Western Reserve University Research Articles

Robust transfer subspace learning based on low-rank and sparse representation for bearing fault diagnosis

With the development of industrial intelligence, data-driven fault diagnosis plays an important role in prognostics and health management. However, there is usually a large amount of unlabeled data from different working conditions, making cross-domain fault diagnosis unstable and inflexible. To deal with this issue, we propose two novel transfer subspace learning methods based on the low-rank sparse representation (LRSR), called LRSR-G and LRSR-R. Specifically, LRSR-G integrates an additional matrix with LRSR to characterize the Gaussian noise for robustness, as well as capture global and local structures. Furthermore, LRSR-R adaptively learns the label matrix from samples instead of using the binary labeling matrix in LRSR-G, thus providing the possibility to improve the flexibility. In addition, we develop two efficient algorithms using the alternating direction method of multipliers to solve the proposed LRSR-G and LRSR-R. Extensive experiments are conducted on the Case Western Reserve University dataset and Jiangnan University (JNU) dataset. The results show that the proposed LRSR-G and LRSR-R perform better than the existing methods, while LRSR-R has more potential in cross-domain fault diagnosis tasks.

Fault diagnosis of rolling bearings based on dynamic convolution and dual-channel feature fusion under variable working conditions

Addressing the challenge of inconsistent data feature distribution and the difficulty of fault diagnosis in rolling bearings operating under variable conditions, a novel approach is proposed for bearings fault diagnosis. Dynamic convolution and dual-channel feature fusion are utilized in our method. In the shallow network layer, we employ a dual-channel convolutional structure, combining a large convolutional group with a small convolutional group to enhance the extraction of high-low frequency fault information from images. The improved GhostNetV2 bottleneck layer was used in the deeper layer of the network to obtain more beneficial features through the dynamic convolution and attention mechanism. Finally, fault classification and evaluation under variable working conditions was performed on the Case Western Reserve University and Drivetrain Dynamic Simulator (DDS) datasets. Our results showed that the methods and model used in this study can effectively handle the precision fault detection across various operational scenarios.

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.

Local feature expansion Vision Transformer model for bearing fault diagnosis under noise environments

Vision Transformer (ViT) shows potential in bearing fault diagnosis due to its multi-head self-attention mechanism and parallel feature extraction network which are efficient to achieve the robust complete feature representation of the fault. However, its adaption to the noise interference relies on the sufficient huge amount of training samples to prepare the local features of the fault and may suffer performance degradation when only a limited number of samples are available for the model training. To combat this challenge, an improved ViT diagnosis model based on the local feature expansion, i.e., LFE-ViT, is proposed. An auxiliary feature extraction block is introduced using a local feature expansion network and works as a parallel module with the ViT encoder. Through the enlargement of the receptive field, the multi-scale local features on a high dimensional space are available upon the limited samples. Then, through a feature embedding channel, the extracted local features are transmitted to the ViT encoder. Finally, by virtue of the multi-head self-attention mechanism to capture the time sequence global information, a fault diagnosis model comprising comprehensively local and global feature information is derived. Experimental validation on the bearing fault dataset from Case Western Reserve University shows that LFE-ViT has provided a rather satisfactory diagnosis performance under limited samples and noise environment.

Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF

Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

A fault diagnosis method based on label-wise density-domain space learning

Abstract Nonlinear space learning of fault samples is a category of common fault diagnosis methods, which usually use Euclidean distances to describe manifold structures among fault samples. However, in nonlinear space, Euclidean distances lead to a potential manifold loss problem. Aiming these issues, we propose a novel fault diagnosis method based on label-wise density-domain space learning. The label-wise density-domain space learns more intrinsic manifold structures from four density-constrained order graphs. Density-constrained order graphs constructed by our method integrate different discriminative relationships from original fault samples with the help of density-domain information, and the density-domain information can effectively capture potential density information and global structure between fault samples. By density Laplacian of the graphs, we further construct a label-wise density-domain manifold space learning model, and the analytical solutions of space projections can be obtained by solving the model. Fault features directly obtained by the space projections possess good class separability. Extensive experiments on the Case Western Reserve University fault dataset and a roll-bearing fault dataset from our roll-bearing test platform show the effectiveness and robustness of our method.

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.

Rolling bearing fault diagnosis based on improved whale-optimization- algorithm–variational-mode-decomposition method

Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis.

Knowledge-driven domain adaptation strategy for rotating machinery fault diagnosis under varying working condition

Due to the frequent switch of the working condition, fault diagnosis model for rotating machinery established on the training set (the source domain) cannot be effectively applied to the test set (the target domain). Traditional domain adaptation methods address this issue by performing feature alignment between the source and target domain, which ignores the positive guidance provided by prior knowledge. In this paper, we propose a prior knowledge-driven domain adaptation (PKDA) method for varying working condition fault diagnosis of rotating machinery, where a self-supervised learning framework is designed to integrate expert prior knowledge and structural prior knowledge. First, an expert prior knowledge guidance module is designed to extract features with physical significance. Then we align the features between the source and target domain by the max mean discrepancy metric. Besides, a progressive Shannon entropy minimization strategy is proposed to realize the feature distribution structure of intra-class compression and inter-class separation, which can effectively integrate the structural prior knowledge. In this way, PKDA can effectively utilize the prior knowledge to achieve better performance in varying working condition fault diagnosis tasks. The effectiveness of the proposed method is illustrated by an open-source rolling bearing fault dataset from Case Western Reserve University and an open-source gearbox fault dataset from Southeast University.

Bearing Fault Diagnosis based on Convolution Neural Network with Logistic Chaotic Map

AbstractBearing is the most basic component of motor, and prone to failure. Bearing fault diagnosis is paramount for improving the reliability and safety in motor‐drive systems. Therefore, convolutional neural network (CNN) is proposed with Logistic chaotic map and its corresponding fault diagnosis approach, which can effectively advance the accuracy of bearing fault diagnosis. Specifically, the Logistic chaotic map and Sigmoid function are combined into a non‐monotonic excitation function, which is employed to the full connection layer of the CNN. The proposed chaotic CNN can solve two issues that the conventional neural network inclines to get the local minimum value and the gradient of Sigmoid excitation function disappears. It is applied to fault data from the center of Western Reserve University and from the American Society for Mechanical Failure Prevention technology (in noiseless and noisy conditions). The results indicate the diagnosis accuracy of the algorithm outperforms other classical bearing diagnosis algorithms. Moreover, the chaotic CNN exhibits better anti‐noise performance.

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine-learning-based models for the tasks of fault detection, classification, and severity estimation. To enhance the effectiveness of fault diagnosis, information acquired in one phase is leveraged in the subsequent phase. Additionally, in the pursuit of attaining models that are both compact and efficient, an explainable artificial intelligence (XAI) technique is incorporated to meticulously select optimal features for the machine learning (ML) models. The chosen ML technique for the tasks of fault detection, classification, and severity estimation is the support vector machine (SVM). To validate the approach, the widely recognized Case Western Reserve University benchmark is utilized. The results obtained emphasize the efficiency and efficacy of the proposal. Remarkably, even with a highly limited number of features, evaluation metrics consistently indicate an accuracy of over 90% in the majority of cases when employing this approach.

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

For the issue of significant noise in the collected bearing fault signals, a new bearing fault diagnosis model based on mutual mapping of signals and images (MMSI) and sparse representation (SR) denoising is proposed. Firstly, the fault signal is divided into several segments with the same number of sampling points, and then arrange these segments in ascending order of rows. Secondly, convert the arranged signals into grayscale image and use dictionary learning for block denoising. Then, the de-noised grayscale image is restored to a signal in line order. Finally, k-nearest neighbor (KNN) is used for fault classification. To verify the performance of the proposed model, experiments are tested on 12 single working conditions and 30 multi working conditions on the Case Western Reserve University dataset and the Paderborn dataset. The experimental results indicate that compared with some existing models, the MMSI–SR–KNN model can not only accurately diagnose bearing faults in artificial damage experiments, but also performs better in real damage faults. This indicates that the model has good generalization ability between different datasets and working conditions.

The social, emotional and professional impact of using appreciative inquiry for strategic planning

ABSTRACT Appreciative inquiry is a strength-based organisational transformation approach developed in 1987 by David Cooperrider at Case Western Reserve. It is based on the idea that organisational change is more effective when the approach focuses on the positive attributes and experiences within an organisation instead of the challenges. Several studies have been conducted on applying appreciative inquiry in the strategic planning process at higher education institutions to discover the impact of appreciative inquiry on organisations and individuals. This study examines how the use of appreciative inquiry in a higher education institution’s strategic planning process influenced participants’ emotional, social and professional experiences. We found that using appreciative inquiry in the strategic planning process may improve contributor social experiences and attitudes towards an organisation, which could lead to higher retention rates through greater contributor satisfaction levels.

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance.

Research on fault diagnosis of industrial materials based on hybrid deep learning model

Abstract Bearing fault detection is becoming more and more important in industrial development, and deep learning image processing technology provides a new solution for this. In this study, ResNet50 is used to replace VGG-16 as the feature extraction network of Faster R-CNN, and feature pyramid network (FPN) and parallel attention module (PAM) are introduced to achieve higher detection accuracy and speed. The experimental validation was conducted with the Case Western Reserve University bearing dataset using a three-fold cross-validation and compared with Yolov5, FPN, and the original Faster R-CNN model. The experimental results show that the accuracy of the proposed bearing image fault detection method is 78.6%, the accuracy is 77.4%, and the recall rate is 76.9%, which can locate and identify bearing faults more accurately. Future work could focus on further optimizing the model structure to enhance detection performance, strengthening the model’s generalization ability to meet the detection requirements of different types of bearing faults.

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection

A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition (SGMD) and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the SGMD SGMD is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved SGMD is more effective compared to traditional SGMD. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.

New health indicators for the monitoring of bearing failures under variable loads

Bearings are one of the most critical components in rotating machines. Unexpected failure of this components may cause serious damages and unplanned breakdowns. In this paper, a new method is proposed for bearing fault diagnosis under various loads based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the sequential backward selection (SBS). The CEEMDAN is used for the automatic selection of intrinsic mode functions from vibration signals to build a bank of health indicators. Then, the SBS algorithm is used to select the most relevant indicators of the bearing different failure modes. To test the proposed method accuracy, it was first applied to the Case Western Reserve University dataset. Then, data collected from a dedicated test bench of the Laboratory of Signal and Industrial Process Analysis were introduced to this method to classify different bearing health states. The obtained results show that the proposed method is effective in identifying and classifying bearing faults under various loads with high accuracy. This method can be used for condition monitoring of bearings and prognostics in real industrial applications.

Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy

In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminator which results in similar feature distribution of source domain and target domain. These issues make it difficult for the domain adaptation-based bearing fault diagnosis methods to achieve satisfactory performance. An unsupervised domain adaptive bearing fault diagnosis method based on maximum domain discrepancy (UDA-BFD-MDD) is proposed in this paper. In UDA-BFD-MDD, maximum domain discrepancy is exploited to maximize the feature difference between the source domain and target domain, while the output feature of target domain feature extractor can cheat the discriminator. The performance of UDA-BFD-MDD is verified through comprehensive experiments using the bearing dataset of Case Western Reserve University. The experimental results demonstrate that UDA-BFD-MDD is more stable during training process and can achieve higher accuracy rate.