Fault Feature Research Articles

Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images

Abstract The rolling bearing is a critical component of mechanical equipment, and its failure can lead to serious consequences. In order to effectively extract fault features of rolling bearings and improve fault diagnosis performance, a fault diagnosis framework based on hierarchical multiscale dispersion entropy (HMDE) and improved histogram of oriented gradient (HOG) is proposed by combining entropy method with image recognition method. Firstly, the original vibration signal is subjected to moving average filtering to eliminate sudden noise and outliers. Then, HMDE is used for the extraction of fault features. HMDE can evaluate the complexity of the signal at different levels and scales, thereby extracting more comprehensive information. Based on HMDE, entropy color block (ECB) images are generated and the improved HOG of the images are extracted. Finally, Knearest neighbor (KNN) is used to classify the improved HOG features, completing the recognition of different working states of rolling bearings. The validity and robustness of the proposed fault diagnosis framework are proved by the verification experiments on the public bearing datasets in Case Western Reserve University and Southeast University.

Enhancing multi-type fault diagnosis in lithium-ion battery systems: Vision transformer-based transfer learning approach

Ensuring the accurate diagnosis of internal short circuit (ISC) faults and consistency anomalies is crucial for maintaining the high safety and longevity of battery systems. The challenge lies in the high similarity between the features of ISC faults and consistency anomalies, which can complicate accurate fault diagnosis. This study introduces a multi-fault diagnostic model that leverages the Vision Transformer (ViT) and employs simulated data to accurately pinpoint ISC faults and intricate consistency anomalies, such as capacity and state of charge anomalies, achieving an impressive accuracy rate of up to 100 %. The process begins with an in-depth analysis of fault mechanisms to extract and differentiate multi-fault features using a mean difference model. Subsequently, the ViT's exceptional ability to capture temporal information, which aids in the accurate identification of fault features. The integration of transfer learning with experimental data further refines the real-world diagnosis accuracy. The results confirm the robustness of the proposed method, highlighting its potential to substantially mitigate safety hazards in battery systems and enhance operational reliability.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Fault diagnosis of bearings under variable operating conditions based on domain adaptation

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.

Seismic fault detection with sliding windowed differential cepstrum–based coherence analysis

AbstractCepstral decomposition is beneficial for highlighting certain geological features within the particular quefrency bands which may be deeply buried within the wide quefrency range of the seismic data. Converting seismic traces into the corresponding cepstrum components can better analyse some characteristics of underground strata than the traditional spectral decomposition methods. We propose the sliding windowed differential cepstrum–based coherence analysis approach to delineate the fault features. First, the data are decomposed using a sliding windowed differential cepstrum, which results in multi‐cepstrum data of corresponding quefrency of certain bandwidth. These different multi‐cepstrum data may highlight the different stratigraphic features in a certain quefrency band. We select the first‐order common quefrency volume as the featured attribute. Then, eigenstructure‐based coherence is applied on the first‐order common quefrency data volume to statistically obtain the fault detection result with a finer and sharper image. Synthetic data and field data examples show that the proposed method has the ability to better visualize all the possible subtle and minor faults present in the data more accurately and discernibly than the traditional coherence method. Compared with the ant‐tracking method, the proposed method is more effective in revealing the major faults. It is hoped that this work will complement current fault detection methods with the addition of the cepstral‐based method.

Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.

Fault Characteristics and Reservoir Potential of Mesozoic Basins in the Southern East China Sea

The East China Sea Basin (ECSB) is an integral part of the Western Pacific tectonic system. Its development is linked to the Kula–Pacific Plate and the formation and expansion of the Philippine Sea Basin. Recent advancements in exploration technologies and theory have been applied to Mesozoic basins in the East China Sea. Researchers have posited that the southern basin has great oil and gas exploration potential. However, the characteristics and evolution of fault structures and their influence on hydrocarbon accumulation remain unclear. Here, in-depth geometric and kinematic analyses of Mesozoic fault structures in the southern ECSB were conducted using the latest interpretations of 2D seismic data and structural analysis theory. The findings revealed that the fault system was well developed and predominantly exhibited multiphase extensional and extensional-torsional features. Based on their lateral distribution and morphology, faults were categorized into three structural styles and seven combinations. According to their developmental timing, periods of active faulting were attributed to the Yanshan and Himalayan epochs. Multiphase fault activities strongly controlled the formation of traps and thus hydrocarbon accumulation, while earlier NE-trending faults controlled the formation of structural belts and hydrocarbon source areas.

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.

Bearing fault diagnosis based on enhanced Canberra distance feature in SDP image

Abstract Feature enhancement is important in mechanical equipment fault diagnosis. A limited set of characteristic parameters is insufficient for diagnosing bearing signals with multiple fault types. The presence of noise increases the difficulty of extracting fault features from images. To address the challenge of diagnosing rolling bearing faults in complex environments, this study presents an enhanced weighted image fusion framework aimed at enhancing fault features within the images, which enables accurate diagnosis of bearing faults using a limited number of features. The proposed method encompasses four distinct stages. In the first stage, a symmetrized dot pattern method is employed to transform one-dimensional time-series data into two-dimensional images, visualizing the signal in a 2D format. In the second stage, image binarization and an improved weighted fusion method are utilized to simplify subsequent processing and enhance the image features. The third stage involves extracting the image’s contrast and maximum singular value to improve the Canberra distance calculation. Finally, the enhanced Canberra distance is used for classifying bearing faults. Performance testing of the image feature enhancement is conducted on various datasets containing rolling bearings. Comparative experiments with alternative enhancement methods demonstrate the superiority of the proposed improved weighted image fusion framework. Comparative experiments with the original Canberra distance validate the effectiveness of the enhanced Canberra distance. Additionally, experiments conducted in noisy environments confirm the robustness of the proposed approach. Furthermore, the image feature enhancement method is applied to other bearing datasets, and the experimental results demonstrate its effectiveness in enhancing fault feature representation and achieving accurate diagnosis of rolling bearings.

Synchronous Decomposition Match-Reassigning Transform and Its Application in Planetary Gearbox Fault Diagnosis

Abstract Under time-varying operating conditions, the instantaneous frequency (IF) of the vibration signal of the planetary gearbox exhibits non-stationary time-varying closely spaced characteristics as well as non-proportional and non-synchronous characteristics, making it difficult for traditional time-frequency analysis (TFA) methods to accurately identify its fault features and obtain accurate time-frequency representations (TFR). To address this challenge, this paper proposes a TFA method based on Synchronous Decomposition Match-Reassigning Transform (SDMRT). Firstly, the successive variational mode decomposition (SVMD) is used to adaptively separate non-synchronous frequency components in the original vibration signal. Then, the chirp rate (CR) describing the signal harmonic structure is used to synchronously match each frequency component, obtaining the TFR corresponding to each component. Next, all TFRs are merged to obtain a complete TFA result. Finally, Finally, the energy spread is re-aggregated to the ridge of the IF using the reassignment principle, resulting in a new frequency estimation operator called the Synchronous Decomposition Match-Reassigning Operator (SDMRO). SDMRT can adaptively separate non-proportional and non-synchronous IFs and achieve precise matching of time-varying closely spaced IFs, thereby completing the TFR of the vibration signal generated by the planetary gearbox. By analyzing simulated signals and measured vibration signals from planetary gearboxes, the method proposed in this paper is significantly superior to other TFA methods, thereby validating the effectiveness and superiority of SDMRT.

Rolling element bearing fault diagnosis under time-varying speed conditions based on improved wavelet packet transform and envelope order tracking

Abstract Rolling element bearings (REBs) are crucial components of rotating machinery and play an important role in industrial production. Failure of REBs can lead to significant economic losses and catastrophic accidents. In order to ensure their safe and stable operation, we propose a novel approach called EOT-IWPT which combines the envelope order tracking (EOT) with an improved wavelet packet transform algorithm (IWPT) for diagnosing failures in REBs under time-varying speed conditions. First, the time- domain vibration signal is resampled at constant angle intervels according to the phase of the rotating shaft to obtain the angle-domian signal. Next, the resampled signal is decomposed to sub-band signals using IWPT. Third, the squared envelope spectra (also called envelope order spectra) of the last layer wavelet packet coefficients are obtained by the Hilbert transform and the Fourier transform. Finally, the diagnostic results can be easily determined based on the squared envelope spectra. The procedure and performance of the proposed approach are illustrated and evaluated through simulation analysis, proving that the proposed method can effectively extract the fault features. Furthermore, experimental analysis results indicate that the proposed method outperforms both EOT-WPT and SK-EOT-BPF.

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

Fault domain separation networks: A cross-domain diagnostic method for variable operating conditions in the elevator guidance system

High-speed elevators play a critical role in vertical transportation. However, they may exhibit different working conditions under varying operating conditions, posing challenges for fault diagnosis. We propose a model named fault domain separation networks (FDSN) to tackle the cross-domain diagnostic problem of guidance system by leveraging transfer learning techniques. FDSN comprises three distinct modules that extract invariant fault features and variable operating condition features, and use orthogonal loss function for feature decoupling, achieving effective feature allocation, and mitigating the impact of variable operating conditions on fault diagnosis. To evaluate and demonstrate the efficacy of FDSN, a high-speed elevator platform is constructed, and fault vibration data under various working conditions are simulated to form a comprehensive dataset. The proposed FDSN achieved an average diagnostic accuracy of 92.7% in cross-domain diagnostic tasks, outperforming other comparative methods and demonstrating the remarkable superiority.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Multi-fault diagnosis of industrial rotating machines using advanced sliding window and WSST-CNN

Accurate fault diagnosis of rotating machines is the most important part of industries, as it helps manage the workforce and inventory within the stipulated time. In industries, when the fault occurs at one component, that may propagate to other components due to excessive speed and load, leading to the occurrence of a simultaneous fault condition. Identification of multi-component faults is relatively more difficult than single-component faults due to the possibility of characteristic frequency overlapping that is observed during signal processing. This work considered various single and complex multi-component fault conditions of a rotating machine, and data is acquired at different speed and load conditions using a standard acoustic array setup. The signals are then converted into signal segments using a continuously moving advanced sliding window. From the extracted segments time-frequency spectrums are generated by using Wavelet Synchrosqueezed Transform (WSST) followed by the training of deep neural networks. The performance analysis of the model is carried out, and it is found that the proposed methodology helped diagnose multi-faults at different speed and load conditions. It mitigates the complexity of high-end signal processing techniques for distinguishing the fault characteristics. This study will be beneficial for industries to diagnose multi-faults in incipiently.

A novel approach to aeroengine performance diagnosis based on physical model coupling data-driven using low-rank multimodal fusion method

Aeroengine health assessment plays a pivotal role in ensuring flight safety and reliability. Traditionally, this process involves diagnosing the performance of the aeroengine gas path. However, owing to the intricacies of operating conditions, non-linear performance, and the interplay of gas path performance fault characteristics, determining the aeroengine health condition directly from engine monitoring information poses a significant challenge, particularly in cases of insufficient sensor data. To address these challenges, a novel digital twin method for aeroengine performance diagnosis has been proposed. This method integrates data-driven and performance models, employing a low-rank multimodal fusion approach. By digitizing the physical system or process through mathematical models and simulation technology, this approach presents distinct advantages compared to previous methods relying solely on models or data. At the aeroengine component level, an adaptive model was implemented, and the data-driven model was constructed using flight data. Gas path fault classification employed support vector machines. The engine digital twin was established through low-order multimodal fusion. Results indicate that the proposed method attains excellent diagnostic accuracy under both steady and transient conditions. It can be harnessed to enhance engine performance monitoring and evaluation, thereby improving the reliability, availability, and efficiency of the engine.

Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

Arc Ignition Methods and Combustion Characteristics of Small-Current Arc Faults in High-Voltage Cables

High-voltage cables will continue to operate for a period of time in the event of a small current arc fault, which poses a risk of fire. Two simulated ignition methods, moving electrode and melting fuses, are proposed to analyze the ignition characteristics of low-current arcs. The ignition test was carried out, and the combustion effect was compared. The results indicate that the moving electrode ignition method can achieve long-distance arc ignition test when the current is small and is suitable for simulating the arc ignition situation of cable outer protective layer damage. By controlling the movement speed, it can be ensured that the arc will not be interrupted during the electrode movement process. However, the arc is difficult to sustain using the fuse melting method when the current is small and the distance is long. The fuse melting method is suitable for simulating insulation breakdown situations. The results show that the critical arc duration for cable ignition under five different current conditions of 2–10 A is 28 s, 21 s, 14 s, 9 s, and 4 s, respectively. The maximum height of the cable flame under 2–10 A arc current is 9–52 cm and 16–63 cm, respectively, when the arc duration is 50 s and 100 s. The self-ignition time of the cable after the arc extinguishing is 8–95 s and 14–261 s, respectively. The maximum temperature of the cable flame is positively correlated with arc current, and the maximum flame temperature of the cable under 2–10 A arc current is 540–980 °C. Based on the actual current monitoring data in cable tunnels, the research results can provide reference for the risk assessment and protection of cable tunnel fires.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved northern eagle algorithm (SPNGO) to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of NGO, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the NGO, and the superiority of the SPNGO algorithm is proved through the comparison of different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The study results show that the fault diagnosis accuracy of SPNGO-VMD-SVM can reach 96.67%, which can effectively improve the accuracy of bearing fault diagnosis.

Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network

Bearing fault diagnosis presents challenges, such as insufficient fault samples and significant fault data distribution variation in different bearing operating conditions. These problems cause traditional deep learning models to show poor generality and accuracy during bearing fault diagnosis. To address these challenges, this paper proposed a few-shot bearing fault diagnosis method based on an Ensemble Empirical Mode Decomposition (EEMD) parallel neural network and a relation network (RN). First, the original bearing fault vibration signal was decomposed by EEMD, while the decomposed signal components were processed via Short Time Fourier Transfor (STFT) to obtain a two-dimensional time-frequency feature map. Then, a parallel neural network was used for initial fault feature extraction, after which the extracted features were fused to construct a more accurate multi-dimensional fault feature map. A more precise fault feature vector was generated via the feature embedding module of the RN, and the fault features of the support and query sets were stitched to create a fault feature vector set. Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. The experimental results showed that the proposed model obtained higher diagnostic accuracy than the matching network (MN) and RN meta-learning fault diagnosis (MLFD) methods. The accuracy of fault diagnosis of the model in 5Way-Nshot is >80%, and the accuracy of fault diagnosis in 5Way-10shot is the highest at 95.2%.