Target Fault Research Articles

Attention guided partial domain adaptation for interpretable transfer diagnosis of rotating machinery

Domain adaptation (DA) is a well-established method to tackle the transfer diagnosis of rotating machinery. However, current DA methods encounter challenges in partial-set transfer diagnosis scenarios, where the target domain only possesses a subset of the source fault classes, due to several limitations: (1) assuming identical fault classes between the target and source domains, (2) neglecting the fine-grained differences among individual instances, and (3) lacking interpretability for transfer logic. To address these problems, this paper proposes an attention-guided partial domain adaptation (AGPDA) method for interpretable transfer diagnosis of rotating machinery. Firstly, to exclude outlier classes in the source domain, a transferability evaluator is presented to evaluate the potential benefits of source instances for domain alignment. Secondly, a novel cross-domain attention (CDA) mechanism is further developed, which leverages instance-wise attention to highlight relevant samples, enabling differentiated and effective knowledge transfer to various target samples. Finally, the CDA mechanism is implemented via a new interpretable linear cosine attention, which is specially designed to facilitate the exploration of the transfer logic. Comparison experiments demonstrate the superiority of AGPDA in both closed-set and partial-set transfer diagnosis. Furthermore, dual-perspective visualization of CDA reveals that target fault recognition primarily relies on source samples in the same class.

Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District

Earthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.

A Controllable Response Power Beam Localization Method and System Implementation

Sound source localization technology has gradually become one of the main methods for fault source target localization in dangerous and difficult-to-discover applications. In order to locate the target fault sound source more intuitively, accurately and in real time in practical applications, an audio-visual positioning system is developed. A 64-channel microphone array is designed, which is evenly distributed in a circular shape, and has a video acquisition target at the same time. The system uses GCC-PHAT to estimate the relative time delay between the signal source arriving at two microphones, and then uses SRP-PHAT algorithm to obtain weighted sum beamforming. A spatial grid search strategy based on spherical coordinate spatial contraction method is proposed. When the output power is maximum, the direction corresponding to the beam is regarded as the sound source direction. Using the same sound source signal, the proposed positioning method is compared with the direct Newton iterative method and the Cartesian coordinate scanning SRP-based maximum method. The simulation results show that the Cartesian coordinate value and spherical coordinate value of the positioning algorithm in this article are closer to the real coordinates of the sound source position than the other two methods, and the positioning operation time is the shortest. In addition, in the actual corona discharge power source positioning experiment, the system can accurately locate the insulator pollution discharge point, which is consistent with the infrared detection and positioning results and has good robustness. Therefore, the system location method can provide a new choice for sound source location applications.

A computational topology-based method for extracting fault surfaces

Fault surface extraction is a crucial step in seismic interpretation, which can help structural interpretation and structural modeling. A key focus of fault surface extraction research is to extract fault surfaces in their entirety as much as possible, rather than just in fault segments, which is more challenging in some complex fault situations. To address this challenge, we develop a fault surface extraction method based on computational topology to extract fault surfaces in their entirety as much as possible from a fault attribute and effectively handle some complex fault situations, such as intersecting faults. From a given seed point on the target fault, we use the idea of regional growth to search for high-confidence points on the target fault, called fault control points, under the constraints of the fault attribute and the calculated fault orientations. Through these fault control points, we extract the fault boundary and process the fault attribute so that only the target fault is included. Furthermore, we use an operation in computational topology called collapse to extract the target fault from the processed fault attribute using the fault boundary as a constraint. By incorporating fault orientation information and using a relatively large search distance during the control point search, our method enables the integration of segmented faults and facilitates the handling of complex fault situations such as intersecting faults. The collapse operation ensures that the extracted fault surfaces align with the fault attribute, correspond to the actual fault locations in seismic data, and enhance fault continuity. In addition, we develop an automatic method for picking seed points to realize the extraction of all the faults in the research data. We test our method on several field data sets and the experimental results demonstrate its effectiveness. In some complex fault situations, such as intersecting faults, our method performs well and indicates a significant improvement over the compared method.

A periodic-modulation-oriented noise resistant correlation method for industrial fault diagnostics of rotating machinery under the circumstances of limited system signal availability

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method.

An effective multi-source data fusion approach based on [formula omitted]-divergence in belief functions theory with applications to air target recognition and fault diagnosis

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in multi-source data fusion. Unfortunately, the application of Dempster’s rule in BFT often produces unexpected and counterintuitive results when it encounters highly conflicting evidence. In this paper, we propose an enhanced belief α-divergence to quantify the discrepancies between the evidences by considering both belief and plausibility functions. We further present two symmetric versions of enhanced belief α-divergence. We demonstrate that the proposed symmetric divergences exhibit key properties including non-negativity, non-degeneracy and symmetry. Also, we analyze that the proposed symmetric divergences can be converted to χ2 divergence, Jeffreys divergence, Hellinger distance, Jensen–Shannon divergence and arithmetic–geometric divergence under the framework of BFT in some special cases. Leveraging the proposed symmetric divergences, a novel multi-source data fusion approach is proposed, which evaluates the credibility and informational volume of each evidence, offering a more nuanced understanding of their significance. To validate the proposed approach, we apply it to air target recognition and fault diagnosis scenarios, where our proposed approach showcases superior performance.

Fault Diagnosis Based on Tensor Computing and Meta-Learning for Smart Grid and Power Communication Network

Fault diagnosis (FD) is a critical challenge for the smart grid and the power communication network, especially when both heterogeneous networks are exponentially becoming enormous and complicated. Consequently, some conventional FD schemes based on labor seem inefficient, even disabled, because they usually cannot efficiently utilize multi-dimensional and heterogeneous big data from both networks. To deal with this challenging technical problem, a novel FD scheme based on tensor computing and meta-learning is proposed for the smart grid and the power communication network. In the proposed scheme, tensor computing is used to process tensor big data from both networks, and a new data fusion scheme is designed to complete and analyze the incomplete and sparse big data. Based on the fused data, a meta-learning approach is used to construct the FD scheme, especially when the target fault samples are inadequate and sparse. In meta-learning, the convolutional neural network is employed as a base learner to generate an FD training model, and the model-agnostic meta-learning algorithm is utilized to fine-tune and further train the pre-trained model. Simulation results and theoretical analysis indicate that the proposed DF scheme based on tensor computing can efficiently process sparse and heterogeneous big data from both networks. Furthermore, the meta-learning-based FD scheme provides an efficient way to diagnose faults with inadequate target samples. The proposed FD scheme based on tensor computing and meta-learning provides a novel solution to detect and analyze the potential faults for smart grid and power communication networks.

Improved differential fault analysis of Grain128-AEAD

The number of smart devices connected to the Internet has been constantly increasing, and as a result, lightweight cryptography (LWC) has become more important in the past decade. The Lightweight Cryptography (LWC) Project is an initiative taken by the National Institute of Standards and Technology (NIST) to standardize such LWC algorithms. Grain128-AEAD, which was submitted to the NIST LWC project, is an encryption algorithm that provides both confidentiality and integrity assurance. Third-party security analysis of the submitted ciphers is an important aspect of the evaluation of the submission to the NIST LWC project. Although several pieces of existing research, such as the bit-flipping attack, random fault attack, and deterministic random fault attack, have examined the security of Grain128-AEAD, there is still room for improvement in the fault attack models of these studies. This work aims to fill this research gap by analyzing the security margin of Grain128-AEAD against a series of improved differential fault attacks. In this study, we developed a probabilistic random fault attack and applied it to Grain128-AEAD. As an improvement of the existing research, a probabilistic approach can be applied to a more relaxed moderate control attack model. The existing moderate control model assumes the fault to be injected within any bit of a given byte, whereas the faults in our improved approach can be injected within any bits of a two-byte/four-byte segment, thereby relaxing the fault precision. The results indicate that the improved moderate control requires 388 keystreams for the two-byte model and 279 for the four-byte model to identify the target fault locations for implementing a state recovery attack. The relaxed fault attack models presented in this work are more practical to implement; hence, the findings of this research have improved the existing studies and narrowed the current research gap on the fault attack models of Grain128-AEAD. % To maintain consistency in terminology, "Grain-128AEAD" has been revised to "Grain128-AEAD" in both the abstract and the main text. Please confirm this revision.

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.

A novel mechanical fault diagnosis for high-voltage circuit breakers with zero-shot learning

In recent years, data-driven methods have been widely used in the field of high-voltage circuit breakers (HVCBs) fault diagnosis. However, due to the complex mechanical structure of HVCBs and the special operating environment, it is difficult to obtain a large amount of fault samples and exhaust all fault types. The lack of fault samples and fault types typically results in significant degradation of diagnostic performance. To address this problem, we design a novel method named R-MLL for zero-shot HVCB fault diagnosis. R-MLL tries to identify unseen fault types only by training seen fault types. First, to focus on all the details of the HVCB mechanical vibration signal, the wavelet transform is used to multi-scale refine the fault data. Second, a new network (RDSCNN) is designed to extract multidimensional features based on convolutional neural network incorporating residual block and depthwise separable convolution. Third, a multi-label attribute learning network is designed, enabling the fusion of fault features and attributes and allowing attribute labels to assist fault diagnosis tasks. Extensive experiments show that R-MLL gets average accuracy of 86.2% for compound fault diagnosis without the need of using target fault samples for building the diagnostic model. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for zero-shot HVCB diagnosis.

Estimating the Number of Extra Tests During Iterative Test Generation for Single-Cycle Gate-Exhaustive Faults

Cell-aware and gate-exhaustive faults are used for modeling defects whose activation requirements are more complex than those of stuck-at or transition faults. The number of cell-aware or gate-exhaustive faults targeted for test generation has to be bounded to avoid the generation of excessive numbers of tests. In this context, it is advantageous to be able to estimate the number of tests in advance based on the number of target faults, and avoid targeting an excessive number of faults that will result in an excessive number of tests. This article suggests such an estimate for the scenario where test generation is applied iteratively using subsets of single-cycle gate-exhaustive faults such that the detection of every additional subset will provide a meaningful increase in the coverage of the test set. Based on the results of iteration I-1, the estimate predicts the number of tests that will be obtained in iteration I. Test generation can terminate, without generating additional tests, when the estimate indicates that the number of tests will exceed a preselected bound. Experimental results for benchmark circuits demonstrate the accuracy of the estimate.

Evaluation of the Relationship Between Possible Earthquake Time History Shape Occurring in a Target Fault Using Pseudo-Basis Function

Evaluation of the Relationship Between Possible Earthquake Time History Shape Occurring in a Target Fault Using Pseudo-Basis Function

Semantic-Consistent Embedding for Zero-Shot Fault Diagnosis

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that can not be collected in advance, which may restrict the performance of fault diagnosis methods. In this paper, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Barlow matrix</i> is designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods. The code of SCE can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/htz-ecust/Semantic-consistent-Embedding</uri> .

A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data

Transfer learning exhibits exciting advantages in solving the data shortage in fault diagnosis, while most of the existing methods still need target domain fault data, which weakens the performance in some applications where the target fault data could not be provided. Focusing on the no fault data problems, this paper proposes a new transfer learning method based on simulation data. During the route of the proposed method, the theoretical fault characteristic frequencies are pre-evaluated for the monitored bearing, based on which the fault impulses are then constructed. The fault vibration signals are further simulated via mixing the constructed fault impulses with the measured normal baseline data. The envelope spectra of the simulation signals are used as the input to train a network with multi-head attention to identify fault types of the target bearing. The diagnosis performance of the proposed method has been validated via three groups of experimental data.

Exploration of geothermal resources using comprehensive electromagnetic method

Geothermal resource is a kind of crucial clean, renewable energy with high potential development and application. However, it involves great exploiting risks since it buried deep beneath the crust of the Earth. The location and depth of geothermal resources can be determined using the geophysical methods. Controlled source audio-frequency magnetotellurics (CSAMT) and transient electromagnetic method (TEM) have relatively high efficiency and robustness. In this paper, a simplified geothermal system resistivity model was established, which allowed TEM and CSAMT to differentiate target faults with various characteristics (resistivity, dip angle and width). Through a comprehensive electromagnetic exploration in Huairen County, Shanxi Province, China as an example, the performance of TEM and CSAMT in determining geothermal resources was evaluated. The results showed that both methods could examine the geoelectrical structures from various perspectives, and the effectiveness of comprehensive electromagnetic method for geothermal resources was presented.

Higher Education Multimedia Teaching System Based on the Artificial Intelligence Model and Its Improvement

In order to improve the quality of multimedia teaching in higher education, this article analyzes the problems existing in multimedia education in higher education, improves the multimedia teaching system, and proposes a three-dimensional multimedia image surface extraction method based on crease. Moreover, this article uses the level set method to segment the Gaussian smoothed multimedia data to obtain the preliminarily divided target fault area and then extracts the fault surface as the feature of the target area. The analysis demonstrates that the multimedia teaching system of higher education based on the artificial intelligence model can effectively enhance the multimedia teaching mode and promote its further development.

Effective Isolation of Fault-Correlated Variables via Statistical and Mutation Analysis

It is a widely-adopted strategy for developers to monitor the values of program variables when debugging in practice. In particular, developers often set breakpoints at specific locations or execute the program step by step in the debugging mode to inspect if abnormal values or status will be observed for concerned variables. Such a practical debugging strategy can facilitate developers in understanding and localizing the target fault. This study aims to identify suspicious program variables of a given fault (i.e., denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fault-correlated variables</i> ) automatically, thus facilitating the debugging activities for developers. To the best of our knowledge, this is the finest granularity in fault localization (FL) so far, which can address the limitations of being coarse-grained as faced by existing FL techniques. However, isolating fault-correlated variables precisely is challenging since there are usually substantially different variables used or defined in a program, and plenty of them are in the same basic block which cannot be well discriminated from each other since they will be either executed or not against the given test suite. To address such challenges, this study presents IsoVar, a two-phase model to isolate fault-correlated variables. Specifically, IsoVar first performs statistical analysis based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variable execution matrices</i> , which is a novel concept proposed in this study, to identify a set of suspicious variables. It then observes the impacts of those variables on the program dynamically after applying subtle mutations at the bytecode level, to further isolate fault-correlated variables. Extensive experiments on Defects4J and Bears demonstrate that IsoVar can outperform state-of-the-art techniques significantly ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$13.0\%$</tex-math></inline-formula> for MAP and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$19.3\%$</tex-math></inline-formula> for MRR). More importantly, we incorporated IsoVar into 11 existing FL techniques as well as 14 automated program repair techniques, and found that IsoVar can significantly boost their performance.

Fault Diagnosis Algorithm of Gearboxes Based on GWO-SCE Adaptive Multi-Threshold Segmentation and Subdomain Adaptation

The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining adaptive multi-threshold segmentation and subdomain adaptation. First of all, in the data acquisition stage, a non-contact, easy-to-arrange, and low-cost sound pressure sensor is used to collect equipment signals, which effectively solves the problems of contact installation limitations and increasingly strict layout requirements faced by traditional vibration signal-based methods. The continuous wavelet transform (CWT) is then used to convert the original vibration signal of the device into time–frequency image samples. Further, to highlight the target fault characteristics of the samples, the gray wolf optimization algorithm (GWO) is combined with symmetric cross entropy (SCE) to perform adaptive multi-threshold segmentation on the image samples. A convolutional neural network (CNN) is then used to extract the common features of the source domain samples and the target domain samples. Additionally, the local maximum mean discrepancy (LMMD) is introduced into the parameter space of the deep fully connected layer of the network to align the sub-field edge distribution of deep features so as to reduce the distribution difference of sub-class fault features under different working conditions and improve the diagnostic accuracy of the model. Finally, to verify the effectiveness of the proposed diagnosis method, a fault preset experiment of the gearbox under variable speed conditions is carried out. The results show that compared to other diagnostic methods, the method in this paper has higher diagnostic accuracy and superiority.

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Most existing methods assume that the prior information on the fault modes of the target domain is known in advance. However, in engineering practice, prior knowledge of fault modes is rare in a new domain, in which there may be only partial source fault modes or some new fault modes. Furthermore, up to the present, almost all existing cross-domain fault diagnosis methods require the labeled source data during the model training process, which restricts their deployment on certain devices with limited computing resources. To this end, we propose a universal source-free domain adaptation method that can handle cross-domain fault diagnosis scenarios without access to the source data and is free of explicit assumptions about the target fault modes. More specifically, we develop a convolutional network with a Transformer as the attention module to extract discriminative feature information from the source data and then send the model and parameters to the target domain. In target domain training, we first propose a supervised contrastive learning strategy based on source class prototypes, which utilizes high-confident predictions to achieve source-free domain alignment and class alignment. Then, we also introduce a threshold-based entropy max–min loss to further align known class samples in the target domain or reject target outlier samples as an unknown class. Furthermore, we introduce self-supervised learning to further learn feature representations of the target domain to reduce the previous misclassification. A series of experiments on two rotating machine datasets demonstrate the effectiveness and practicability of the proposed method.

Seismicity-based 3D model of ruptured seismogenic faults in the North-South Seismic Belt, China

The North–South Seismic Belt produces the most frequent strong earthquakes in the Chinese continental region, such as the MS 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013. This seismicity results in significant hazards. Fault geometry modeling is crucial for analyzing earthquake preparation and trigger mechanisms, simulating and predicting strong earthquakes, inverting fault slip rates, etc. In this study, a novel method for obtaining geometric models of ruptured seismogenic faults over a large area is designed based on datasets from surface fault traces, fault orientations, focal mechanism solutions, and earthquake relocations. This method involves three steps. 1) An initial model of the fault geometry is constructed from the focal mechanism solution data. This initial model is used to select the earthquake relocation data related to the target fault. 2) Next, a fine model of the fault geometry with a higher resolution than that of the initial model is fitted based on the selected earthquake relocation data. 3) The minimum curvature interpolation method (Briggs, 2012) is adopted to build a 3D model of the subsurface fault geometry according to the three-dimensional coordinates of nodes on all profiles of each fault/segment. Based on this method and data collected in the North–South Seismic Belt, the fine morphologies of different faults along 1,573 transverse profiles were fitted, and a 3D model of 263 ruptured seismogenic faults or fault segments in the North–South Seismic Belt was built using the minimum curvature spatial interpolation method. Since the earthquake number decreases with increasing depth, the model uncertainty increases with increasing depth. Different ruptured faults have different degrees of seismicity, so different fault models may have different uncertainties. The overall fitting error of the model is 0.98 km with respect to the interpreted results, from six geophysical exploration profiles.