Development Of Fault Diagnosis Research Articles

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

Sensor Fault Detection and Diagnosis of Linear Parabolic PDE Systems With Unknown Inputs

This paper investigates the sensor bias fault detection and diagnosis problem for linear parabolic partial differential equation (PDE) systems under the existence of unknown input signals. A variation of Wirtinger's inequality is used to design a Luenberger-type PDE observer and radial basis function (RBF) neural networks are applied to approximate the unknown inputs, guaranteeing the boundedness of the state estimation error. Taking the measurement error as the fault indication signal, a residual evaluation logic is developed to achieve fault detection. An adaptive diagnostic law is constructed to estimate the values of sensor bias faults once they are detected. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -modification RBF neural networks are designed to compensate for the unknown inputs in the presence of sensor faults, ensuring that both state and sensor bias faults estimation errors converge to some adjustable sets. The effectiveness and applicability of the developed fault diagnosis strategy are demonstrated by simulation results on a heat diffusion process.

Thermal Image Analysis of Photovoltaic Panel for Condition Monitoring Using Hybrid Thermal Pixel Counting Algorithm and XGBoost Classifier

The use of thermal infrared imaging and the advancement of image processing techniques have developed fault diagnosis of Photo Voltaic (PV) panels without the need to incorporate expensive electrical detection circuitry. PV thermal image processing, supports in continuous monitoring of panels without disturbing the continuity of operation and mitigates the problem of manual inspection involved over a large area. The thermal pictures of photo voltaic panels are used to identify the nature of faults such as bypass diode, cell under fault condition, circuit which is under open condition, hot spot, polarization, Cracking, Corrosion, discoloring and delamination. An automatic PV Computer Aided Diagnosis (CAD) based condition monitoring systems with thermal image analysis is developed to identify and classify the different fault conditions such as Hotspot, Ethylene Vinyl Acetate (EVA) Discoloration, Delamination and Open Circuit. The proposed system involves a hybrid thermal pixel counting algorithm to identify the thermal patterns. The Extreme Gradient Boosting algorithm (XGBoost) is used to detect the numerical characteristics of the captured image thermal variations to support in classification. The experimental results show that the proposed Hybrid Thermal Pixel Counting (HTPC) Algorithm with XGBOOST Classifier gives better detection accuracy with improved computational speed.

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Abstract With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.

Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine

Wind turbines play a crucial role in renewable energy generation systems and are frequently exposed to challenging operational environments. Monitoring and diagnosing potential faults during their operation is essential for improving reliability and reducing maintenance costs. Supervised learning using data-driven techniques, particularly deep learning, offers a viable approach for developing fault diagnosis models. However, a significant challenge in practical wind power equipment lies in the scarcity of annotated samples required to train these models effectively. This paper proposes a semi-supervised fault diagnosis approach specifically designed for wind turbines, aiming to address this challenge. Initially, a semi-supervised deep neural network is constructed using adversarial learning, where a limited set of annotated samples is used in conjunction with a vast amount of unannotated samples. The health status features present in the unannotated samples are leveraged to capture a generalized representation of the underlying features. Subsequently, a metric learning-guided discriminative features enhancement technique is employed to improve the separability of different manifolds, thereby enhancing the performance of the semi-supervised training process. By employing this methodology, it becomes possible to develop a fault diagnosis model with superior accuracy using only a limited amount of annotated samples. Comprehensive fault diagnosis experiments were conducted on a wind turbine fault dataset, revealing the efficacy and superiority of the presented methodology.

Fault diagnosis of rolling bearing based on BP neural network with fractional order gradient descent

The health of rolling bearing is of great importance for the normal operation of rotating machinery. The fault diagnosis process can be roughly summarized as signal processing, feature extraction, and fault classification. In this paper, a novel feature extraction and fault diagnosis method with fractional order back-propagation neural network is put forward. The new sine cosine algorithm optimized variational mode decomposition is performed on vibration signals, and the fault feature vectors are selected and built by singular value decomposition. Inspired by the fractional order calculus, a fractional order back-propagation neural network is employed to realize fault classification. The capability of the developed fault diagnosis algorithm is comprehensively evaluated via benchmark bearing data. The experimental results demonstrate that the designed method substantially extracts bearing defect features, increases classification accuracy and efficiency, and also outperforms existing algorithms.

고장 데이터 부재 및 부족 상황에서의 딥러닝 기반 기계시스템의 고장진단 방법론

Deep learning-based fault diagnosis systems for prognostics and health management of mechanical systems is an active research topic. Notably, the absence and class imbalance of fault data (insufficient fault data compared to normal data) have been shown to cause many challenges in developing fault diagnosis systems for the manufacturing fields. Therefore, this paper presents case studies using deep learning algorithms in the absence or class imbalance of fault data. Auto-encoder-based anomaly detection method, which can be used when fault data is absent, was applied to diagnose faults in a robotic spot welding process. The anomaly detection threshold was set based on the reconstruction error of trained normal data and the confidence level of the distribution of normal data. The anomaly detection performance of the auto-encoder was verified using non-trained normal data and three sets of fault data through the threshold. As a case study for insufficient fault data, synthetic data was generated based on cGAN and applied to diagnose fault of bearing. Using the imbalanced dataset to generate synthetic fault data and to reduce the imbalance ratio, it was confirmed that the accuracy of the synthetic data generation-based 2DCNN fault diagnosis model was improved.

Open-Circuit Fault Diagnosis and Tolerant Control for 2/3-Level DAB Converters

A two-to-three (2/3)-level dual-active-bridge (DAB) converter is a promising dc–dc converter for medium- and high-voltage applications. That is due to its advantages like high power density, galvanic isolation, and capability of withstanding higher voltage ratings compared to the two-level DAB converters. However, the open-circuit fault (OCF) is critical for the 2/3-level DAB converters, resulting in various negative effects, e.g., dc bias, overshoot current, and capacitor voltage imbalance. To address these issues, it is necessary to develop fault diagnosis and fault-tolerant control strategies. This article, thus, proposes a method to identify the faulty switch based on the dynamic characteristics when the OCF occurs. The midpoint voltages of the neutral-point-clamped bridge arms are employed as the fault diagnosis signals, where the faulty switch can be identified accurately based on the mean values and duty cycles of the midpoint voltages. Subsequently, a fault-tolerant control scheme based on a complementary-switch-blocking method is proposed. In this scheme, when the OCF occurs on one switch, the gate-driving signal of its complementary switch is blocked, and the OCF negative effects, e.g., dc bias, overshoot current, and unbalanced capacitor voltages, can be alleviated significantly. Furthermore, the power transfer capability of the 2/3-level DAB converters is enhanced with the proposed scheme compared to the traditional bypass-arm method. Finally, experimental tests are carried out to verify the theoretical analysis.

Fault data generation of lithium ion batteries based on digital twin: A case for internal short circuit

Battery accidents are emerging for various kinds of applications, such as commercial electronics, electric scooters, electric vehicle and energy storage stations. Effective early warning algorithm is essential for mitigating the damage caused by battery failure. An early warning algorithm needs to be trained by failure data. However, building battery failure data (such as internal short circuit) by experiment consumes so much time and cost that it cannot match the development requirement of new product. This work demonstrates the validity of using digital twin technique to generate battery failure data to train the online early warning algorithm. The digital twin is formed by a highly reliable thermal-electrical coupled battery model. The influential factors, e.g., load current, short circuit position, equivalent short resistance and environment temperature, which determine the performance of the early warning algorithm were discussed. The warning threshold that may judge the fault warning rate and response speed of the algorithm was also analyzed. The digital twin helps us find that there is a temperature difference between the sensor location and the failure (short circuit) point. The temperature difference enlarges under the cases that the failure point is far from the measurement point, thereby hindering the timely detection and alarm for fault. The temperature gap can be as high as 261.2 °C under the extreme case of 0.1 Ω short circuit resistance. Therefore, the mismatch of the failure point and the measurement will give us the illusion that the failure occurs suddenly, or the so-called sudden death. The thermal runaway boundary for battery module varies with the ambient temperature and the short resistance, helping the determination of the threshold of the detection algorithm for thermal runaway. At last, a guidance on how to develop fault diagnosis algorithm with the help of digital twin to judgement of fault diagnosis threshold is summarized, with clear discussion of the accuracy, cost and efficiency of the method. The proposed method paves a way for the application of the digital twin on the fast development of battery management algorithms.

Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN) improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert experience and knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models; on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast running speed on the premise of sufficient output precision, and the local optimization problem of K-means clustering algorithm is well suppressed by AGA group calculation; this also ensures the performance of RBFNN; on the other hand, KPCA is employed to reduce the dimension of the model input data, which not only effectively extract the nonlinear features, but also further simplify the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages.

A novel wind turbine fault diagnosis based on deep transfer learning of improved residual network and multi-target data

In industrial production, problems such as lack of data, complex fault types, and low generalizability of deep learning models seriously affect the fault diagnosis of wind turbines. Therefore, we have developed a fault diagnosis model for wind turbines under harsh conditions to address the above problems. First, the collected one-dimensional vibration data is reshaped into two-dimensional form by using the Gramian Angular Field. The two-dimensional form not only extends the spatial structure of the data, but also effectively improves the information expression of the data. In addition, the data is classified into large-scale data, medium-scale data, small-scale data, class-imbalanced data, and heterogeneous data based on the data type. Then, the deep residual network structure is redesigned to improve the diagnostic performance of the model based on the sensitivity of the reshaped data to the size of the convolutional kernel, and the new structure of the network is employed to implement transfer learning. Finally, we adopt the developed fault diagnosis model to achieve the fault diagnosis of bearings and gears in the wind turbine gearbox. Meanwhile, an automatic hyperparameter search mechanism was added to improve the partial hyperparameter optimization in this study. It is demonstrated that the model proposed in this study has excellent diagnostic performance with multi-target data for wind turbines, and has excellent generalizability and reliability.

Fault Diagnosis under Varying Working Conditions with Domain Adversarial Capsule Networks

&lt;p&gt;Most existing studies that develop fault diagnosis methods focus on performance under steady operation while overlooking adaptability under varying working conditions. This results in the low generalization of the fault diagnosis methods. In this study, a novel deep transfer learning architecture is proposed for fault diagnosis under varying working conditions. A modified capsule network is developed by combining the domain adversarial framework and classical capsule network to simultaneously recognize the machinery fault and working conditions. The novelty of the proposed architecture mainly lies in the integration of the domain adversarial mechanism and capsule network. The idea of the domain adversarial mechanism is exploited in transfer learning, which can achieve a promising performance in cross-condition fault diagnosis tasks. With the novel architecture, learned features exhibit identical or very similar distributions in the source and target domains. Hence, the deep learning architecture trained in one working condition can be applicable to discriminative conditions without being hindered by the shift between the two domains. The proposed method is applied to analyze vibrations of a bearing system acquired under different working conditions, i.e., loads and rolling speed. The experimental results indicate that the proposed method outperforms other state-of-the-art methods in fault diagnosis under varying working conditions.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Hardware-In-The-Loop Assessment of Fuzzy and Neural Network Fault Diagnosis Schemes for a Wind Turbine Model

The fault diagnosis of wind turbines includes extremely challenging aspects that motivate the research issues considered in this paper. In particular, this work studies fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis techniques that exploits the estimation of the fault by means of data-driven approaches. These fuzzy and neural network structures are integrated with auto-regressive with exogenous input regressors, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of fault diagnosis schemes are validated by using a real-time simulator of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. This realistic simulator relies on a hardware-in-the-loop tool that is finally implemented for verifying and validating the performance of the developed fault diagnosis strategies in an actual environment.

Research on the Application of XML in Fault Diagnosis IETM

AbstractFault diagnosis IETM is a digital technical manual which integrates editing, management, display and publication and faces the field of fault diagnosis. In its development process, there are some problems such as complex data management and difficult interaction. The successful application of XML technology becomes the key to solve these problems. In this paper, based on S1000D IETM development standard, the main characteristics of XML and its related technologies are first introduced, and then the application of XML and its related technologies in fault diagnosis IETM is analyzed and studied, including the creation of fault information data module, the display control of fault information and the comprehensive management of data. A conversion method between XML document data and database data is proposed, and the design process of fault diagnosis IETM based on XML is discussed. Finally, a fault diagnosis IETM display system based on XML is preliminarily developed by combining the related technologies mentioned above. Experiments show that XML technology can effectively manage the information in fault diagnosis IETM and display it to users in an interactive way, which has important theoretical value and certain practical significance for developing fault diagnosis IETM and exploring the deeper application of XML in IETM.

Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines

Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.

Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.

Image representation of vibration signals and its application in intelligent compound fault diagnosis in railway vehicle wheelset-axlebox assemblies

Axlebox vibration signals contain critical status information regarding the operating state of a wheelset-axlebox assembly in railway vehicles, and an increasing number of studies have demonstrated the superiority of operating state diagnostic methods based on machine learning that apply image representations of vibration signals as input data. In this regard, the Markov transition field algorithm has been demonstrated to represent the state features of a time series efficiently. However, the frequency spectrum of axlebox vibrations has been shown to provide superior performance in these diagnostic methods. Unfortunately, the conventional Markov transition field algorithm is not applicable to the frequency domain. The present study addresses this problem by proposing a novel spectral Markov transition field (SMTF) algorithm to represent the frequency spectrum characteristics of vibration signals in image form through the construction of the first-order Markov transition matrix of the frequency-domain signal. The proposed algorithm employs no parameters requiring prior knowledge. Moreover, the machine learning network is efficiently trained to recognize specific features related to compound faults using the transfer learning technique, which enables the network to obtain highly separable and intelligent image classification results under small sample conditions. The performance of the SMTF algorithm is evaluated experimentally using a scaled test rig to collect vibration data for three types of artificial wheelset-axlebox faults under three different velocities. In addition, three other state-of-the-art image representation algorithms are employed for comparison. The results demonstrate that the proposed SMTF algorithm effectively represents compound fault features comprehensively, and the developed fault diagnosis framework based on the SMTF representations obtains superior classification results with much greater separability under all three velocity conditions. The present study represents a foundation for developing intelligent condition monitoring for other rotating machinery.

Validation and Refinement of a Steering Friction Increase Detection Algorithm Using Test Drive Data

The Electric Power Steering (EPS) System provides steering assist in conventional vehicle driving and is the main actuator for vehicle lateral control in active safety features. While the driver can sometimes compensate for reduced or loss of steering assist caused by EPS mechanical or electrical degradations, it may become very difficult to steer for larger vehicles. Furthermore, active safety functions cannot control the vehicle effectively for lateral motions without a healthy EPS system. Hence, comprehensive EPS system fault monitoring is essential for the next generation of vehicles. Previous works have utilized computer simulation and hardware-in-the-loop experiments to develop fault diagnosis and prognosis algorithms for electrical and mechanical failures in EPS systems. Using test drive data collected, this paper validates and refines a previously developed algorithm designed for detecting increases in EPS system internal mechanical friction. The data include 215 minutes of natural driving with different speeds and steering maneuvers. Noise factors such as tire type and level of friction introduced are also considered. The previous algorithm is refined to enhance performance addressing issues of time delays and parameter uncertainty specific to the previous model-based algorithm. Specifically, a Kalman filter-based joint state-parameter estimator that uses a simplified vehicle dynamic model is developed to provide a direct estimate of steering friction increase. Data collected from test drives indicate that the refined algorithm can robustly indicate a friction increase before an average human driver notices a difference in steering feel.

Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell

One of the barriers for commercialization of polymer electrolyte membrane fuel cell system is its reliability. One good solution for reliability improvement is to develop fault diagnosis model. This study suggests a diagnostic method for polymer electrolyte membrane fuel cell thermal management system. With the increase of fuel cell stack power these days, waste heat from the stack also increased and thereby thermal management system is also receiving attention. In this study, thermal management system fault experiments are repeatedly performed under various loads and stack degradation conditions. With the degradation of stack, waste heat from the stack increases and fault response as well as its impact on the system varies. The diagnosis model developed in this study detects faults on component-level and diagnoses severity of the faulty components. Furthermore, the diagnosis model is applicable to fuel cell system with degraded stack. In the process of model development, single-task learning technique is applied to neural network diagnosis model for higher diagnostic accuracy and compared with conventional multi-task learning technique.

Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

In this paper, a novel multiple-fault diagnosis (MFD) scheme using radial basis function neural network (RBFNN)-based observers is presented for a spacecraft attitude control system (ACS) in the presence of external disturbances and nonlinear uncertainties. Based on dynamic and kinematic models, robust fault detection observers (FDOs) are designed to detect the simultaneous occurrence of actuator, gyro, and star sensor faults. Then, a series of RBFNN-based fault isolation observers (FIOs) are designed to decouple the faults of different components completely. This complete decoupling will guarantee that the diagnosis result of one component is not affected by the faults of other components; thus, multiple faults can be diagnosed simultaneously. To improve the accuracy of fault detection and reconstruction, disturbance compensation observers (DCOs) based on the RBFNN are also designed to compensate for the external disturbances. It is worth noting that the developed fault diagnosis scheme can be used to detect and isolate small faults. Finally, simulation results are presented to show the effectiveness and feasibility of the proposed method.