Search Results (33)

In view of the data of fault diagnosis and good product testing in the industrial field, high-noise unbalanced data samples exist widely, and such samples are very difficult to analyze in the field of data analysis. The oversampling technique has proved to be a simple solution to unbalanced data in the past, but it has no significant resistance to noise. In order to solve the binary classification problem of high-noise unbalanced data, an enhanced majority-weighted minority oversampling technique, MWMOTE-FRIS-INFFC, is introduced in this study, which is specially used for processing noise-unbalanced classified data sets. The method uses Euclidean distance to assign sample weights, synthesizes and combines new samples into samples with larger weights but belonging to a few classes, and thus solves the problem of data scarcity in smaller class clusters. Then, the fuzzy rough instance selection (FRIS) method is used to eliminate the subsets of synthetic minority samples with low clustering membership, which effectively reduces the overfitting tendency of minority samples caused by synthetic oversampling. In addition, the integration of classification fusion iterative filters (INFFC) helps mitigate synthetic noise issues, both raw data and synthetic data noise. On this basis, a series of experiments are designed to improve the performance of 6 oversampling algorithms on 8 data sets by using the MWMOTE-FRIS-INFFC algorithm proposed in this paper. Full article

At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model’s ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults. Full article

A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions. Full article

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition. Full article

Intelligent fault diagnosis encounters the challenges of varying working conditions and sample class imbalance individually, but very few approaches address both challenges simultaneously. This article proposes an improvement network model named ICDAN-F, which can deal with fault diagnosis scenarios with class imbalance and working condition variations in an integrated way. First, Focal Loss, which was originally designed for target detection, is introduced to alleviate the sample class imbalance problem of fault diagnosis and emphasize the key features. Second, the domain discriminator is improved by the default ReLU activation function being replaced with Tanh so that useful negative value information can help extract transferable fault features. Extensive transfer experiments dealing with varying working conditions are conducted on two bearing fault datasets with the effect of class imbalance. The results show that the fault diagnosis performance of ICDAN-F outperforms several other widely used domain adaptation methods, achieving 99.76% and 96.76% fault diagnosis accuracies in Case 1 and Case 2, respectively, which predicts that ICDAN-F can handle both challenges in a cohesive manner. Full article

The feedback spring rod of the armature assembly was eliminated in the double-redundancy electro-hydraulic servo valve (DREHSV), which employed a redundant design in contrast to the typical double-nozzle flapper electro-hydraulic servo valve (DNFEHSV). The pilot stage was mainly composed of four torque motors, and the double-system spool was adopted in the power stage. Consequently, the difficulty of spool displacement control was increased. By artificially changing the structural parameters of the simulation model in accordance with the theoretical analysis through AMESim, this paper aimed to study the dynamics and static characteristics of the DREHSV. The advantage of redundant design was further demonstrated by disconnecting working coils and setting the different worn parts of the spool. On the test bench, the necessary experiments were performed. Through simulation, it was discovered that when the clogged degree of the nozzle is increased, the zero bias value increases, the pressure and flow gain remain unchanged, and the internal leakage decreases. The pressure gain changes very little, the flow gain close to the zero position grows, the zero leakage increases significantly, and the pilot stage leakage changes very little as a result of the wear of the spool throttling edge. The basic consistency between the simulation curves and the experimental findings serve to validate the accuracy of the AMESim model. The findings can serve as a theoretical guide for the design, debugging, and maintenance of the DREHSV. The simulation model is also capable of producing a large amount of sample data for DREHSV fault diagnosis using a neural network. Full article

To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data from other devices. In light of this, this paper proposes a novel network architecture, named Signal Bootstrap Your Own Latent (SBYOL), which utilizes unlabeled vibration signals to address the challenging issues of variable working conditions, strong noise, and limited data in rotating machinery fault diagnosis. The architecture consists of a self-supervised pre-training-based fault feature recognition network and a diagnosis network based on knowledge transfer. The fault feature recognition network uses ResNet-18 as the backbone network for self-supervised pre-training and transfers the trained fault feature extractor to the target diagnostic object. Additionally, a unique vibration signal data augmentation technique, time–frequency signal transformation (TFST), is proposed specifically for rotating machinery fault diagnosis, which addresses the key task of contrastive learning and achieves high-precision fault diagnosis with very few labeled samples. Experimental results demonstrate that the proposed diagnostic model outperforms other methods in both extremely limited sample and strong noise scenarios and can transfer unlabeled data utilization between similar and even different device types. Full article

Rolling bearings are one of the most widely used parts in all kinds of rotating machinery (including wind power equipment) and also one of the most easily damaged parts, which makes fault diagnosis of rolling bearings a promising research field. To this end, recent studies mainly focus on fault diagnosis cooperating with deep learning. However, in practical engineering, it is very challenging to collect massive fault data, resulting in low accuracy of bearing fault classification. To solve the problem, an auxiliary classifier optimized by a principal component analysis method is proposed to generate an adversarial network model in which Wasserstein distance and gradient penalty are used to improve the stability of the network training process in case of over-fitting and gradient disappearance during model training. Specifically, we implement the model system using two main components. First, the one-dimensional time domain signal is transformed into a two-dimensional grayscale image and the principal component analysis is employed to reduce the dimension of the original data; this is instead of random noise as the input of the generator thereby preserving the characteristics of the original data to a certain extent. Second, in a generative adversarial network, the label information of the fault data is inserted into the generator to achieve supervised learning, thereby improving the data generation performance and reducing the training time cost. The experimental results show that our model could produce high-quality samples that are similar to real samples and that it could significantly improve the classification accuracy of fault diagnosis in the case of insufficient fault samples. Full article

Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity. Full article

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method. Full article

It is extremely dangerous for a turbocharged boiler to have a leakage fault in its vaporization tube. However, early detection and fault diagnosis of micro-leakage faults are very difficult. On the one hand, there are few fault samples that lead to a difficult and intelligent diagnosis. On the other hand, the system fault response characteristics of the characterization parameters in the process are complex and easily confused with the load-changing characteristics. In order to obtain fault samples and identify fault characteristics, a fault simulation model for the micro-leakage of the boiler evaporation tube is established based on the dynamic mathematical model of all working conditions. The model’s effectiveness is verified by typical fault experiments. The dynamic simulation experiments of three kinds of micro-leakage and four kinds of load changing were carried out. Through the analysis of combustion equilibrium and vapor-liquid equilibrium of 14 groups of characterization parameters, it is found that: (1) The reason for the poor discriminability in micro-leakage faults is that most of the characterization parameters tend to balance after 300 s and the dynamic response characteristics are similar to those of load increase. (2) There are four highly distinguishable parameters: the speed of the turbocharger unit, the air supply flow, the flue gas temperature at the superheater outlet, and the furnace pressure. When the micro-leakage fault is triggered, the first three parameters have a large disturbance. They show a trend of decreasing first and then increasing in short periods, unlike normal load-changing conditions. The fourth parameter (furnace pressure) rises abnormally fast after failure. (3) Under the normal working condition of varying loads, the main common parameters take 300 s to stabilize; the common stability parameter values should be recorded because when the micro-leakage fault of evaporation occurs, the steady-state increment of failure is larger than the normal steady increment under variable load conditions, by 2 to 3 times. (4) As the leakage fault increases, the disturbance amplitude of the characteristic parameters becomes larger. In addition, the stability of the steam system becomes worse, and fault discrimination becomes more obvious. Full article

At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method. Full article

Grid-connected inverters are the core equipment for connecting marine energy power generation systems to the public electric utility. The variation of current sensor fault severity will make fault samples multimodal. However, linear discriminant analysis assumes that the same fault is independent and identically distributed. To solve this problem, this paper proposes a layering linear discriminant analysis method based on traditional linear discriminant analysis. The proposed method divides the historical fault data based on the sensor fault severity layer-by-layer until the distribution of the same fault category in each subset is very close. Linear discriminant analysis is used to analyze historical fault data in each subgroup, and the kappa coefficient is applied as the basis for ending the training process. A BP neural network is employed to estimate the fault severity during the testing process, and the fault diagnosis sub-model is selected. The proposed method enables the accurate diagnosis of faults with different distributions in the same category and provides an accurate estimate of the sensor’s fault severity degree. The estimated value of the sensor’s fault degree can provide critical information for the maintenance of the equipment and can be used to correct the sensor’s output. Full article

Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance. Full article

The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Fault diagnosis of wind turbines is beneficial to improve the reliability of wind turbines. Due to various reasons, such as difficulty in obtaining fault data, random changes in operating conditions, or compound faults, many deep learning algorithms show poor performance. When fault samples are small, ordinary deep learning will fall into overfitting. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples. A novel method based on meta-analogical momentum contrast learning (MA-MOCO) is proposed in this paper to solve the problem of the very few samples of wind turbine failures, especially one-shot. By improving the momentum contrast learning (MOCO) and using the training idea of meta-learning, the one-shot fault diagnosis of wind turbine drivetrain is analyzed. The proposed model shows a higher accuracy than other common models (e.g., model-agnostic meta-learning and Siamese net) in one-shot learning. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) in order to test the effectiveness of the proposed model. Full article