Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model's robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.

Intelligent fault diagnosis for unbalanced battery data using adversarial domain expansion and enhanced stochastic configuration networks

Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring operation of machinery, and it has been facilitated by the growing popularity of convolutional neural networks (CNNs). The outstanding performance of fault diagnosis CNNs results from complex and redundant network structures and parameters, resulting in huge storage and computational requirements, which makes it challenging to implement these models in resource-limited industrial devices. This study aims to address this problem by proposing a comprehensive compression method for CNNs that is applied to intelligent fault diagnosis. It involves several different compression methods, including tensor train decomposition, parameter quantization, and knowledge distillation for deep network compression. This results in a significant decrease in redundancy and speeding up the training of CNN models. Firstly, tensor train decomposition is applied to reduce redundant connections in both convolutional and fully connected layers. The next step is to perform parameter quantization to minimize the bits needed for parameter representation and storage. Finally, knowledge distillation is used to restore accuracy to the compressed model. The effectiveness of the proposed approach is confirmed by an experiment and ablation study with different models on several datasets. The results show that it can significantly reduce redundant information and floating-point operations with little degradation in accuracy. Notably, on the CWRU dataset, with about 60% parameter reduction, there is no degradation in our model’s accuracy. The proposed approach is a new attempt at the intelligent fault diagnosis of rolling bearings in industrial equipment.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Fault diagnosis

In recent years, convolutional neural network (CNN) has been studied extensively in synthetic aperture radar (SAR) ship detection for its powerful feature extraction and classification capability, and has significantly improved the accuracy and robustness in condition of large scenes, multi-resolutions, and complex backgrounds. At present, a lot of related research focuses on design more efficient net structures. There have not been studies on how the detection performance varies for images with different complexity, backgrounds, surroundings, and quality. Taking two open-access SAR datasets, i.e. SSDD and UCAS_SARShip, as our source datasets, this paper first divides all the images into four classes according to their surroundings and backgrounds, then, dividing each class of images to training subset, validating subset, and test subset based on their features (image quality, noise level, complexity, etc.). Secondly, a large number of SAR ship detection experiments are conducted with five representative CNN based detectors on the four datasets we made. The experimental results show that detection preference is serious for each of the five detectors when they were trained with one of the four datasets. The research in this paper is a benefit for peers to understand and analyze detection preference, besides provide some valuable reference for the collection and division of SAR datasets for ship detection in the following research.

Part-based methods have seen popular applications for face verification in the wild, since they are more robust to local variations in terms of pose, illumination, and so on. However, most of the part-based approaches are built on hand-crafted features, which may not be suitable for the specific face verification purpose. In this paper, we propose to learn a part-based feature representation under the supervision of face identities through a deep model that ensures that the generated representations are more robust and suitable for face verification. The proposed framework consists of the following two deliberate components: 1) a deep mixture model (DMM) to find accurate patch correspondence and 2) a convolutional fusion network (CFN) to extract the part-based facial features. Specifically, DMM robustly depicts the spatial-appearance distribution of patch features over the faces via several Gaussian mixtures, which provide more accurate patch correspondence even in the presence of local distortions. Then, DMM only feeds the patches which preserve the identity information to the following CFN. The proposed CFN is a two-layer cascade of convolutional neural networks: 1) a local layer built on face patches to deal with local variations and 2) a fusion layer integrating the responses from the local layer. CFN jointly learns and fuses multiple local responses to optimize the verification performance. The composite representation obtained possesses certain robustness to pose and illumination variations and shows comparable performance with the state-of-the-art methods on two benchmark data sets.

The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

Rail fastener condition is crucial for rail vehicle safety. However, due to the high physical costs and low inspection accuracies that characterise traditional inspection tasks, intelligent fault diagnosis is becoming an important option among current mechanical system maintenance methods. Deep learning models, such as the convolutional neural network (CNN), have been successfully applied to fault diagnosis tasks with good results. However, considering the large sizes, long detection times, and high requirements for field equipment hardware when using general neural networks, this paper proposes a rail fastener detection method based on improvements to the lightweight network ShuffleNet v2. This method effectively increases the detection accuracy and efficiency for rail fastener. First, the faulty rail fastener sample dataset is expanded using data augmentation and labelled for use as network input. Then, the selective kernel attention mechanism is integrated into the basic unit to improve its channel focus, and a new optimiser, Ranger21, is adopted into the model to solve the problem of AdamW instability in the initial state, thereby increasing the fault classification detection accuracy. Experimental results show that the detection accuracy and precision of the improved model increased by 1.45% and 2.00%, respectively, which verifies the effectiveness of the algorithm.

Deep-learning (DL)-based intelligent fault diagnosis (IFD) methods are extensively used in mechanical systems. In practical industrial contexts, DL models are constrained by three primary limitations: 1) the performance of DL models heavily relies on a significant volume of high-quality labeled data during the training process; 2) the diagnostic capabilities of DL models are insufficient to handle the complex and variable conditions encountered in mechanical systems; and 3) current DL-based IFD approaches primarily focus on diagnosing coarse-grained faults, while less attention is given to fine-grained fault detection. In response to these limitations, a new meta-transfer spiking neural network (MTSNN) is introduced for cross-machine fine-grained fault diagnosis in mechanical equipment. First, a convolutional spiking neural network (CSNN) is constructed to extract the representative features from raw signals by leveraging the low energy consumption, native ability to process time-series data, and high robustness to noise of spiking neural network (SNN). Second, a novel comprehensive loss function called meta-prototype contrastive loss is proposed, which integrates prototypical learning (PL) loss, meta contrastive learning (MCL) loss, and entropy information maximization (EIM) loss, allowing the model to effectively transfer diagnostic knowledge across different machines in limited samples scenario. The experimental evaluation conducted on three datasets reveals that the proposed MTSNN significantly outperforms other methods in diagnosing faults, particularly when applied to different machines with heterogeneous fault categories.

To find out the hidden danger in the industrial production process in time, it is necessary to monitor the health condition of the key components of the mechanical system in operation. However, traditional fault diagnosis methods usually adopt manual feature extraction, which not only costs expensively and depends on prior knowledge. Therefore, it is of great significance to study the application of automatic fault identification based on the original vibration signals. Recently, existing studies have shown that most of fault diagnoses are implemented by using deep neural network. Although these methods have achieved satisfactory performances, there are obvious limitations in real applications, that is, the complexity of deep neural network requires a lot of hardware computing resources. This hinders the development of online fault diagnosis tools. To solve this problem, this paper proposes a fault diagnosis model based on lightweight convolutional neural network MobileNet, and realizes an end-to-end intelligent fault classification and diagnosis application. We evaluated the proposed method with the rolling bearing dataset from Western Reserve University. The best average precision, recall and F1 score of ten different bearing health conditions are about 96%, 82% and 88%, respectively. In addition, we also compare the accuracy of the rolling bearing fault diagnosis classification model under the standard ReLU and the improved ReLU. Experimental results show that both obtain good performances, but the improved ReLU reaches the over 96% accuracy more rapidly.

ABSTRACTIn order to maintain the safe operation of various types of equipment, the health status of main components should be monitored in real-time, and the demand for intelligent fault diagnosis algorithm has increased sharply. However, the traditional intelligent diagnosis algorithm is based on the manual method for signal feature extraction, which has high requirements for expert experience and poor generality. A convolutional neural network, with big data as its engine, is the most effective pattern classification algorithm at present. In this paper, the convolutional neural network is applied to time-domain vibration signal fault diagnosis, taking the bearing as an example, and an intelligent diagnosis method of bearing based on convolution neural network is proposed. The proposed method does not need manual feature extraction, and can automatically complete feature extraction and automatic fault recognition. The convolutional neural network has three convolutional layers. We use data enhancement techniques for the input raw signal and convert the one-dimensional original time-domain vibration signal into a two-dimensional signal. The model shows good results on CWRU dataset and the recognition accuracy of the algorithm in the CWRU bearing database is more than 96%.

This paper presents an enhanced ensemble classification framework for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) under diverse operational conditions, including Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed method integrates the strengths of Residual Neural Networks (ResNet) replacing Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and template matching, leveraging majority voting to combine their complementary capabilities. The ensemble framework achieves improved robustness and classification accuracy across varied scenarios. The methodology employs ResNet, a deep learning architecture known for its superior feature extraction and classification capabilities, replacing AlexNet to address limitations in generalization and consistency. ResNet demonstrated better performance with average accuracies of 92.67% under SOC and 88.9% under EOC, showing consistent results across all six target classes, as compared to the CNN-based ensemble approach with average accuracies of 90.30% under SOC and 87.22% under EOC. The SVM is employed for its robustness in handling overfitting and classifying features extracted from 16 region properties. Template matching is included for its resilience in challenging conditions where deep learning techniques may underperform. Experimental validation using the MSTAR dataset, a standard benchmark for SAR ATR, highlights the effectiveness of this ensemble approach. The results confirm significant improvements in classification accuracy and robustness over individual classifiers, demonstrating the practical applicability of the ensemble approach to real-world SAR ATR challenges. This research advances SAR ATR by addressing critical challenges, including noise, occlusion, and variations in viewing angles while achieving high classification performance under diverse conditions. The integration of ResNet further enhances the framework’s adaptability and reliability.

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.

Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset