Gearbox Dataset Research Articles

Discriminative manifold domain adaptation for cross-domain fault diagnosis of rotating machineries

Domain adaptation aims to solve the issue of distribution shift. Manifold learning uses Riemann metrics as nonlinear distance to overcome the limitation of Euclidean metric in domain adaptation. However, it ignores the feature distortion and relationship change caused by the feature mapping framework. Class overlap leads to degradation of classifier performance. Thus, this study proposes a new discriminative manifold domain adaptation (DMDA) framework to solve these problems. DMDA can align manifold structures and domain distributions through discriminative manifold alignment (DMA) and joint distribution alignment, respectively. The DMA method is developed via a new pseudo label voting mechanism, the local fisher discriminant analysis, and the geodesic flow kernel. It can achieve the manifold structure alignment and learn the discriminative features. Moreover, a structural risk minimization classifier is constructed to achieve Laplacian regularization and align the joint distributions of the source domain and target domain. It also implements the fault classification task. The experimental results on bearing and planetary gearbox datasets prove that DMDA has better fault transfer diagnosis performance than the typical domain adaptation methods based on machine learning.

Attention guided multi-wavelet adversarial network for cross domain fault diagnosis

Adversarial transfer learning is an effective method for diagnosing rotating machinery faults. However, it does not extract features completely and underutilizes unlabeled target data. Hence, an attention-guided multi-wavelet adversarial network (AMWAN) based on the hybrid metric strategy of a multi-wavelet convolutional feature extractor is proposed. First, hidden fault features in the input data are extracted via multi-wavelet convolution and an attention mechanism. Subsequently, the extracted features are input into the domain discriminator and label classifier. A gradient reversal layer is introduced to map and mix the source- and target- domain samples for feature extraction. Specifically, the k-nearest neighbor is adopted to construct a label propagation model in the source-domain feature layer to obtain pseudo-labels for the target-domain feature data. This extends the labeling information for the target-domain feature data. A hybrid metric based on the maximum mean discrepancy distance and feature cosine metric strategy is introduced in the feature layer for adversarial training, which can significantly enhance the domain adaptive effect and improve the cross-domain fault diagnosis performance of the AMWAN. Experimental validation based on two sets of planetary gearbox datasets indicate that the AMWAN exhibits outstanding fault diagnosis performance under both constant- and variable-speed and -load conditions, i.e., it achieves an average accuracy rate of up to 96.85 %, thus surpassing other methods.

Residual attention temporal recurrent network for fault diagnosis of gearboxes under limited labeled data

Data-driven fault diagnosis methods have significantly contributed to the rapid development of prognostics and health management of gearboxes. However, these methods require sufficient labeled data for model training. In real industrial scenarios, labeled data is usually scarce. Thus, a residual attention temporal recurrent network is proposed for fault diagnosis of gearboxes under limited labeled data. More specifically, a fault diagnosis framework with self-supervised sample generation learning is explored in this paper. First, a small amount of labeled data is utilized to self-supervise the generation of new samples with similar semantics in adversarial generation modeling. Second, a temporal recurrent network with residual attention is constructed to extract fault features of vibration signals using two-stage temporal and recursive operations. In addition, the construction of the residual attention mechanism can facilitate the network to have a deeper semantic understanding of the vibration signal. Finally, the model can guarantee a more robust classifier in the proposed fault diagnosis framework using a small number of labeled samples. The extensive validation of the proposed method is performed on the gearbox dataset. The experimental results show that the proposed method can achieve competitive fault diagnosis performance under limited labeled data.

A Fault Diagnosis Approach for Wind Turbine Gearbox Based on Ensemble Learning Model and Dung Beetle Optimization Algorithm

The gearbox is one of the crucial components in wind turbines, and its performance degradation would give rise to out-of-order or even damage. To accurately identify the fault of the gearbox, a novel fault diagnosis approach based on the ensemble model and the dung beetle optimization algorithm is introduced. Firstly, an ensemble learning model with different activation functions is established. Secondly, the dung beetle optimization is applied to select the base models for improving the performance and generalization ability of the ensemble model. Finally, the proposed method is tested with a gearbox dataset. The experimental results show that the proposed approach achieves higher diagnostic accuracy on the gearbox dataset.

Fault Diagnosis Method for Rotating Machinery Based on Multi-scale Features

The vibration signals of rotating machinery usually contain various natural oscillation modes, exhibiting multi-scale features. This paper proposes a Multi-Branch one-dimensional deep Convolutional Neural Network model (MBCNN) that can extract multi-scale features from raw data hierarchically, thereby improving the diagnostic accuracy of gearbox faults in noisy environments. Meanwhile, the algorithms for multi-branch generation and algorithms of the convolution and pooling for each branch are deducted. The MBCNN integrates multiple branches with interrelated convolution kernels of different widths, and each branch can extract the high-level features of the signal. The network parameters of each branch are adjusted by the loss function, which makes the features of the branches complementary. Through the design of MBCNN, the local, global, deep layer and comprehensive information can be obtained from the raw data. On the widely used Case Western Reserve University Bearing Dataset, this paper conducted a performance comparison between the proposed MBCNN and other baselines including the shallow learning methods, 1D-CNN, and multi-scale feature learning methods. Moreover, our gearbox dataset was conducted on a fault diagnosis platform, and a series of experiments were conducted to verify the effectiveness and superiority of the MBCNN. The results indicate that the MBCNN can identify the faults in the gearbox with an accuracy of higher than 92%, and the average validation time per sample is less than 3.2 ms. In a noisy environment, the diagnostic accuracy can reach 90%. The proposed MBCNN provides an effective and intelligent detection method to identify the faults of rotating machinery in the manufacturing processes.

Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features

With the development of deep learning methods, the data-driven fault diagnosis methods have attracted a great deal of interest. However, as for the data-driven fault diagnosis methods, technology has to overcome various difficulties in the practical industrial scenarios, such as variable working conditions, insufficient effective samples, and environmental noise interference. Combining with the time–frequency analysis of vibration signals, a domain adaptation fault diagnosis model based on ResNet and Transformer (DAFDMRT) is proposed in this work, aiming to solve the problems encountered by current rotating machinery fault diagnosis methods in the field of application. Firstly, the vibration signal is processed by wavelet packet transform and the time–frequency information grayscale maps is constructed. Next, a deep fusion feature extraction network combining ResNet and Transformer encoder, is designed for the extraction and fusion of the local and global features of multi-scale time–frequency information. Finally, the multi-kernel maximum mean discrepancy is applied to measure and minimize the distribution difference between the deep features of source and target domain, thereby improving the diagnostic performance of the diagnosis model in variable working conditions. In this work, comparative experiments are conducted as for bearing and gearbox datasets under variable working conditions. The results indicate that DAFDMRT can show excellent performances in terms of fault diagnosis and generalization ability.

A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression

The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present a signal-adaptive asymmetrical autoencoder with a transform domain layer to compress sensor signals. First, a new discrete cosine Stockwell transform (DCST) layer is introduced to replace linear layers in a multi-layer autoencoder. A trainable filter is implemented in the DCST domain by utilizing the multiplication property of the convolution. A trainable hard-thresholding layer is applied to reduce redundant data in the DCST layer to make the feature map sparse. In comparison to the linear layer, the DCST layer reduces the number of trainable parameters and improves the accuracy of data reconstruction. Second, training the autoencoder with a sparsifying DCST layer only requires a small number of datasets. The proposed method is superior to other autoencoder-based methods on the University of Connecticut (UoC) and Southeast University (SEU) gearbox datasets, as the average quality score is improved by 2.00% at the lowest and 32.35% at the highest with a limited number of training samples.

Cocktail LSTM and Its Application Into Machine Remaining Useful Life Prediction

In the industrial field, gearbox and bearing are the key components of machines, and their health statuses are crucial to the reliable operation of machines. Therefore, the remaining useful life (RUL) prediction of gearbox and bearing is of great significance. To accurately predict the long-term RULs of gearboxes and bearings, a novel multihierarchy network based on multiordered neurons, namely, cocktail long short-term memory (C-LSTM), is proposed. First, the 21 time–frequency characteristics are extracted from the collected vibration signals, which are then input into the trained variational autoencoder to construct the health indicator (HI) with the distinct degradation trend. Next, the generated HI points are fed into C-LSTM for predicting the future HI points. With regard to C-LSTM, the existing HI vector is first divided to get the multihierarchy, and different update rules are put forward to extract various trend information from the known HI points based on the hierarchy result, then the future HI points are predicted in sequence until the threshold is exceeded. It then follows that RUL can be calculated. The proposed methodology has been verified using gearbox datasets and the IEEE 2012 bearing dataset, and the comparative results in Score, mean absolute percentage error, mean absolute error, and normalized root-mean-square error show that C-LSTM has a higher comprehensive predictive performance than the traditional prediction methods.

A robust intelligent fault diagnosis method for rotating machinery under noisy labels

Despite achieving considerable success, the fault diagnosis methods will still be disturbed by noisy labels, this causes the model’s degradation and reduced diagnostic precision. Focused on solving the above issues, a robust intelligent fault diagnosis approach for rotating machinery under noisy labels is proposed. Firstly, we maintain two deep neural networks (DNNs) and alternatively execute parameters updating and models optimization by referring to the Co-teaching strategy, which can maximize filtering different error types and implement pre-training of DNNs. Secondly, adopting a two-component Gaussian mixture model (GMM) to fit training dataset’s cross-entropy (CE) loss and realize the clean and noisy labels division according to the threshold. Then, a data augmentation method called Mixup operation is employed in semi-supervised learning (SSL) to increase noise robustness and avoid error accumulation, subsequently, performing fine-tuning and correction for clean and noisy samples. Challenging experiments on a transmission gearbox dataset under different noisy labels levels show that the proposed method has robustness to noise and significantly surpasses other approaches, which provides an important reference value for accurate fault diagnosis for rotating equipment parts with noisy labels.

Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions

In recent years, the rapid development of convolutional neural networks (CNNs) has significantly advanced the progress of intelligent fault diagnosis. Most currently-available CNN-based diagnostic models are built on the premise that the monitored machine operates under stable conditions. However, in real-world scenarios, rotary machinery usually operates at varying speeds, making the fault-related pulse features susceptible to noise oversaturation. To extract discriminative features from mechanical signals under non-stationary conditions, a global contextual feature aggregation network (GCFAN) is developed in this paper. To begin with, a global contextual module (GCM) is embedded in the CNN architecture to explore multimodal features. Then, a multiscale attention module (MSAM) is introduced to guide the model to focus on global and local discriminative information. Further, a multiscale feature enhancement module (MFEM) is established to enlarge the receptive field and eliminate useless features. Finally, the GCFAN architecture is constructed based on these improvements. To achieve favourable diagnostic results under fluctuating variable speed conditions, we apply the label smoothing algorithm and the AMSGrad algorithm to assist the training of the model. Two case studies using the benchmark variable speed bearing dataset and the HF-MS variable speed gearbox dataset were carried out to test the practicality of the developed approach. Experimental results demonstrated that the developed GCFAN performs better than seven state-of-the-art approaches.

Relationship Transfer Domain Generalization Network for Rotating Machinery Fault Diagnosis Under Different Working Conditions

Many domain adaptation (DA) models have been explored for fault transfer diagnosis. However, their successes completely rely on the availability of target-domain samples during the training process. As target domain is usually unseen, the domain-adaptation-based diagnostic models cannot meet the requirement of real-time diagnosis in actual engineering. To achieve the domain confusion in the actual diagnosis scenario, a novel relationship transfer (RT) diagnosis framework is first proposed, which can indirectly measure and reduce the distribution discrepancy between the source domain and unseen target domain. Based on the proposed RT framework, a new domain generalization transfer method, called relationship transfer domain generalization network (RTDGN) is constructed. RTDGN is divided into two phases including task-irrelevant domain adaptation (TIDA) and task-relevant domain generalization (TRDG). In the TIDA phase, a DA adversarial network with several domain discriminators is built to enhance the domain confusion of RT framework. Furthermore, to bring the adversarial network a more general domain confusion ability, a new inverse entropy loss is designed. In the TRDG phase, a residual fusion classifier is constructed to improve the generalization ability of fault classifier. Finally, the experimental results on the wind turbine planetary gearbox dataset and bearing dataset verify the effectiveness and superiority of the proposed RTDGN.

Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

Intelligent fault diagnosis has attracted intensive efforts in machine predictive maintenance. However, the structural information from multi-sensor signals has not been fully investigated. In this study, a novel temporal–spatial graph neural network with an attention-aware module (A-TSGNN) is proposed to accomplish multi-source information fusion. First, the graph structure naturally organizes the diverse sensors. The graph convolution model realizes the feature representation in the spatial dimension. Then, time-dependent learning is applied in the temporal dimension, and a temporal–spatial learning framework is built. An additional attention module is designed to learn the flexible weights and model the importance of individual sensors and their correlations. Experiments on a wind turbine dataset achieves an accuracy of 0.9669 and an F1-score of 0.9649. For the gearbox dataset, the values are 0.9927 and 0.9920, respectively. The overall macro-average area under the curve metrics reach a perfect score of 1.00 for both datasets, indicating exceptional performance. The adaptive attention mechanism is also discussed to verify the superiority of the A-TSGNN. Furthermore, comparisons with the single-sensor scheme and other fusion models demonstrate the stable performance of the proposed method. The A-TSGNN provides a potential model for comprehensively utilizing multi-sensor data, showing a promising prospect.

A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems

Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.

Semisupervised Fault Diagnosis for Gearboxes: A Novel Method Based on a Hybrid Classification Network and Weighted Pseudo-Labeling

To make the gearboxes reliable and secure, accurate and efficient fault diagnosis has received wide attention. Recently, many data-driven intelligent methods have made great progress due to their powerful feature extraction capability. However, most deep learning based methods can only perform well with sufficient labeled data and the shortage of labeled data has become a common problem in practical industrial applications. Therefore, a semi-supervised approach based on a hybrid classification network and weighted pseudo-labeling is proposed to relieve the above problem. The hybrid classification network is composed of an autoencoder and a softmax classifier. With this structure, the process of latent representation learning of autoencoder is guided by the supervised classification training. Therefore, the features extracted by the autoencoder are more suitable for the classification task. Moreover, a weighted pseudo-labeling method is developed to further enhance the generalization capability of the model. Firstly, pseudo labels for the unlabeled samples are generated according to the model predictions. Then, the proposed sample weighting scheme assigns a confidence-based weight to each pseudo-labeled sample, which is used to filter out the incorrect pseudo labels. Finally, these weighted pseudo-labeled samples are utilized to further optimize the model. Two gearbox datasets are utilized to validate the effectiveness of the proposed method. One is an experimental gearbox dataset and the other is an industrial wind turbine gearbox dataset. The experimental results show that the proposed method can still achieve high diagnostic accuracies for both the experimental and industrial datasets under limited labeled data.

An interpretable convolutional neural network with multi-wavelet kernel fusion for intelligent fault diagnosis

Deep learning (DL) has been widely developed and applied in gearbox fault diagnosis of smart manufacturing systems due to its powerful feature representation ability. However, typical DL-based fault diagnosis approaches do not correlate the domain information or mechanism knowledge into the neural network learning process. This causes issues for the reliability and stability of fault diagnosis engineering applications. To address this problem, we propose a novel interpretable DL model named Multi-Wavelet Kernel Convolution Neural Network (MWKCNN) for fault diagnosis in this article. First, a feature extraction layer named the Multi-Wavelet Kernel Convolution (MWKC) layer is constructed based on the continuous wavelet transform (CWT) to locate and detect the impulse signatures of faults. It can capture sufficient feature representations by involving signal processing knowledge. Then, to balance the influence of different wavelet kernels, we design a kernel weights recalibration (KWR) module to dynamically assign different weights to different wavelet kernels. Finally, the performance of the proposed method is validated using two gearbox datasets. In addition, we illustrate the interpretability of the proposed method by analyzing the peculiarity of different wavelet kernels, the variation of kernel weights, and the visualization of learned features.

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis.

An Improved MSCNN and GRU Model for Rolling Bearing Fault Diagnosis

In this paper, a novel fault diagnosis method based on the fusion of squeeze and excitation-multiscale convolutional neural networks (SENet-MSCNN) and gate recurrent unit (GRU) is proposed to address the problem of low diagnosis rate caused by the fact that normal samples are much larger than fault samples in the vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SENet-MSCNN. The temporal features extracted by GRU in order to bring them into the fully connected layer for identification so as to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis. The results reveal that the model can reach 98.98 % and 76.44 % migration diagnostic accuracy in bearing and gearbox datasets. At the same time, it has strong noise immunity, adaptivity, and robustness, providing an effective way for intelligent diagnosis of rolling bearing vibration big data.

Speed adaptive gate: A novel auxiliary branch for enhancing deep learning-based rotating machinery fault classification under varying speed conditions

Rotating machines like wind turbines often work under varying speed conditions. Faults may occur in these machines as time goes on. It is important to detect faults early and classify the type and the severity of an occurred fault, so that timely maintenance could be scheduled. This paper attempts to improve the fault classification accuracy of existing deep learning models when they are used for the fault classification of rotating machinery that operates under varying speed conditions. To achieve this goal, we firstly investigate the effects of speed variation from the perspective of deep learning. We find that the fault information in vibration signals is imbalanced due to speed variation. The imbalance future deteriorates the fault classification accuracy of deep learning models. That is, the accuracy does not evenly distribute across speed but increases with speed. We then propose an auxiliary branch named speed adaptive gate (SAG) for existing deep learning models to address the speed induced fault information imbalance. The SAG takes the speed signal as the input and outputs speed adaptive gate values. The gate values control the information flow of deep learning models so that the effects of speed variation are mitigated. Case studies with two baseline models, i.e., a convolutional neural network (CNN) and a residual network (ResNet), over two experimental datasets, i.e., a planetary gearbox dataset and a fixed-shaft gearbox dataset, have validated the effectiveness of the proposed SAG and its superiority over existing approaches.

Fault Diagnosis of Rotating Machinery Based on 1D–2D Joint Convolution Neural Network

Convolutional neural network has been widely used in fault diagnosis of mechanical devices. In particular, two-dimensional convolutional neural network requires manual selection of multi-scale transformation to transform vibration signal into two-dimensional structure. Although one-dimensional convolution neural network can directly use the vibration signal for convolution processing, it can't make full use of the nonlinear information in one-dimensional space. In order to make full use of the advantages of one-dimensional and two-dimensional convolutional neural networks, this paper develops a one-dimension in tandem with two-dimension joint convolutional neural network (1D-2D JCNN) for rotating machinery fault diagnosis. More specifically, one-dimensional convolution is employed to adaptively obtain the multi-scale feature vectors of the vibration signal, and these feature vectors are constructed into two-dimensional maps, and then these two-dimensional vectors are used as the input of the two-dimensional convolutions neural network. Take the cross-entropy loss function as the loss function, and use the error back propagation algorithm to optimize the filter parameters of the 1D-2D JCNN model to obtain the final fault diagnosis model. Using the motor bearing data set and the worm gearbox data set, the experimental results show the excellent classification performance of bearings and gears under different working conditions. The average diagnostic accuracy of 10 runs on CWRU bearing data set is 99.92%, and the variance is 3.96e-6. The average diagnostic accuracy of 10 runs on worm gear data set is 99.82%, and the variance is 7.82e-6. Compared with the traditional fault diagnosis model and the latest convolution neural network method, the 1D-2D JCNN shows better diagnosis performance.

Multibranch and Multiscale Dynamic Convolutional Network for Small Sample Fault Diagnosis of Rotating Machinery

Deep learning methods have been widely used in the field of fault diagnosis of rotating machinery. As one of the deep learning methods, multi-scale convolutional neural network (MSCNN) can achieve accurate fault diagnosis by extracting feature information of vibration signals at different scales. However, in practical applications, a small number of fault samples limit the performance of MSCNN. To overcome this problem, a small sample rotating machinery fault diagnosis method based on multi-branch and multi-scale dynamic convolutional network (MBSDCN) is proposed. Firstly, a feature splitting strategy is proposed, which splits a single input into multi-branch inputs to ensure that each multi-scale convolutional layer is matched to the appropriate input. Secondly, a channel reconstruction (CR) attention mechanism is designed, and CR attention mechanism can form a direct mapping of high-dimensional space with the fully connected layer through the reconstruction processing of channels, thus making the connection between feature channels closer. Finally, a novel multi-scale feature extraction model is constructed, in which multi-scale convolutional layers are applied to extract multi-branch input features, and the CR attention mechanism is used to calibrate the contribution of multi-scale convolution layers. The performance of MBSDCN is evaluated with CWRU rolling bearing dataset and UOC gearbox dataset and is compared with some advanced deep learning methods. The comparison results indicate that the proposed MBSDCN can accurately diagnose faults under small sample conditions. In addition, the experimental results in noisy environments indicate that the proposed MBSDCN has satisfactory anti-noise performance under small sample conditions and noisy environments.