Complete Fault Research Articles

Fault Detection and Zonal Protection Strategy of Multi-Voltage Level DC Grid Based on Fault Traveling Wave Characteristic Extraction

Currently, DC breakers are commonly used in mainstream protection schemes for DC grids to eliminate faults. However, the cost of high voltage DC (HVDC) breakers is high, and equipping each DC line with DC breakers is expensive. In order to minimize the number of DC breakers while ensuring the reliability of the power supply, a zonal protection strategy suitable for multi-voltage level DC grids is proposed. Subsequently, the qualitative impact of partial power interruption caused by fault DC areas is analyzed in the system. The basic zonal principle of the multi-voltage level DC grid is formulated, taking into account unbalanced power, the mode of system-level control, and the type of converters. Additionally, a time sequence coordination strategy is derived in detail based on the characteristics of DC breakers, AC/DC converters, DC/DC converters, AC breakers, high-speed switches, and other fault removal components. Finally, a seven-terminal DC grid is modeled in the PSCAD/EMTDC simulation platform. According to the simulation analysis, the DC grid can adopt the converter with fault clearing ability or an AC circuit breaker to cooperate with the fast disconnector (FD) to complete fault clearing in the DC fault area under the proposed zonal protection coordination strategy.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis

The application of transfer learning to effectively identify rolling bearing fault has been attracting much attention. Most of the current studies are based on single-source domain or multi-source domains constructed from different working conditions of the same machine. However, in practical scenarios, it is common to obtain multiple source domains from different machines, which brings new challenges to how to use these source domains to complete fault diagnosis. To solve the issue, a conditional distribution-guided adversarial transfer learning network with multi-source domains (CDGATLN) is developed for fault diagnosis of bearing installed on different machines. Firstly, the knowledge of multi-source domains from different machines is transferred to the single target domain by decreasing data distribution discrepancy between each source domain and target domain. Then, a conditional distribution-guided alignment strategy is introduced to decrease conditional distribution discrepancy and calculate the importance per source domain based on the conditional distribution discrepancy, so as to promote the knowledge transfer of each source domain. Finally, a monotone importance specification mechanism is constructed to constrain each importance to ensure that the source domain with low importance will not be discarded, which enables the knowledge of each source domain to participate in the construction of the model. Extensive experimental results verify the effectiveness and superiority of CDGATLN.

Simulation-Driven Subdomain Adaptation Network for bearing fault diagnosis with missing samples

Deep learning has been employed widely in bearing fault diagnosis. However, in industry, sufficient and complete fault samples are hard to acquire and the working conditions vary frequently, which result in significant diagnosis performance deterioration. To solve these problems, a fully dynamic model and subdomain adaptation based intelligent fault diagnosis framework is proposed. Firstly, the fully dynamic bearing models are used to fill in some missing fault samples. Then, a simple but effective subdomain adaptation method is developed to align class-level subdomain distributions of source and target domains with consideration of samples transferability. Contrast experiments on two bearing test rigs are carried out to verify the proposed method, which contain different working conditions and missing fault types. Experimental results indicate that the proposed method is superior to non-adapted and four widely used transfer learning models, which is promising for real industrial applications.

Fault diagnosis for rotor based on multi-sensor information and progressive strategies

Fault diagnosis is an effective tool to ensure safe operation of machinery and avoid serious accidents. As most currently used fault diagnosis methods usually employ mapping relationship established by training samples and their labels to achieve classification of testing samples, it is difficult for them to achieve fault diagnosis under the condition of incomplete training sample types. In addition, previous studies usually focus on feature extraction of single-channel vibration signal, which cannot get complete fault feature information. To solve the above problems, a progressive fault diagnosis method is investigated in this paper. First, the preliminary fault detection for the rotor is performed by studying reconstruction error of a sparse auto-encoder. Second, if a fault exists in the rotor, the outlier detection is implemented by the support vector data description method. Finally, if there are no outlier samples, the well -trained support vector machine is used to confirm the type of fault samples and complete the diagnosis. The performance of the proposed method was verified using the data obtained from a rotor laboratory bench. The types of rotor states investigated include normal, contact-rubbing, unbalance and misalignment. The experimental results verify the effectiveness and superiority of the proposed method in reducing the incidents of fault omission and fault misunderstanding.

Fault detection and isolation for a secondary loop refrigeration system

Secondary loop refrigeration systems offer advantages over conventional system architectures: refrigerant charge reduction, maintenance simplification, and reduced safety hazards. Currently, no model-based fault detection for a secondary loop system has been reported. This article presents a fault detection and isolation scheme for a secondary loop refrigeration system where parametric faults (dirt/ice-buildup and secondary loop pump failure) and sensor faults (four temperature sensors in the secondary loop) are detected and isolated. The fault detection and isolation system is based on unknown input observers and on extended Kalman filters. The models are obtained through physical relations combined with experimental parameter determination (gray-box model) and are cross-validated against measurement data. The complete fault detection and isolation architecture is experimentally validated on a secondary loop refrigeration system mounted on a cooling box inside a climate chamber. Experimental results show that important parametric faults can be reliably detected and isolated, and the fault detection and isolation of sensor faults are effective.

Some classes of easily testable circuits in the Zhegalkin basis

Abstract We identify the classes of Boolean functions that may be implemented by easily testable circuits in the Zhegalkin basis for constant type-1 faults on outputs of gates. An upper estimate for the length of a complete fault detection test for three-place functions is obtained.

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

Early fault diagnosis of rotating machinery based on composite zoom permutation entropy

• Composite zoom permutation entropy (CZPE) is proposed to extract features. • The mechanism of the composite analysis is provided to reveal the merit of CZPE. • A diagnosis framework is developed to identify early faults of rotating machinery. • The proposed method is verified by simulational and experimental results. Fault diagnosis of rotating machinery serves an important role in informing system operation and predictive maintenance decisions. To quantify the fault information from vibrational signals, the multiscale permutation entropy has become a promising tool for fault diagnosis of rotating machinery. However, multiscale permutation entropy fails to extract weak features of early faults because it can hardly capture the tiny oscillation patterns of signals over the full frequency band. To address this issue, this paper presents an effective feature extraction method called composite zoom permutation entropy. First, composite zoom permutation entropy employs multiple wavelets to capture complete fault features with multiple resolutions over the full frequency band. Then the composite analysis is performed to improve the separability of extracted features for identifying different early faults. Based on composite zoom permutation entropy, a diagnosis framework has been developed to improve the operational reliability of rotating machinery by identifying faults as early as possible. The simulation results show that composite zoom permutation entropy has better extraction ability compared with other permutation entropy based methods. The experimental results show that the proposed method outperforms existing methods in identifying early faults of rotating machinery.

A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE)

Fault detection is an important and demanding problem in industry. Recently, many researchers have addressed the use of deep learning architectures for fault detection applications such as an autoencoder. Traditional methods based on an autoencoder usually complete fault detection by comparing reconstruction errors, and ignore a lot of useful information about the distribution of latent variables. To deal with this problem, this paper proposes a novel unsupervised fault detection method named one-dimension convolutional adversarial autoencoder (1DAAE), which introduces two new ideas: one-dimension convolution layers for the encoder to obtain better features and the adversarial thought to impose the latent variable z to cluster into a prior distribution. The proposed method not only has powerful feature representation ability than the traditional autoencoder, but has also enhanced the discrimination ability by imposing a prior distribution of the latent variables to cluster. Then, two anomaly scores for 1DAAE were proposed to detect fault samples, one based on reconstruction errors, and the other based on latent variable distribution. Finally, it was shown by the experiments that the proposed method outperformed the autoencoder-based, adversarial autoencoder-based, one-dimension convolutional autoencoder-based and generative adversarial network-based algorithms on the Tennessee Eastman process. Through the experiments, we found that the both one-dimension convolution layers and the latent vector distribution are helpful for fault detection.

Smart multichannel mode extraction for enhanced bearing fault diagnosis

In bearing fault diagnosis, multichannel data can contain more abundant and complete fault information to alleviate the influence of accidental factors in a single channel. To fully employ the fault information concealed in the multichannel data, this paper proposes a smart multichannel mode extraction (SMME) for enhanced bearing fault diagnosis. The SMME method based on multivariate variational mode decomposition (MVMD) and manifold learning overcomes the problems of predefined model parameters in MVMD and shows good performance in mining the intrinsic nonlinear and nonstationary features from multichannel modes of different quality. First, inspired by the convergence property of MVMD, a smart multichannel spectral structure scanner with solid mathematical theory is constructed to adaptively detect the latent center frequencies (CFs) in the multichannel bearing signals without prior knowledge. Second, multichannel single-step decomposition induced by the detected CFs is established to obtain corresponding multichannel modes through only single-step calculation instead of considerable iterations. Third, a fault feature enhancement strategy is designed for locating and fusing the aligned multichannel sensitive modes with different qualities of fault information to highlight the inherent fault features. The superiority of the SMME method for enhanced bearing fault diagnosis in effectiveness and efficiency is proven through simulation and two experiments.

Flotation Process Fault Detection and Isolation using Neural ODE for generation of vector-field features

Flotation in the mining industry is of vital importance for obtaining the right quality of product with efficiency and represents a critical process where possible failures must be monitored at all times. In this paper, complete fault detection and isolation system (FDI) based on the Neural Ordinary Differential Equations (NODE) framework is proposed; the NODE is employed to represent the dynamics of the studied plant based on the measured variables and inputs. Then, a classifier can be used to identify the faults based on the projections of the derivatives or local vector field generated by the NODE using the estimations and actual measurements. The proposed approach is applied to a controlled mining flotation process that has perturbations. The solution is compared with other known machine learning techniques showing better performance metrics. Moreover, it is demonstrated with t-SNE representation that features generated from the NODE model improve the classification.

Adaptive scale decomposition and weighted multikernel correntropy for wheelset axle box bearing diagnosis under impact interference

In this paper, a novel diagnosis method based on adaptive scale decomposition and weighted multikernel correntropy (ASD-WMKC) is developed to diagnose the faults of wheelset axle box bearing under strong wheel-rail impact interference and background noise. The ASD method is utilized to extract the complete fault characteristic signal of axle box bearing, in which the CHIMDO method is applied to restrict the overshoot and undershoot problems, an optimal shape approximation extension method is developed to suppress the end effects, and an adaptive decomposition scale construction method is proposed to overcome the mode mixing and splitting problems of decomposition. The WMKC is applied to enhance the weak fault characteristic signal and suppress the impact interferences, in which the multikernel correntropy combined with a 2D Gaussian kernel is utilized to overcome the defects of weak adaptability and poor anti-interference ability of correntropy, respectively. Simulation and application studies show that the proposed ASD-WMKC method outperforms the comparative methods and can extract the fault characteristic signal of axle box bearing with the highest completeness and SNR under strong wheel-rail impact interference and background noise.

Improving Efficiency of Angular Velocity Sensors on Aircraft

With the development of science and technology, intelligent systems on aircraft help users know the device's operating status in real time. Using smart devices shortens the time for maintenance, repair, and operation of ground equipment and aircraft equipment. Therefore, building devices capable of self-diagnosis and warning failure are essential in aeronautical engineering. In many published studies, the authors often use the foundation of classic algorithms such as genetics, neural networks, and AI to solve the problem of identification and troubleshoot some simple devices. In Vietnam, there are currently not many published studies on failure diagnosis in aviation engineering, so the author's research has built the foundation for developing studies on fault diagnosis crash in the future. The primary purpose of the research is to create a complete automatic fault diagnosis and repair system for a specific class of inductance (angular speed sensor). The algorithms proposed in the paper are simulated on Matlab Simulink software, which will prove the feasibility of the proposed algorithm. In future studies, the author will apply new algorithms to build more complex fault diagnosis systems for other objects on the flying device.

A fault-tolerant sensor fusion in mobile robots using multiple model Kalman filters

Accurate localization is crucial in the navigation of mobile robots. However, in other circumstances, single-sensor localization faces different challenges, including software and hardware problems or data outages. Sensor fusion is used in most autonomous vehicles (including aerial and ground vehicles) to overcome such challenges. In this paper, the localization of a mobile robot is studied in the presence of sensor faults. The mobile robot has two sensors: two Inertial Measurement Units (IMU) and wheel encoders. Regarding the fault-tolerant scheme, measurements of both sets of sensors are fused using an Interacting Multiple Model (IMM) Kalman filter based on both unscented and extended Kalman filters (UKF and EKF). UKF and EKF-based IMM are chosen for this study since the dynamic model of the localization is highly nonlinear. Regarding contributions, it should be noted that this scheme eliminates the need to model every single fault scenario and use an additional sensor to oversee the performance of the sensing system. Also, comparing this method with similar approaches adopted by other studies shows better performance regarding the cost of computations and RMSE. To evaluate performance, the outputs of the proposed filters are simulated and compared for different trajectories where the data of each sensor is intentionally corrupted to observe the fault detection capability. Simulations are performed for different trajectories and noises to demonstrate this method’s efficiency in different situations. In addition, the results of unscented and extended Kalman filter-based IMM are compared in terms of error and computational costs to evaluate their performance. Overall, simulation and experiments indicate accurate 3D estimations in all cases. Moreover, designated weights vividly show that sensor fault detection is achieved by both unscented and extended IMM Kalman filters, which enable complete fault isolation consequently. This approach provides mobile robots with a reliable and straightforward sensor fault detection and localization solution.

Conditional Joint Distribution-Based Test Selection for Fault Detection and Isolation.

Data-driven fault detection and isolation (FDI) depends on complete, comprehensive, and accurate fault information. Optimal test selection can substantially improve information achievement for FDI and reduce the detecting cost and the maintenance cost of the engineering systems. Considerable efforts have been worked to model the test selection problem (TSP), but few of them considered the impact of the measurement uncertainty and the fault occurrence. In this article, a conditional joint distribution (CJD)-based test selection method is proposed to construct an accurate TSP model. In addition, we propose a deep copula function which can describe the dependency among the tests. Afterward, an improved discrete binary particle swarm optimization (IBPSO) algorithm is proposed to deal with TSP. Then, application to an electrical circuit is used to illustrate the efficiency of the proposed method over two available methods: 1) joint distribution-based IBPSO and 2) Bernoulli distribution-based IBPSO.

Fault detection and diagnosis for liquid rocket engines with sample imbalance based on Wasserstein generative adversarial nets and multilayer perceptron

The reliability of liquid rocket engines (LREs), which are the main propulsion device of launch vehicles, cannot be overemphasised. The development of fault detection and diagnosis (FDD) technology for LREs can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. With the rapid development of artificial intelligence technologies such as machine learning and artificial neural network, data-driven FDD methods have gained increasing attention. However, the scarcity of engine fault samples limits the application of this methods. We proposed a method combining Wasserstein generative adversarial nets (WGANs) and multilayer perceptron (MLP) to perform FDD for LREs with sample imbalance. Wasserstein generative adversarial nets were trained using the fault data from the actual hot-firing ground test of a large LRE. Considerable fault data were generated to expand the data set to balance the ratio of positive and negative samples. Subsequently, the expanded data set was used to train the MLP for FDD of a large LRE. The results showed that the samples generated by the WGAN were authentic, confirming the application of the proposed method as a novel and effective tool for establishing a complete LRE fault database. Furthermore, the diagnosis times of the proposed method on five fault tests were advanced by 0.66, 15.82, 0.24, 0.14 and 1.08 s in relation to those of the conventional red-line cut-off system. Compared with support vector machine and adaptive threshold algorithm, the proposed method also performed better.

Power switch open circuit fault diagnosis strategy for novel fault-tolerant electric drive system based on fuzzy logic

A novel fault-tolerant electric drive system based on dual-winding permanent magnet motor (DWPMM) has been proposed to enhance the reliability of electric drive system. The proposed electric drive system with strong fault-tolerant capability not only possesses the superiority of high reliability, but also can reduce the cost of drive system and adapt to single power supply. Due to temperature mutation, line aging, electromagnetic interference, and low hardware reliability, the intermittent open circuit fault will be caused in power switches by the failure of drive signal. The power switch intermittent open circuit fault is a fault between normal state and complete fault state, which is not a complete open circuit fault and cannot be diagnosed by the conventional fault diagnosis methods. The fuzzy logic means to imitate the way of judgment and reasoning of uncertain concepts in human brain, and reason to overcome the fuzzy information problem which is difficult to solve by regular methods. In this paper, the power switch open circuit fault diagnosis strategy for novel fault-tolerant electric drive system based on fuzzy logic is proposed and investigated. Through theoretical analysis and verification, the research results indicate that the proposed power switch open circuit fault diagnosis strategy based on fuzzy logic can detect and locate the general and intermittent open circuit fault in real time and accurately.

A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples.

Aiming at the problems of low fault diagnosis accuracy caused by insufficient samples and unbalanced data sample distribution in bearing fault diagnosis, this paper proposes a fault diagnosis method for rolling bearings referencing conditional deep convolution adversarial generative networks (C−DCGAN) for efficient data augmentation. Firstly, the concept of conditional constraints is used to guide and improve the sample generation process of the original generative adversarial network, and specific constraints are added to the data generation model to perform a balanced expansion of muti-category fault data for small sample data sets. Secondly, aiming at the phenomena of training instability, gradient disappearance and gradient explosion in the imbalanced sample set, it is proposed to optimize the structure of the generative network by using the structure of self-defined skip connections and spectral normalization, while using the Wasserstein distance with penalty term instead of cross entropy. The function is used as the loss function of the generative adversarial network to improve the stable feature extraction ability of the generative network and the effect of the training process; in this way, simulation sample data with only a small variation from the real data distribution can be generated. Finally, the complete fault data set (after mixing the original data with sufficient fault category and sample number) and the generated data are input into the one-dimensional convolution neural network for fault diagnosis of rolling bearing. The experiment’s results show that the diagnosis method in this paper can improve the fault classification effect of rolling bearings by generating balanced and sufficient sample data.

Fault surface extraction from a global perspective

Fault surface extraction plays a vital role in structural interpretation and structural modeling, which can enhance our understanding of geologic structures. Significant effort has been invested in fault surface extraction in the past few years. These contributions can be roughly divided into two types: one constructs fault sticks or fault patches and links them to form fault surfaces, and the other constructs a complete fault surface by roughly determining the location of a fault. The former is likely to extract incomplete fault surfaces in areas with low data quality or complex fault structures. The latter has problems dealing with faults in the case of complex fault structures, such as intersecting faults. To effectively address these problems, we have developed a novel fault extraction workflow from a global perspective, which can extract fault surfaces on the premise of obtaining the fault distribution of the entire data set. First, we introduce a multilayer complex network to characterize the overall distribution of faults, in which nodes represent faults, node attributes cover information, such as the location of the faults, and edge attributes give the relationship between faults. Then, for each fault represented by the node, we develop a fault surface extraction method based on computational topology, which can guarantee the completeness of the fault surfaces. We apply our method to synthetic data and field seismic data to evaluate the method quantitatively and qualitatively, respectively. The results indicate that the proposed approach extracts the fault effectively, ensures the completeness of the fault surface, and has high performance in areas with complex faults. Thus, the approach can work well in complex situations, such as intersecting faults.