Feature-Level Attention-Guided Multitask CNN for Fault Diagnosis and Working Conditions Identification of Rolling Bearing

Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism

Fault diagnosis of wind turbine bearings is crucial for ensuring operational safety and reliability. However, traditional serial-structured deep learning models often fail to simultaneously extract spatio- temporal features from fault signals in noisy environments, leading to critical information loss. To address this limitation, this paper proposes a Wind Turbine Bearing Fault Diagnosis Model Based on Efficient Cross Space Multiscale CNN Transformer Parallelism (ECMCTP). The model first transforms one-dimensional vibration signals into two-dimensional time-frequency images using Continuous Wavelet Transform (CWT). Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. Finally, the features from both branches are concatenated along the channel dimension and classified using a softmax classifier. Experimental results on two publicly available bearing datasets demonstrate that the proposed model achieves 100% accuracy under noise-free conditions and maintains superior noise robustness under low signal-to-noise ratio (SNR) conditions, showcasing excellent robustness and generalization capabilities.

An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis

Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information. Aiming at the above problems, this paper proposes an Attention-based Multidimensional Fault Information Sharing (AMFIS) framework, which aims to overcome the difficulties of multidimensional bearing fault diagnosis in a small sample environment. Specifically, firstly, a shared network is designed to capture the common knowledge of the Fault Localization Task (FLT) and the Fault Quantification Task (FQT) and save it to the global feature pool. Secondly, two branching networks for performing FLT and FQT were constructed, and an attentional mechanism (AM) was used to filter out features from the shared network that were more relevant to the task to enhance the branching network's capability under small samples. Meanwhile, we propose an innovative Dynamic Adjustment Strategy (DAS) designed to adaptively regulate the training weights of FLT and FQT tasks to achieve optimal training results. Finally, extensive experiments are conducted in two cases to verify the effectiveness and superiority of AMFIS.

Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism

Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide kernel convolutional autoencoder (WDCAE) with a large kernel attention (LKA) mechanism to improve fault detection under unlabeled conditions, and the adaptive threshold module based on a multi-layer perceptron (MLP) dynamically adjusts thresholds, boosting model robustness in imbalanced scenarios. Experimental validation on two datasets (CWRU and a customized ball screw dataset) demonstrates that the proposed model outperforms both traditional and state-of-the-art methods. Notably, WDCAE-LKA achieved an average diagnostic accuracy of 90.29% in varying fault scenarios on the CWRU dataset and 72.89% in the customized ball screw dataset and showed remarkable robustness under imbalanced conditions; compared with advanced models, it shortens training time by 10-26% and improves average fault diagnosis accuracy by 5-10%. The results underscore the potential of the WDCAE-LKA model as a robust and effective solution for intelligent fault diagnosis in industrial applications.

An Adaptation-Aware Interactive Learning Approach for Multiple Operational Condition-Based Degradation Modeling

Although degradation modeling has been widely applied to use multiple sensor signals to monitor the degradation process and predict the remaining useful lifetime (RUL) of operating machinery units, three challenging issues remain. One challenge is that units in engineering cases usually work under multiple operational conditions, causing the distribution of sensor signals to vary over conditions. It remains unexplored to characterize time-varying conditions as a distribution shift problem. The second challenge is that sensor signal fusion and degradation status modeling are separated into two independent steps in most of the existing methods, which ignores the intrinsic correlation between the two parts. The last challenge is how to find an accurate health index (HI) of units using previous knowledge of degradation. To tackle these issues, this article proposes an adaptation-aware interactive learning (AAIL) approach for degradation modeling. First, a condition-invariant HI is developed to handle time-varying operation conditions. Second, an interactive framework based on the fusion and degradation model is constructed, which naturally integrates a supervised learner and an unsupervised learner. To estimate the model parameters of AAIL, we propose an interactive training algorithm that shares learned degradation and fusion information during the model training process. A case study that uses the degradation data set of aircraft engines demonstrates that the proposed AAIL outperforms related benchmark methods.

PeriodNet: Noise-Robust Fault Diagnosis Method Under Varying Speed Conditions

Rolling bearings are critical components in modern mechanical systems and have been extensively equipped in various rotating machinery. However, their operating conditions are becoming increasingly complex due to diverse working requirements, dramatically increasing their failure risks. Worse still, the interference of strong background noises and the modulation of varying speed conditions make intelligent fault diagnosis very challenging for conventional methods with limited feature extraction capability. To this end, this study proposes a periodic convolutional neural network (PeriodNet), which is an intelligent end-to-end framework for bearing fault diagnosis. The proposed PeriodNet is constructed by inserting a periodic convolutional module (PeriodConv) before a backbone network. PeriodConv is developed based on the generalized short-time noise resist correlation (GeSTNRC) method, which can effectively capture features from noisy vibration signals collected under varying speed conditions. In PeriodConv, GeSTNRC is extended to the weighted version through deep learning (DL) techniques, whose parameters can be optimized during training. Two open-source datasets collected under constant and varying speed conditions are adopted to assess the proposed method. Case studies demonstrate that PeriodNet has excellent generalizability and is effective under varying speed conditions. Experiments adding noise interference further reveal that PeriodNet is highly robust in noisy environments.

WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the poor interpretability and noise robustness of DL-based methods are still the main factors limiting their wide application in industry. To address these issues, an interpretable wavelet packet kernel-constrained convolutional network (WPConvNet) is proposed for noise-robust fault diagnosis, which combines the feature extraction ability of wavelet bases and the learning ability of convolutional kernels together. First, the wavelet packet convolutional (WPConv) layer is proposed, and constraints are imposed to convolutional kernels, so that each convolution layer is a learnable discrete wavelet transform. Second, a soft threshold activation is proposed to reduce the noise component in feature maps, whose threshold is adaptively learned by estimating the standard deviation of noise. Third, we link the cascaded convolutional structure of convolutional neutral network (CNN) with wavelet packet decomposition and reconstruction using Mallat algorithm, which is interpretable in model architecture. Extensive experiments are carried out on two bearing fault datasets, and the results show that the proposed architecture outperforms other diagnosis models in terms of interpretability and noise robustness.

Facial Video-Based Non-Contact Stress Recognition Utilizing Multi-Task Learning With Peak Attention

Negative emotional states, such as anxiety and depression, pose significant challenges in contemporary society, often stemming from the stress encountered in daily activities. Stress (state or level) recognition is a crucial prerequisite for effective stress management and intervention. Presently, wearable devices have been employed to capture physiological signals and analyze stress states. However, their constant skin contact can lead to discomfort and disturbance during prolonged monitoring. In this paper, a peak attention-based multitasking framework is presented for non-contact stress recognition. The framework extracts rPPG signals from RGB facial videos, utilizing them as inputs for a novel multi-task attentional convolutional neural network for stress recognition (MTASR). It incorporates peak detection and HR estimation as auxiliary tasks to facilitate stress recognition. By leveraging multi-task learning, MTASR can utilize information related to stress physiological responses, thereby enhancing feature extraction efficiency. For stress recognition, two binary classification tasks are applied: stress state recognition and stress level recognition. The model is validated on the UBFC-Phys public dataset and demonstrates an accuracy of 94.33% for stress state recognition and 83.83% for stress level recognition. The proposed method outperforms the dataset's baseline methods and other competing approaches.

Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the development of the field of fault diagnosis due to their powerful feature extraction ability for handling massive monitoring data. However, most of them still suffer from the following three limitations. First, many existing DL-based intelligent diagnosis methods cannot extract proper discriminative features from signals with strong noise. Second, the interactions or relationships between signals are ignored, while they mainly focus on extracting temporal features from the signal. Third, owing to their black-box nature, the learned features lack interpretability, which hinders their application in the industry. To tackle these issues, an explainable graph wavelet denoising network (GWDN) is proposed to achieve intelligent fault diagnosis under noisy working conditions in this article. In GWDN, the collected signals are first transformed into graph-structured data to consider the interactions among signals. Then, the graph wavelet denoising convolution (GWDConv) is proposed based on the discrete graph wavelet frame, which allows GWDN to achieve multiscale feature extraction for graph-structured data and realize signal denoising. Extensive experiments are implemented to verify the efficacy of the proposed GWDN, and the experimental results show that GWDN can achieve state-of-the-art performance among the comparison methods. Besides, by using the square envelope spectrum to analyze the extracted features of GWDConv, we find that it can well retain the fault-related components of the signal and realize signal denoising, which further proves that GWDN is explainable.

An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples

Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump

Most current data-driven prognosis approaches suffer from their uncontrollable and unexplainable properties. To address this issue, this article proposes a physics-constraint variational neural network (PCVNN) for wear state assessment of the external gear pump. First, a response model of the pressure pulsation of the gear pump is constructed via a spectral method, and a compound neural network is utilized to extract features from the pressure pulsation signal. Then, the response model is formulated into an objective function to softly constrain the learning process of the neural network, forcing the learned features to have explicit physics meaning. Meanwhile, to characterize the system uncertainty, the variational inference is utilized to extend a Kullback-Leibler (KL) divergence into the objective function. Finally, the wear state is evaluated based on the distance of learned physics features. Experimental results on an external gear pump validate the merits of the proposed method in explainable representation learning and system uncertainty estimation. It also offers a controllable and explainable perspective to understand the dynamic behavior of the system.

Variational Attention-Based Interpretable Transformer Network for Rotary Machine Fault Diagnosis

Deep learning technology provides a promising approach for rotary machine fault diagnosis (RMFD), where vibration signals are commonly utilized as input of a deep network model to reveal the internal state of machinery. However, most existing methods fail to mine association relationships within signals. Unlike deep neural networks, transformer networks are capable of capturing association relationships through the global self-attention mechanism to enhance feature representations from vibration signals. Despite this, transformer networks cannot explicitly establish the causal association between signal patterns and fault types, resulting in poor interpretability. To tackle these problems, an interpretable deep learning model named the variational attention-based transformer network (VATN) is proposed for RMFD. VATN is improved from transformer encoder to mine the association relationships within signals. To embed the prior knowledge of the fault type, which can be recognized based on several key features of vibration signals, a sparse constraint is designed for attention weights. Variational inference is employed to force attention weights to samples from Dirichlet distributions, and Laplace approximation is applied to realize reparameterization. Finally, two experimental studies conducted on bevel gear and bearing datasets demonstrate the effectiveness of VATN to other comparison methods, and the heat map of attention weights illustrates the causal association between fault types and signal patterns.

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent.