Fault Feature Research Articles

Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery

Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

The research on fault and structural trap of the Weixinan Sag, Beibuwan Basin, South China Sea

The Weixinan Sag in Beibuwan Basin is a pilot area of the offshore exploration and development integrated technology. The main oil bearing layers, first member of Liushagang formation and third member of Weizhou formation, develop many fault systems and structural traps. The key factors of the offshore exploration and development integrated technology are”the fault research”and “the effectiveness of structural trap”. Fault research includes the study of the geometry, dynamics, kinematics characteristics of fault, and the division of fault system. The effectiveness of structural trap include analogical studies on the structural characteristics of traps, quantitative studies on fault lateral sealability, fault vertical sealability, and prediction technology of oil column height. Synthesizing the relationship of the law of oil and gas distribution,the differential fault system in the internal of the No. 2 fault zone, and the effectiveness research of traps, the fault-controlled reservoir laws and the dominant fault-controlled reservoir mode are obtained. The research on fault and trap of offshore integrated exploration and development has been successfully applied in the Weixinan Sag. The 37 of 45 development evaluation wells have been drilled successfully and the drilling success rate is 82%. It has promoted the ODP(original drilling project) implementation of 5 oilfields, the adjustment implementation of 8 oilfields and made important contribution to the increase of reserves and production in the Weixinan Sag.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.

A Pilot Protection of Negative Sequence and Additional Network Considering Photovoltaic Integration

Background: Introducing pilot protection in active distribution networks containing PV can improve the reliability and selectivity of protection. However, the basic communication facilities of the existing distribution network make it difficult to meet the requirements of data synchronization, and the PV T-connection to the network leads to sudden changes in the impedance angle. Methods: Therefore, pilot protection of a negative sequence and additional network considering PV is proposed. The scheme is based on the feature that the PV model only outputs positive sequence components after a fault. For asymmetrical faults, the negative sequence impedance detected at both ends of the protection is utilized to construct a comparative negative sequence impedance protection criterion. For symmetrical faults, the voltage characteristics of the faulty additional network are utilized to construct a protection criterion. Results: Finally, an actual distribution network model with PV is constructed in PSCAD to verify the effectiveness and reliability of the protection method. Conclusion: The protection method is selective and reliable and is not affected by high penetration rate and PV fault characteristics.

A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers

Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model’s ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.

Fault feature extraction, feature fusion, and severity identification approaches for AUVs with weak thruster faults

Fault feature extraction, feature fusion, and severity identification approaches for AUVs with weak thruster faults

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable features under specific tasks, which affects the diagnostic performance. Hence, this paper proposes a complementary weighted dual adversarial network combined with supervised contrastive learning (CWDAN-SCL) to address the open-set cross-different working fault diagnosis of bearings. Specifically, a novel complementary weighted adversarial learning strategy is designed using supervised classification and uncertainty measurement to effectively control the participation of target domain features in the domain adaptation process and achieve the alignment of shared class fault features between the source and target domains. Moreover, an adaptive unknown fault separation module is designed using an adversarial learning method to construct a hyperplane between shared and unknown class fault features in the target domain to identify unknown class faults accurately. Additionally, a supervised contrastive loss term is designed based on contrastive learning and label knowledge to improve the aggregation of fault features of the same class and enhance the model’s generalization ability in target domain diagnosis tasks. Subsequently, the efficacy and advancement of the proposed method are substantiated through experimentation on two datasets. The experimental results illustrate that the average diagnostic performance of the proposed method is 91.73 %. This study contributes a dependable diagnostic approach for ascertaining the health status of rotating machinery equipment.

Improved GNN based on Graph-Transformer: A new framework for rolling mill bearing fault diagnosis

The structure of the rolling mill system is complex and the operating conditions are changeable. Therefore, the interdependence between the data needs to be fully considered in the fault diagnosis of the rolling mill. Although graph neural network (GNN) is a powerful architecture based on non-Euclidean spatial data, the current method is difficult to represent the long-range dependence of rolling mill fault vibration signals. Simply increasing the depth of GNN is not enough to expand the receptive field of the model, because the larger GNN model may have the problem of gradient disappearance or transition smoothing. In order to solve the above problems, an improved graph neural network based on Graph-Transformer is proposed to diagnose the health status of rolling mill. This method first performs sliding maximum sampling on the spectrum of the original vibration signal to improve the frequency resolution and reduce the feature dimension. Second, the relationship between fault features is characterized by constructing affinity graph. Finally, the long-range dependency between paired features is learned through the readout module and the self-attention mechanism in Graph-Transformer and the diagnostic results are output by the classifier. The experimental results on the rolling mill platform show that this method can not only adapt to the changing working conditions of the rolling mill but also achieve excellent performance in the case of sample imbalance and strong noise.

A hybrid dynamic adversarial domain adaptation network with multi-channel attention mechanism for rotating machinery unsupervised fault diagnosis under varying operating conditions

Data-driven intelligent fault diagnosis methods have been extensively researched and applied in rotating machinery. In practical application scenarios, factors such as variable operating conditions and scarcity of labeled samples in rotating machinery hinder the engineering application and promotion of diagnostic models. To address these challenges, this paper proposes an unsupervised domain adaptation network called the Multi-scale Hybrid Domain Adaptation with Attention (MHDAA). Firstly, a multi-scale convolutional module was developed to extract fault features at different scales. Secondly, a multi-channel attention mechanism was proposed to enable the convolution layers of different convolution kernels fully extract feature information. Finally, a hybrid domain adaptation was constructed to dynamically extract invariant features from both the source and target domains. The method was evaluated in multiple transfer scenarios of planetary gearboxes and bearings. Experimental results demonstrate that the proposed method can effectively utilize fault features with high correlation from multiple source domains to complete fault diagnosis with unknown data labels in the target domain. Moreover, the proposed method exhibits superior diagnostic performance.

Advancing the Diagnosis of Aero-Engine Bearing Faults with Rotational Spectrum and Scale-Aware Robust Network

The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a Rotational-Spectrum-informed Scale-aware Robustness (RSSR) neural network is proposed in this study to address intricate fault characteristics and significant noise interference. The RSSR algorithm amalgamates a scale-aware feature extraction block, a non-activation convolutional network, and an innovative channel attention block, striking a balance between simplicity and efficacy. We provide a comprehensive analysis by comparing traditional CNNs, transformers, and their respective variants. Our strategy not only elevates diagnostic precision but also judiciously moderates the network’s parameter count and computational intensity, mitigating the propensity for overfitting. To assess the efficacy of our proposed network, we performed rigorous testing using two complex, publicly available datasets, with additional artificial noise introductions to simulate challenging operational environments. On the noise-free dataset, our technique increased the accuracy by 5.11% on the aero-engine dataset compared with the current mainstream methods. Even under maximal noise conditions, it enhances the average accuracy by 4.49% compared with other contemporary approaches. The results demonstrate that our approach outperforms other techniques in terms of diagnostic performance and generalization ability.

Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Extract Deep Information of Bearing Fault in Steam Turbines via Deep Belief Network

Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.

An adaptive few-shot fault diagnosis method based on virtual samples generated by fault characteristics of rotating machines

The existing intelligent diagnostic methods based on the machine learning achieve good diagnostic results under the condition of large amount of the failure data available. However, in most cases, the diagnosis model is difficult to be constructed under the few-shot problem, which only normal data of the equipment is available. Therefore, a novel Adaptive Diagnosis with Fault Characteristics (ADFC) method, which builds the diagnosis model by generating personalized virtual fault samples, is proposed in the paper. The fault common characteristics are obtained based on the failure mechanism and the law of transmission paths, and the personalized fault virtual samples are generated by combining health state data contain the individual characteristics of the equipment. Then, the frequency domain features reflecting the operating status of the equipment are extracted as training samples. Finally, the fault diagnosis model based on the Convolutional Neural Network (CNN) is constructed for the equipment. The ADFC method is validated by the rotating machinery data, including the public data, the laboratory data and the field application data. The results indicate that the ADFC method achieved an average diagnostic accuracy of 92.16%, and the accuracy has been improved by at least 0.78% compared to the comparison method.

Spectral feature informed variational model and its applications to machinery fault diagnosis

Variational mode extraction (VME), a novel signal decomposition method based on a frequency-domain filter in essence, has recently become a potential tool in fault diagnosis. However, the original VME algorithm is not provided with full self-adaptation, and its performance in the extraction of fault features is subject to predefining the initial parameters, including initial center frequency (ICF) and balance parameter. To address these issues, a spectral feature informed variational model (SFIVM) algorithm is constructed to overcome the defects of parameters setting and efficiently realize the fault diagnosis without prior knowledge. Specifically, a spectral feature detector inspired by the convergence property of ICF is first developed to reveal the spectral features, including the detected center frequencies and boundary frequencies. Then, a balance parameter estimation formula is designed to adaptively determine the target balance parameter by taking advantage of the above spectral features. Finally, a highly efficient decomposition model is proposed to extract the fault-related mode from the vibration signal, where iterative optimization is unnecessary. The effectiveness of the proposed SFIVM method is verified by one simulated and two experimental cases. Moreover, its superiority and high efficiency are demonstrated by comparing it with some advanced and classical fault diagnosis methods.

Fault Characterization for AC/DC Distribution Networks Considering the Control Strategy of Photovoltaic and Energy Storage Battery

In order to cope with the failure of existing fault analysis schemes for AC/DC distribution networks with a high proportion of distributed generations, this paper proposes a fault characteristic analysis method for AC/DC distribution networks that considers the influence of distributed generation control strategies. Firstly, a transient model for the AC/DC distribution network connected to distributed generations is built. Then, the fault characteristics of the AC/DC distribution network in different stages, such as the capacitor discharge stage, inductive renewal stage, and steady state stage, is analyzed. Finally, detailed simulation analysis is conducted using PSCAD/EMTDC to validate the effectiveness of the developed scheme by the superior approximation performance between simulated curves and calculated curves.

A coarse and fine-grained deep multi view subspace clustering method for unsupervised fault diagnosis of rolling bearings

Abstract To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

Fault diagnosis method for rotating machinery based on SEDenseNet and Gramian Angular Field

The fault diagnosis in rotating machinery is crucial for ensuring the safe and dependable operation of intricate mechanical systems. Addressing the limitations inherent in traditional deep learning approaches concerning extended time sequence encoding and subpar generalization capability is paramount. The study utilizes the Gramian Angular Field (GAF) and Squeeze and Excitation (SE) attention mechanisms to alleviate these constraints. GAF enhances feature extraction by emphasizing the angular relationships among adjacent signal points to uncover latent fault characteristics. Simultaneously, through the integration of SE with DenseNet architecture, the network facilitates global information exchange and improves multi-scale fusion, thereby enhancing the precise identification of fault type and location within the signal. Experiments conducted on two datasets achieved accuracies of 100% and 99.85%, respectively, outperforming other methods and models, thereby validating the effectiveness of this study.

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.