Learning-based Fault Diagnosis Methods Research Articles

IFD-BiC: A Class-incremental Continual Learning Method for Diesel Engine Fault Diagnosis

Abstract In recent years, deep learning-based fault diagnosis methods for diesel engines have been developed assuming that a comprehensive dataset is available for one-time learning. However, in reality, it is impractical to obtain a complete dataset all at once. Instead, new data is gradually acquired over time. To address this issue, this paper proposes an incremental fault diagnosis method with bias correction (IFD-BiC). When receiving incremental fault data, the IFD-BiC model incorporates new classifier nodes and updates network parameters using a combination of distillation loss and cross-entropy. During the update process, old samples saved in the exemplar set are replayed, and a bias correction layer is used to correct the prediction bias caused by the imbalance between new and old samples. This enables the model to learn new fault tasks without experiencing catastrophic forgetting. To ensure the applicability of the method to various operating conditions of diesel engines, cyclic angular vibration with consistent length at different speeds is used as the model input. Through experimental validation across a pair of distinct diesel engine models, the proposed method achieves highly accurate fault diagnosis on stream format training data. Moreover, an evaluation against leading incremental learning techniques affirms the proposed method’s advantage. 

Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism

Aircraft sensors are crucial for ensuring the safe and efficient operation of aircraft. However, these sensors are vulnerable to external factors that can lead to malfunctions, making fault diagnosis essential. Traditional deep learning-based fault diagnosis methods often face challenges, such as limited data representation and insufficient feature extraction. To address these problems, this paper proposes an enhanced GraphSage-based fault diagnosis method that incorporates attention mechanisms. First, signal data representing the coupling characteristics of various sensors are constructed through data stacking. These signals are then transformed into graph data with a specific topology reflecting the overall sensor status of the aircraft using K-nearest neighbor and Radius classification algorithms. This approach helps fully leverage the correlations between data points. Next, node and neighbor information is aggregated through graph sampling and attention-based aggregation methods, strengthening the extraction of fault features. Finally, fault diagnosis is performed using multi-layer aggregation and transformation within fully connected layers. Experiments demonstrate that the proposed method outperforms baseline approaches, achieving better detection performance and faster computational speed. The method has been validated on both simulated and real-flight data.

Development of deep reinforcement learning-based fault diagnosis method for actuator faults in unmanned aerial vehicles

Abstract Actuator faults in unmanned aerial vehicles (UAVs) can have significant and potentially adverse effects on their safety and performance, highlighting the critical importance of fault diagnosis in UAV design. Ensuring the reliability of these systems in various applications often requires the use of advanced diagnostic algorithms. Artificial intelligence methods, such as deep learning and machine learning techniques, enable fault diagnosis through sample-based learning without the need for prior knowledge of fault mechanisms or physics-based models. However, UAV fault datasets are typically small due to stringent safety standards, which presents challenges for achieving high-performance fault diagnosis. To address this, deep reinforcement learning (DRL) algorithms offer a unique advantage by combining deep learning’s automatic feature extraction with reinforcement learning’s interactive learning approach, improving both learning capabilities and robustness. In this study, we propose and evaluate two DRL-based fault diagnosis models, which demonstrate remarkable accuracy in fault diagnosis, consistently exceeding $99{\rm{\% }}$ . Notably, under small sample scenarios, the proposed models significantly outperform traditional classifiers such as decision trees, support vector machines, and multilayer perceptron neural networks. These findings suggest that the integration of DRL enhances fault diagnosis performance, particularly in data-limited environments.

A diesel engine valve leakage fault diagnostic method using modified VGG19 and transfer learning

ABSTRACT Traditional deep learning-based fault diagnosis methods for diesel engine valve leakage, often rely on large datasets. This study proposes the GAP-VGG19-BN method based on VGG19 and transfer learning. Global average pooling (GAP) is adopted to replace the full connection layer and improve parameter reduction. Batch Normalization (BN) layers are set after each convolution layer to enhance the computational speed. We build a small-scale dataset using the time–frequency and the wavelet methods constructed from the diesel engine cylinder head vibration signal. The GAP-VGG19-BN is pre-trained through ImageNet; then, the well-trained parameters are transferred onto the constructed dataset. The BN method is adopted to make the learning rate approach obtain an optimal global solution of learning rate. Experimental results show that the proposed method has higher accuracy, stronger robustness, less computational cost, and better generalisation for small-scale datasets. The proposed method enriches the fault diagnosis method of diesel engine valve leakage.

Research on hydraulic system fault diagnosis method based on machine learning

Abstract This study proposes a machine learning-based fault diagnosis method for hydraulic systems, focusing on using K-means clustering algorithm for data preprocessing, use Support Vector Machine (SVM) and Convolutional Neural Network Gated Recurrent Unit (CNN-GRU) models respectively for fault classification. By performing cluster analysis on sensor data, the data dimensions can be effectively reduced and the efficiency of fault analysis can be improved. The results show that the accuracy of the SVM algorithm in the cooler status and valve status classification tasks reached 99.77% and 100.00% respectively. After the introduction of the clustering algorithm, its training time was significantly reduced, showing extremely high real-time fault diagnosis capabilities. The CNN-GRU model performs particularly well in handling complex fault classification tasks, especially in the hydraulic accumulator fault classification task, with an accuracy rate as high as 96.60%, which is significantly better than the SVM model. Although the training time of CNN-GRU is longer, its advantages in complex pattern recognition give it obvious advantages in high-accuracy application scenarios. Multi-fault diagnosis was conducted, and the results show that the CNN-GRU model achieves the best performance without employing clustering, emphasizing the importance of preserving the integrity of the original sensor data in complex fault scenarios.

Research on deep learning-based fault diagnosis methods for aero engine gas path

Research on deep learning-based fault diagnosis methods for aero engine gas path

Dilated dynamic supervised contrastive learning framework for fault diagnosis under imbalanced dataset conditions

Deep learning-based fault diagnosis methods have achieved significant results. However, under actual working conditions, the inability of equipment to operate for long periods in a fault state results in a much smaller amount of collected fault data compared to normal data. This leads to existing fault diagnosis models being able to learn only limited diagnostic knowledge, causing a substantial decline in diagnostic accuracy. Therefore, a dilated dynamic supervised contrast learning framework for imbalanced data scenarios is proposed. In this framework, a dilated convolution network is designed as a feature extractor to enhance the feature extraction ability by changing the dilation rate. Then, the dynamic supervision loss function is designed, and the sample compensation factor of each category is given according to the frequency of sample used in the training process to enhance the contribution of the minority class and optimize the feature extractor. Finally, a dynamic cross-entropy loss function is designed to train the network and classifier to achieve accurate classification of faults. Experiments on two open-source datasets show the effectiveness of the proposed model framework.

A novel multi-sensor local and global feature fusion architecture based on multi-sensor sparse Transformer for intelligent fault diagnosis

A novel multi-sensor local and global feature fusion architecture based on multi-sensor sparse Transformer for intelligent fault diagnosis

A Novel Machine Learning Based Fault Diagnosis Method for All Gas-Path Components of Heavy Duty Gas Turbines With the Aid of Thermodynamic Model

A Novel Machine Learning Based Fault Diagnosis Method for All Gas-Path Components of Heavy Duty Gas Turbines With the Aid of Thermodynamic Model

Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

Digital twin-assisted fault diagnosis framework for rolling bearings under imbalanced data

Digital twin-assisted fault diagnosis framework for rolling bearings under imbalanced data

A review on adversarial–based deep transfer learning mechanical fault diagnosis

Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advancement of artificial intelligence and industrial intelligence, industrial big data and its intelligent analysis provide robust support for fault prediction and health management of equipment. Building on existing research, intelligent diagnostics for mechanical equipment based on deep learning have gained significant attention and application. However, the success of big data relies on comprehensive fault data, which is challenging to obtain in practical applications where continuous equipment operation is essential. Moreover, mechanical equipment often operates under varying conditions, leading to different data distributions for training and testing. This discrepancy can result in low diagnostic accuracy or even failure of deep learning methods. Deep Transfer Learning (DTL) is an emerging machine learning paradigm that not only leverages the advantages of deep learning (DL) in feature representation but also harnesses the strengths of transfer learning (TL) in knowledge transfer. Consequently, DTL techniques can make deep learning-based fault diagnosis methods more reliable, robust, and applicable, leading to extensive development and research in the field of intelligent fault diagnosis. This paper primarily introduces adversarial-based deep transfer learning (ADTL) models, which are fundamentally based on Generative Adversarial Network (GAN). We provide a detailed discussion of the main applications of ADTL and its recent developments in intelligent fault diagnosis, along with some future challenges and prospects.

Simulation data-driven adaptive frequency filtering focal network for rolling bearing fault diagnosis

Simulation data-driven adaptive frequency filtering focal network for rolling bearing fault diagnosis

Time-segment-wise feature fusion transformer for multi-modal fault diagnosis

Time-segment-wise feature fusion transformer for multi-modal fault diagnosis

A Domain Generation Diagnosis Framework for Unseen Conditions Based on Adaptive Feature Fusion and Augmentation

Emerging deep learning-based fault diagnosis methods have advanced in the current industrial scenarios of various working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address the challenge of fault diagnosis under unseen working conditions, a domain generation framework for unseen conditions fault diagnosis is proposed, which consists of an Adaptive Feature Fusion Domain Generation Network (AFFN) and a Mix-up Augmentation Method (MAM) for both the data and domain spaces. AFFN is utilized to fuse domain-invariant and domain-specific representations to improve the model’s generalization performance. MAM enhances the model’s exploration ability for unseen domain boundaries. The diagnostic framework with AFFN and MAM can effectively learn more discriminative features from multiple source domains to perform different generalization tasks for unseen working loads and machines. The feasibility of the proposed unseen conditions diagnostic framework is validated on the SDUST and PU datasets and achieved peak diagnostic accuracies of 94.15% and 93.27%, respectively.

An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples

An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples

Fault Diagnosis of Centrifugal fan Bearings Based on I-CNN and JMMD in the Context of Sample Imbalance

The fault diagnosis is an effective technical means to improve the reliability of centrifugal fan bearings. The serious imbalance of data is one of the important issues facing bearing fault diagnosis.In this paper, a transfer learning-based fault diagnosis method for Centrifugal fan bearings is proposed, utilizing the improved CNN (I-CNN) and Joint Maximum Mean Discrepancy (JMMD) algorithms. The raw vibration signals of the bearings are enhanced through fast Fourier transform for feature representation. The signals are then processed by parallel multi-scale CNNs with an embedded Squeeze-and-Excitation (SE) attention to focus on key features. Furthermore, the JMMD is introduced as a metric for quantifying the disparity between the source and target domains, thereby mitigating domain shift. In the loss function, weight factors and scaling factors are introduced to increase attention on minority samples and easily confused samples within the imbalanced dataset. The proposed method is validated on the Centrifugal fan bearing dataset from Jiangnan University and the CWRU dataset.

A coarse and fine-grained deep multi view subspace clustering method for unsupervised fault diagnosis of rolling bearings

Abstract To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

Few-shot learning fault diagnosis of rolling bearings based on siamese network

Abstract This paper focuses on the fault diagnosis problem in the scenario of scarce bearing samples, facing two main challenges: complex noise background and variations in operating conditions. While deep learning-based fault diagnosis methods have achieved significant progress, they heavily rely on large amounts of samples. This paper proposes a few-shot learning fault diagnosis method based on siamese networks (SN), which classify samples based on the similarity between pairs rather than end-to-end classification. Tested on two bearing datasets, the proposed method outperforms SVM, DCNN, WDCNN, and CNN-BiGRU. The influence of factors such as parameter regularization, noise, and load variation on the proposed method is also discussed. Experimental results demonstrate that double parameter regularization contributes more to the model’s generalization ability, maintaining good stability and generalization even under noise interference or load variation.

A model-data combination driven digital twin model for few samples fault diagnosis of rolling bearings

Abstract Deep learning-based fault diagnosis methods for rolling bearings are widely utilized due to their high accuracy. However, they have limitations under conditions with few samples. To address this problem, a model-data combination driven digital twin model (MDCDT) is proposed in this work for fault diagnosis with few samples of rolling bearings. The simulation signals generated by different fault dynamic models of rolling bearings and the measured signals are mixed through MDCDT. The MDCDT generates virtual signals to bridge the gap between the simulated signals and the measured signals by combining their respective advantages. This paper also proposes image coding method based on the Markov transfer matrix (MTMIC) to convert one-dimensional vibration signals into two-dimensional images with both frequency domain information and time domain information, making it easier to extract fault features in neural network training. In the end, the developed MDCDT was evaluated using real rolling bearing data. Experiments show that the MDCDT can generate virtual data for fault diagnosis, and the fault diagnosis accuracy is significantly improved.