Western Reserve University Research Articles

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

The unsupervised bearing fault diagnosis method based on the dual-framework Siamese network

Abstract Aiming at the problem of insufficient bearing fault samples in practical engineering, an unsupervised bearing fault diagnosis method with double frame twin network is proposed in this paper. This method only uses the source domain data to diagnose the target domain data, and can minimize the problem of domain difference. The concrete method is firstly to carry on continuous wavelet transform (CWT) to the rolling bearing signal, and the processed signal is used as the input feature of convolutional neural network (CNN). The twin network structure is optimized and Manhattan distance is used as the loss function in the training process. The proposed CM network is applied to the diagnosis of inner ring fault, outer ring fault and normal state of rolling bearing. The results show that CM network can achieve over 90% accuracy on SQ variable speed vibration signal data set (SQV), Shijiazhuang Railway University data set (STD) and Case Western Reserve University data set (CWRU), which is suitable for bearing fault diagnosis under different working conditions and provides a new direction for fault diagnosis. The PyTorch code is available at https://github.com/xiaotianqu/The-unsupervised-bearing-fault-diagnosis-method-based-on-the-dual-framework-Siamese-network

Rolling bearing fault diagnosis method based on adaptive signal diagnosis network and its application

Aiming at the problems of fixed convolution kernel size and poor targeting of extracted features in the application of deep learning in fault diagnosis, this paper develops a fault diagnosis framework called adaptive signal diagnosis network (ASDN), in which the EEMD of multi-scale signal decomposition is improved in the adaptive preprocessing stage to adaptively capture the transient changes of signals. Meanwhile, SSA is improved to further extract fault trends and periodic components to optimize the signal representation. In the adaptive deep learning stage, an innovative temporal convolutional network (TCN) with a dynamic adjustment mechanism was developed to enable the neural network to adjust its convolutional kernel size according to different frequency components so as to accurately process signals of different frequencies. Validation on datasets from Case Western Reserve University and Xi'an Jiaotong University shows the superior performance of the proposed method, with diagnostic accuracies of 100% and 97.42% on these two public datasets, respectively.

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.

Rolling bearing fault diagnosis method based on wavelet packet logarithmic energy map

Aiming at the fault diagnosis problem of rolling bearings, a diagnosis method based on wavelet packet logarithmic energy map is proposed. Firstly, a new wavelet packet node logarithmic energy formula is improved and proposed to overcome the shortcomings of cumbersome parameter determination and strong subjectivity in the traditional wavelet packet energy formula, improve the recognition of high-frequency faults and the distinction of low-frequency fault categories, so as to fully extract the initial time-frequency domain features; secondly, the Gram angle and field idea is used to realize the conversion from one-dimensional features to two-dimensional image features, so as to construct the logarithmic energy map feature based on wavelet packet, which further considers the spatial information between adjacent features, thereby optimizing the initial time-frequency domain features and improving the significance of the obtained features. On this basis, the residual network is used to improve the accuracy of fault diagnosis classification results. Finally, through the simulation verification of the standard rolling bearing data set of Case Western Reserve University, it can be seen that the fault diagnosis model constructed by the proposed method has high diagnostic accuracy and strong generalization ability.

Threshold-optimized and features-fused semi-supervised domain adaptation method for rotating machinery fault diagnosis

In the field of intelligent fault diagnosis, domain adaptation (DA) technology achieves significant breakthroughs, particularly in reducing reliance on large volumes of labeled samples. Despite these advancements, challenges persist when unlabeled data do not accurately represent actual application scenarios. Additionally, the impact of pseudo-labels on conditional domain adaptation raises concerns. To overcome the above challenges, a novel DA approach based on chaos sparrow search algorithm (CSSOA) optimized threshold parameters and feature fusion deep belief network (DBN) is proposed, named CSS-DADBN. Firstly, this method, by integrating pseudo-label updating with semi-supervised domain adaptation (SSDA) and employing confidence and entropy threshold parameters as corrective rules for pseudo-label filtering, along with the introduction of iterative conditions as an additional selection criterion, effectively alleviates the aforementioned issues. Furthermore, combining the feature extraction capabilities of DBN with a domain feature fusion strategy significantly enhances cross-domain feature learning, thereby substantially improving diagnostic accuracy. Ultimately, to validate the effectiveness and practicality of the CSS-DADBN method, a series of experiments conducted on the PT700 and Case Western Reserve University (CWRU) rolling bearing test platform clearly demonstrate its utility and efficiency in intelligent fault diagnosis.

An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and classification

Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throughout industrial operations supports real-time condition monitoring of these assets. Leveraging digital technologies to analyze acquired data creates an ideal environment for applying advanced data-driven machine learning techniques, such as convolutional neural networks (CNNs) and vision transformer (ViT) to detect faults and classify, enabling accurate prediction and timely maintenance of industrial assets. In this paper, we present a novel hybrid framework based on the local feature extraction ability of CNN with comprehensive understanding of transformer within a global context. The proposed method leverages the complex weight-sharing properties of CNNs and ability of transformers to understand the larger context of spatial relationships in large-scale patterns, making it applicable to datasets of varying sizes. Preprocessing methods such as data augmentation are used to train the model on the Case Western Reserve University (CWRU) dataset in order to increase generalization through computational efficiency. An average fault classification accuracy of 99.62% is accomplished over all three fault classes with an average time-to-fault detection of 38.4 ms. MFPT fault dataset is used to further validate the method with an accuracy of 99.17% for outer race and 99.26% for inner race. Moreover, the proposed framework can be modified to accommodate alternative convolutional models.

An Improved Rolling Bearing Fault Diagnosis Model of Long Short-Term Memory Network Based on VMD Denoised Vibration Signals

Data driven fault diagnosis methods are increasingly being used for condition monitoring of rotating machinery in the era of Industry 4.0. The effectiveness of Variational Mode Decomposition (VMD) denoised vibration signals in improving the Long Short-Term Memory (LSTM) method for the intelligent fault diagnosis of a rolling bearing is reported. The raw and VMD denoised vibration signals of rolling bearings are provided as inputs to the LSTM network for classification of bearing condition. The efficacy of the methodology to extract the fault information is assessed through datasets obtained from experiment test rig and through the open-source dataset of Case Western Reserve University (CWRU). A comparative analysis is also carried out using both VMD based decomposition and denoising techniques along with four machine learning classifiers viz. Decision Tree, k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) by using the statistical features of the VMD modes. Among the different methods evaluated, VMD denoised signals when fed to LSTM result in the maximum classification accuracy of 99.14 % with experimental dataset. For the case of CWRU dataset, VMD denoised signals as input to the SVM resulted in maximum classification accuracy of 98.16%.

SPRout-DBN: a cross domain bearing fault diagnosis method based on spatial pyramid pooling residual network-DBN

Abstract Effectively leveraging the spatial features of time series signals to improve the accuracy of bearing fault classification in neural networks presents a significant challenge. To address this issue of different operating conditions, a novel model termed spatial pyramid pooling residual network-deep belief network (SPRout-DBN) is proposed. First and foremost, the Gramian angular difference fields (GADF) are utilized to encode original vibration signals of bearings. Secondly, two-dimensional images transformed by GADF from original signals are input to a novel designed residual network with spatial pyramid pooling to extract fixed-size temporal fusion feature vectors. Finally, a deep belief network is employed for classification and cross-domain learning, enabling the identification of fault samples under varying operating conditions. The proposed method is validated by two sets of datasets from Case Western Reserve University and Jiangnan University, achieving accuracies of 99.81% and 99.0% under identical operating conditions, and 99.41% and 98.43% under different operating conditions with 40 samples. Comparative analysis indicates that the proposed SPRout-DBN remains more robust and effective compared with other methods such as K-nearest neighbors, support vector machines, LeNet-5, ResNet-18, domain adaptation networks, and domain-adversarial neural networks in diverse operating environments.

Application of improved adaptive method of CEEMD and data-drive in fault diagnosis

Purpose The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis. Design/methodology/approach This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis. Findings According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate. Research limitations/implications In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied. Originality/value Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Enhanced fault diagnosis of rolling bearings using an improved inception-lstm network

ABSTRACT Rolling bearings are integral to the operation of various mechanical systems, where their condition directly impacts equipment reliability and performance. Accurate fault diagnosis is essential for preventing unexpected failures and minimising downtime in industrial settings. This study presents a novel diagnostic model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, enhanced by an improved Inception module. This integrated approach captures both spatial and temporal features of vibration signals, leading to superior diagnostic accuracy under diverse operating conditions. Experimental results using the Case Western Reserve University (CWRU) bearing dataset demonstrate an impressive average fault diagnosis accuracy of 99.83%, outperforming conventional models. The robustness of the proposed model against noise and its adaptability to varying operational environments underscore its practical value for real-world engineering applications, offering a reliable solution for maintaining the safety and efficiency of critical machinery.

Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

Feasibility of Solar Thermal Water Heating Systems on Case Western's Campus

Feasibility of Solar Thermal Water Heating Systems on Case Western's Campus

Federated learning for decentralized fault diagnosis of a sucker-rod pumping system with class imbalance data

The development of modern oilfields has entered the middle and late stages, transforming towards digitalization and intelligence. However, the distribution of the sucker-rod pumping systems is decentralized, and the working condition information is skew-distributed. This situation poses a significant challenge to existing centralized fault diagnosis mechanisms. To address the existing practical challenge in the oilfield, a federated learning-based fault diagnosis framework for class imbalance in decentralized sucker-rod pumping systems (FL-CI) is proposed. This framework incorporates a parameter anonymization-ratio upload mechanism to mitigate the risk of gradient tracking. Then, a monitoring mechanism is leveraged to reversely infer global class-imbalance data using trained parameters uploaded by the clients. In addition, a ratio loss function is designed to calibrate the influence of class imbalance on the global system. After conducting comparative analysis, ablation analysis, and sensitivity analysis on a rod-pumping unit dataset (RPUD), as well as comparative and ablation analyses on the Case Western Reserve University bearing dataset (CWRU), the experimental results demonstrate that the FL-CI framework achieves superior diagnostic performance on the RPUD, with eight out of twelve evaluation metrics significantly outperforming seven state-of-the-art methods. A similar trend is observed on the CWRU, further validating the effectiveness and generalizability of the FL-CI.

Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.

A verified training support vector machine in bearing fault diagnosis

Abstract The combination of support vector machine (SVM) and deep learning is widely used in bearing fault diagnosis. The SVM-based bearing fault diagnosis method usually uses features extracted from bearing vibration signals as input. However, environmental noise in industrial applications can affect the feature representation, potentially leading to failures in SVM-based fault diagnosis methods. Adversarial robustness verification methods can evaluate the robustness of models to small perturbations in feature representations. Certified defense methods achieve robustness enhancement by embedding adversarial robustness verification into the learning process of the model. Thus, we propose a certified defense method named verified training SVM (VT-SVM) based on Lagrange duality. The proposed method incorporates adversarial robustness verification into the SVM learning framework, thereby enhancing the robustness of SVM in noisy environments. First, the certified defense optimization problem of SVM is constructed by combining the original optimization problem of SVM with the adversarial robustness verification problem. Then, the certified defense optimization problem is converted into an equivalent convex quadratic programming problem by introducing slack variables. Next, the Lagrange duality is used to transform the convex quadratic programming problem into a dual problem. Finally, the solution to the original optimization problem can be obtained by solving the dual problem. We validate the effectiveness of VT-SVM on the artificial dataset, the rolling bearing dataset from Case Western Reserve University, and the VBL-VA001 dataset from Sepuluh Nopember Institute of Technology. Experimental results demonstrate that our method both ensures high predictive performance and improves the resistance of the model to small noise.

A Gradient-Based Optimizer Method for Improving the Feature Extraction Capability of Network Architecture and Its Application to Condition Monitoring of Rotating Machinery

Aiming at the problem of inaccurate feature extraction under the massive data of rotating machinery, this paper proposes a feature extraction capability enhancement method based on Gradient-Based Optimizer for trusted network architecture to monitor the conditions of bearings, which firstly transforms the original signal into a wavelet time-frequency image and visualizes the high-dimensional and difficult-to-visualize data in low dimensions. Then, different convolutional neural network architectures are tested by implanting different noise intensities to determine the most robust network architecture. Secondly, this paper adopts the grid method as well as the swarm optimization algorithm to guide the parameters of the model to achieve the best feature extraction effect, compares the optimization effect of several swarm optimization algorithms horizontally, and determines the most effective GBO algorithm among them. Finally, to improve the credibility of the best network model, this paper combines the grad-cam method with the trusted fusion method to improve the accuracy and reliability of the model. Validated by the bearing data of Case Western Reserve University, it shows that the proposed model in this paper has good credibility and accuracy.

A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD ) and conditional domain adversarial network with entropy conditioning (CDAN+E ) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model.

Fault diagnosis of rolling bearing based on adaptive attention network and federated learning

Abstract For rolling bearings in actual industrial scenarios, the problem of low diagnostic accuracy is caused by the large difference in data distribution under different working conditions and the complexity of working conditions with a lot of redundant information, in this paper, a combination of the Federated Adaptive Attention Network (FAAN) based fault diagnostic model is proposed. Firstly, the fault information is randomly divided into multiple sequences, by utilizing dual convolutional layers and adaptive attention mechanism to process abundant vibration data, it enables accurate identification of fault information distribution in the original signal while removing superfluous information nd fusing characteristics to improve diagnostic accuracy. Furthermore, a federated learning model incorporating attention mechanism is proposed. It performs asynchronous updates based on local data distribution, improving the efficiency and accuracy of data analysis uploads, and enhancing the model’s generalization capability. Simulation experiments have been carried out using the datasets from Case Western Reserve University and Jiangnan University, and after comparative analysis, the proposed method has better performance and generalization ability.