive least-squares method is employed to identify the model parameters. Considering the system nonlinear properties, measurement noise and unknown disturbance, the Kalman filter based state observer is designed for joint estimation of both the battery voltage and state-of-charge using the identified battery model. Finally, the positive and negative virtual insulation resistance are quantitatively assessed based on the prediction results of the state observer. Experiments under different loading profiles are performed to verify the proposed method.

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors.

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

A simulation model based fault diagnosis method for bearings

A simulation model based fault diagnosis method for bearings