A novel graph representation framework for bearing fault diagnosis via wavelet packet decomposition and Gramian angular field weighting

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices.

Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals. In this technique, 10 categories of 1D vibration signals from rolling bearings are sampled using a sliding window approach. The sampled data is then subjected to wavelet packet decomposition (WPD), and the wavelet energy from the final layer of the four-level WPD decomposition in each frequency band is used as the node feature. The weights of edges between nodes are calculated using the Pearson correlation coefficient (PCC) to construct a node graph that describes the feature information of rolling bearings under different health conditions. Data augmentation of the node graph in the dataset is performed by randomly adding nodes and edges. The graph convolutional neural network (GCN) is employed to encode the augmented node graph representation, and deep graph contrastive learning (DGCL) is utilized for the pre-training and classification of the node graph. Experimental results demonstrate that this method outperforms contrastive learning-based fault diagnosis methods for rolling bearings and enables rapid fault diagnosis, thus ensuring the normal operation of mechanical systems. The proposed WPDPCC-DGCL method offers two advantages: (1) the flexibility of wavelet packet decomposition in handling non-smooth vibration signals and combining it with the powerful multi-scale feature encoding capability of GCN for richer characterization of fault information, and (2) the construction of graph node-level fault samples to effectively capture underlying fault information. The experimental results demonstrate the superiority of this method in rolling bearing fault diagnosis over contrastive learning-based approaches, enabling fast and accurate fault diagnoses for rolling bearings and ensuring the normal operation of mechanical systems.

In actual engineering production, bearings typically operate in harsh environments. The fault features of bearing vibration signals are often submerged by background noise, making it difficult to extract the fault signal features and impacting the accuracy of fault diagnosis. To address this problem, this paper proposes a bearing fault diagnosis method based on the Savitzky-Golay Gramian Angular Field (GAF) with fault feature enhancement combined with ResNet. First, the acquired vibration signals are segmented, and the segmented signals are subjected to Butterworth high-pass filtering to obtain the high-frequency components of the signals that contain fault information. Secondly, the extracted high-frequency components are boosted by the S-enhancement algorithm for fault features. The boosted signals are then filtered by Savitzky-Golay to achieve data smoothing aggregation enhancement. Subsequently, the featureenhanced GAF graphs are obtained using the transformation method. Finally, bearing fault diagnosis is performed using the Glamian Angle field diagram as input to the ResNet18 model. To verify the feasibility of the proposed method, experiments were conducted using Case Western Reserve University (CWRU) bearing fault dataset and bearing fault dataset of laboratory experimental platform. The experimental results showed that the fault diagnosis accuracy were 99.28% and 100%, respectively. The results validated the feasibility of the proposed method. Through comparative experiments with the Symmetric Dot Pattern (SDP) method, the traditional GAF method and the Recurrence Plots (RP) method, the results demonstrate that the proposed method has high diagnostic accuracy, proved the effectiveness of the method. INDEX TERMS Bearings fault diagnosis, Savitzky-Golay filtering, Gramian angle field, Fault feature enhancement, Residual network.

Traditional fault diagnosis methods often require extracting features from raw vibration signals based on prior knowledge, which are then input into intelligent classifiers for pattern recognition. This process is prone to information loss and can be inaccurate when relying on human experience for fault identification. To address this issue, this paper proposes an intelligent fault classification and diagnosis model for rolling bearings based on Fast Fourier Transform (FFT) combined with a time convolutional network (SE-TCN) incorporating an attention mechanism, with a Support Vector Machine (SVM) used as the classifier. First, the FFT is applied to transform the collected raw time-domain data of bearing faults into the frequency domain, obtaining the sequence information in the frequency domain. Second, the frequency–domain sequence data are fed into the SE-TCN model, which uses multiple convolutional layers and a channel attention mechanism to extract deep fault features. Finally, the extracted feature vectors are input into the SVM classifier, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the SVM parameters. The optimal separating hyperplane is obtained through training to classify the fault types of the rolling bearings. To verify the effectiveness and diagnostic performance of the proposed method, experiments are conducted using bearing fault datasets from Case Western Reserve University (CWRU) and a laboratory self-built fault diagnosis experimental platform. The experimental results show that the classification accuracy of the proposed method exceeds 99% on the CWRU test dataset, and it also demonstrates advantages in handling small sample data, with an accuracy of over 90%. Additionally, it exhibits good diagnostic performance on the bearing fault data collected from the laboratory self-built platform. The results validate the effectiveness of the proposed classification model in bearing a fault diagnosis.

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper.

Fault diagnosis methods for rolling bearings based on deep learning have become a research hotspot. However, these methods mostly use convolutional neural networks (CNNs), which have the problem of gradient dispersion or disappearance as the network deepens. Moreover, directly converting vibration signals into images as network input cannot preserve the temporal correlation between signals. In the case of small datasets and complex and variable working conditions, the accuracy of fault diagnosis is low and the generalisation ability is poor. To solve the above problems, a rolling bearing fault diagnosis method based on the Gramian angular field (GAF) and an SE-ResNeXt50 transfer learning model is proposed. Firstly, the parameters of the GAF obtained from multiple experiments are selected and the one-dimensional time-series vibration signal is encoded by combining the data enhancement method, and converted into a Gramian angular difference field (GADF) diagram and a Gramian angular sum field (GASF) diagram with local time information and uniqueness. Then, a fine-tuning transfer learning strategy is used to transfer the pre-trained model parameters to an SE-ResNeXt50 model, which improves the training speed of the model and improves the overfitting problem of the model on small target datasets. Finally, the GAF diagram is used as the input to the model and a feature recalibration strategy is used to adaptively obtain the importance of each feature channel, which further improves the feature utilisation. To verify the effectiveness and superiority of the proposed method, the rolling bearing data from Case Western Reserve University are selected for experimental verification and the generalisation performance of the proposed method is tested under varying loads and different dataset scales. The results show that when there is only a small amount of data, the proposed method can still achieve high diagnosis accuracy for different loads and has better recognition accuracy and generalisation compared to other fault diagnosis methods.

The most common cause of electric motor failure is the bearings, and so methods for fast and accurate diagnosis of motor bearing failure are urgently needed. Traditional fault diagnosis methods have high uncertainty and complexity since they require manual extraction of features. Deep learning has shown good performance in electrical equipment fault detection, and it can directly complete end-to-end diagnosis of motor faults, avoiding human involvement. Here, a new fault diagnosis method is presented which combines Gramian angular field (GAF) image coding, extreme learning machine (ELM) and convolutional neural network (CNN). The method has three main stages: First of all, GAF is utilized to convert the acquired vibration break signals into 2-D pictures. Next, the enhanced CNN model is taken to identify the elements of the converted image quickly and accurately. Finally, the ELM is used as the final classifier to gain further accuracy and diagnostic speed of fault classification. Experiments were designed to validate the proposed method using two different motor bearing fault datasets at Case Western Reserve University and autonomous experiment and performance is compared with several commonly used intelligent diagnosis algorithms. The proposed method’s accuracy in the experiment designed in this paper can reach 99.2% at most, and it only takes 0.835s to complete the diagnosis, which outperforms traditional diagnostic methods on both datasets and improving the maximum diagnostic accuracy by 33.6%. The findings indicate that this method can classify various fault types efficaciously, and has the benefits of quick diagnosis, high accuracy, and good generalization ability.

Near-infrared spectroscopy analysis of compound fertilizer based on GAF and quaternion convolution neural network

Given that the manual feature selection process is troublesome and not sufficiently accurate, an integrated learning method based on Gramian Angular Field (GAF) and optimal feature channel adaptive selection is proposed when designing the rolling bearing fault diagnosis model. First, the GAF transformation is performed on the raw vibration signal compressed by using the Piecewise Aggregation Approximation (PAA) technique, and the vibration signals of different states are encoded into different types of GAF images. Then convolutional channel attention residual network (CCARN) is used to learn advanced features from GAF images and the fault diagnosis results are produced. Finally, in order to further improve the stability of the proposed fault diagnosis method, an integrated learning method based on hierarchical scoring strategy is proposed. The reliability of the output results of diagnostic model is further enhanced through the voting decision results of multiple models. Experimental results show that the proposed method has good classification performance on rolling bearing data, and outperforms those state-of-the-arts.

Rolling element bearings are one of the most critical components of rotating machinery, with bearing faults amounting up to 50% of the faults in electrical machines. Therefore, the bearing fault diagnosis has attracted attention of many researchers. Typically, the bearing fault diagnosis is performed using vibration signals from the machine. In addition, by using deep learning algorithms on the vibration signals, the fault detection accuracy close to 100% can be achieved. However, measurement of vibration signals requires an additional sensor, which is not present in majority of the machines. Nevertheless, with an alternative approach, the stator current can be used for diagnosis. Therefore, the paper emphasizes on the diagnosis of bearing faults using the stator current. The diagnosis requires signal processing for the fault signature extraction that is buried underneath the noise in the current signal. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway and real damaged outer raceway bearings with different fault severity. For fault signature extraction from the current signals, the redundant frequencies in the signals are filtered, then from the filtered signals eight features are extracted from the time and time-frequency domain by using the wavelet packet decomposition (WPD). Then, using these features and the well known deep learning algorithm Long Short-Term Memory (LSTM), bearing fault classification is made. The deep learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 96%, which outperforms most of the present algorithms that perform bearing fault diagnosis using stator current. The method developed is independent of the speed and the loading conditions.

Rolling element bearings are very important components in electrical machines. Almost 50% of the faults that occur in the electrical machines occur in the bearings. This makes bearings as one of the most critical components in electrical machinery. Bearing fault diagnosis has drawn the attention of many researchers. Generally, vibration signals from the machine’s accelerometer are used for the diagnosis of bearing faults. In literature, application of Deep Learning algorithms on these vibration signals has resulted in the fault detection accuracy that is close to 100%. Although, fault detection using vibration signals from the machine is ideal but measurement of vibration signals requires an additional sensor, which is absent in many machines, especially low voltage machines as it significantly adds to its cost. Alternatively, bearing fault diagnosis with the help of the stator current or Motor Current Signal (MCS) is also gaining popularity. This paper uses MCS for the diagnosis of bearing inner raceway and outer raceway fault. Diagnosis using MCS is difficult as the fault signatures are buried beneath the noise in the current signal. Hence, signal-processing techniques are employed for the extraction of the fault features. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway, and real damaged outer raceway bearings with different fault severity. Fault features are extracted from MCS by first filtering out the redundant frequencies from the signal and then extracting eight features from the filtered signal, which include three features from time domain and five features from time–frequency domain by using the Wavelet Packet Decomposition (WPD). After the extraction of these eight features, the well-known Deep Learning algorithm Long Short-Term Memory (LSTM) is used for bearing fault classification. The Deep Learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 97%. A comparison of the proposed algorithm is done with the traditional Machine Learning techniques, and it is shown that the proposed methodology outperforms all the traditional algorithms which are used for the classification of bearing faults using MCS. The method developed is independent of the speed and the loading conditions.

With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.

Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

A lightweight neural network fault diagnosis method based on Gramian angular field (GAF) feature map construction and efficient channel attention (ECA) optimization is presented herein to address the problem of the complex structure of traditional neural networks in bearing fault diagnosis. Firstly, a GAF is used to encode vibration signals into a temporal image. Secondly, the double-layer separation residual convolution neural network (DRCNN) is used to learn advanced features of the sample. The multi-branch structure is used as the receiving domain. ECA learns the correlation between feature channels. The extracted feature channels are adaptively weighted by adding a small additional computational cost. Finally, the method is tested and evaluated using wind turbine bearing data. The experimental results show that, compared with the traditional neural network, the DRCNN model based on GAF achieves higher diagnostic accuracy with less parameter calculation.

Aiming at reducing the classification accuracy caused by unbalanced sample data in bearing fault diagnosis, we present a bearing fault diagnosis method based on wavelet packet and fuzzy support vector machine (FSVM). In the actual sampling environment, the bearing fault sample is difficult to obtain, the data between the normal sample and the fault sample are not balanced, and the bearing is easily caused by the difference of the factors such as the personnel operation, the environment temperature, and the inherent noise of the machine. Therefore, the wavelet packet decomposition technique is used to extract the energy of the bearing vibration signal, and adaptive FSVM is used as a fault diagnosis algorithm to solve the unbalancing problem of training samples. The simulation results show that compared with the standard SVM and FSVM, the method can improve the classification accuracy of bearing fault diagnosis.