Fault Diagnosis Of Rotating Machinery Research Articles

Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.

Parametric study and Ensuring Explainability of Physics-Informed Artificial Intelligence for Rotating Machinery Fault Diagnosis

Parametric study and Ensuring Explainability of Physics-Informed Artificial Intelligence for Rotating Machinery Fault Diagnosis

Rotating Machinery Fault Classification Based on One-Dimensional Residual Network with Attention Mechanism and Bidirectional Gated Recurrent Unit

Abstract Conventional convolutional neural networks (CNNs) predominantly emphasize spatial features of signals and often fall short in prioritizing sequential features. As the number of layers increases, they are prone to issues such as vanishing or exploding gradients, leading to training instability and subsequent erratic fluctuations in loss values and recognition rates. To address this issue, a novel hybrid model, termed one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit (1D-RAM-BGRU) is developed for rotating machinery fault classification. First, a novel one-dimensional residual network (1D-ResNet) with optimized structure is constructed to obtain spatial features and mitigate the gradient vanishing or exploding. Second, the attention mechanism (AM)is designed to catch important impact characteristics for fault samples. Next, temporal features are mined through the bidirectional gated recurrent unit (BGRU .) Finally, feature information is summarized through global average pooling, and the fully connected layer is utilized to output the final classification result for rotating machinery fault diagnosis. The developed technique which is tested on one set of planetary gear data and three different sets of bearing data, has achieved classification accuracy of 98.5%, 100%, 100%, and 100%, respectively. Compared with other methods, including CNN, CNN-BGRU, CNN-AM, and CAM-BGRU, the proposed technique has the highest recognition rate and stable diagnostic performance.

Rotating Machinery Fault Diagnosis under Time–Varying Speed Conditions Based on Adaptive Identification of Order Structure

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults.

Physics-inspired multimodal machine learning for adaptive correlation fusion based rotating machinery fault diagnosis

Multimodality is a universal characteristic of multi-source monitoring data for rotating machinery. The correlation fusion of multimodal information is a general law to strengthen the cognition of fault features, and an effective way to improve the reliability and robustness of fault diagnosis methods. However, the physical connotation gaps between multimodal information hinder the construction of correlations, preventing mainstream machine learning (ML) based intelligent diagnosis methods from reliably and effectively taking advantage of multimodal fusion. To address these issues, a physics-inspired multimodal fusion convolutional neural network (PMFN) is proposed in this paper. It is the first attempt to integrate physical knowledge into ML models to bridge physical connotation gaps between multimodal fault information. Specifically, the characterization patterns of rotating machinery fault in multimodal information are embedded in the attention mechanism to focus on representative fault features with physical properties, and generate the universal representation of multimodal information. Furthermore, the cross-modal correlation fusion module is introduced to adaptively construct the correlations of multimodal information, thereby highlighting the unique feature of unimodal information and the shared representation of multimodal information. Finally, the superiority of the proposed fusion method is verified by two cases of industrial gearbox and bearing-rotor system.

A Time–Frequency Image Quality Evaluation Method Based on Improved LIME

Deep learning has the characteristics of high efficiency, high accuracy and low knowledge dependence, and has become a hot method in the research field of rotating machinery fault diagnosis. The time–frequency transform can show both the time and frequency characteristics of the vibration signal, so the time–frequency image is often used as the input of deep learning networks. At present, there are many time–frequency transform methods, and how to choose one is a problem worth discussing. This paper proposed a time–frequency image quality evaluation method based on improved local interpretable model-agnostic explanations (LIME). With the input of deep learning networks as the application background, this method evaluates the time–frequency image quality of rotating machinery vibration signals from two aspects: the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge. The feasibility of the proposed evaluation method is verified by experiments on the measured data set, and engineers’ trust in the deep learning model is improved.

Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments

Rotating machinery operates continuously for long periods of time under varying conditions in actual industrial environments. The number of fault samples increases with equipment operating time, whereas differences in data distribution are inevitable because of varying operating conditions. As a result, fault diagnosis of rotating machinery poses challenges related to the fault domain increments. In addition, the issue of catastrophic forgetting, where previously learned knowledge significantly affects the model's performance, remains a critical concern. Deep adaptive sparse residual network (DASRN), a new lifelong learning-based method, is proposed to overcome these challenges. The DASRN model is constructed based on an improved deep residual network. The model uses a task-aware dynamic masking strategy to adjust the retention and utilization of blank weights in the network, thereby balancing the model's memory and learning capabilities, mitigating catastrophic forgetting. Furthermore, throughout the entire training process of the model, a dynamic data removal strategy is applied to eliminate easily distinguishable samples. This approach adaptively allocates higher weights to more challenging samples, enhancing the model's learning efficiency while consolidating the sparsity within the model. Finally, the effectiveness of the proposed DASRN is validated on bearing datasets from the Chinese CRH380A high-speed train gearbox and integrated wheelset transmission test rig. Results demonstrate that DASRN provides an effective solution for catastrophic forgetting and domain incremental scenarios in rotating machinery fault diagnosis tasks.

Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network

The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.

Ensemble difference mode decomposition based on transmission path elimination technology for rotating machinery fault diagnosis

Difference mode decomposition (DMD) is an effective technique for accurately separating a signal into fault, natural, and noise components. However, DMD relies on the assumption that noise spectral lines have zero amplitude, making it less effective in cases with a low signal-to-noise ratio. In practical scenarios, signals from bearing faults are often weak and can get buried under strong noise due to the influence of the transmission path (TP). To broaden the application scope of DMD, we proposed the ensemble DMD (EDMD) method, incorporating the transmission path elimination (TPE) technology. Firstly, an Intrinsic time-scale decomposition (ITD)-based TPE approach is proposed to reduce the influence of excessive noise spectral line amplitude caused by the TP. Secondly, calculating the average of the difference between two kinds of normalized Fourier spectrums and using it as the initial weight of EDMD can improve the operation speed of EDMD. Finally, the signal with the transmission path eliminated is input to the EDMD to extract the faulty signal. Simulation and experimental verification demonstrate the capability of this method in accurately extracting fault features in rolling bearings and gears under the influence of the TP.

TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis

Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the availability of massive labeled training data. However, in practical industrial applications, it is often challenging and costly to annotate a large amount of data. To address the few-label fault diagnosis problem, a time–frequency prediction (TFPred) self-supervised learning framework is proposed to extract latent fault representations from unlabeled fault data. Specifically, the TFPred framework consists of a time encoder and a frequency encoder, with the frequency encoder to predict the low-dimensional representations of time domain signals generated by the time encoder with randomly augmented data. Subsequently, the pre-trained network is hooked with a classification head and fine-tuned with limited labeled data. Finally, the proposed framework is evaluated on a run-to-failure bearing dataset and a hardware-in-the-loop high-speed train simulation platform. The experiments demonstrate that the self-supervised learning framework TFPred achieved competitive performance with only 1% and 5% labeled data. Code is available at https://github.com/Xiaohan-Chen/TFPred.

Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN

As vital equipment in modern industry, the health state of rotating machinery influences the production process and equipment safety. However, rotating machinery generally operates in a normal state most of the time, which results in limited fault data, thus greatly constraining the performance of intelligent fault diagnosis methods. To solve this problem, this paper proposes a novel fault diagnosis method for rotating machinery with limited multisensor fusion samples based on the fused attention-guided Wasserstein generative adversarial network (WGAN). Firstly, the dimensionality of collected multisensor data is reduced to three channels by principal component analysis, and then the one-dimensional data of each channel are converted into a two-dimensional pixel matrix, of which the RGB images are obtained by fusing the three-channel two-dimensional images. Subsequently, the limited RGB samples are augmented to obtain sufficient samples utilizing the fused attention-guided WGAN combined with the gradient penalty (FAWGAN-GP) method. Lastly, the augmented samples are applied to train a residual convolutional neural network for fault diagnosis. The effectiveness of the proposed method is demonstrated by two case studies. When training samples per class are 50, 35, 25, and 15 on the KAT-bearing dataset, the average classification accuracy is 99.9%, 99.65%, 99.6%, and 98.7%, respectively. Meanwhile, the methods of multisensor fusion and the fused attention mechanism have an average improvement of 1.51% and 1.09%, respectively, by ablation experiments on the WT gearbox dataset.

Rotary Machinery Fault Diagnosis Based on Split Attention Mechanism and Graph Convolutional Domain Adaptive Adversarial Network

Rotary Machinery Fault Diagnosis Based on Split Attention Mechanism and Graph Convolutional Domain Adaptive Adversarial Network

Knowledge-driven domain adaptation strategy for rotating machinery fault diagnosis under varying working condition

Due to the frequent switch of the working condition, fault diagnosis model for rotating machinery established on the training set (the source domain) cannot be effectively applied to the test set (the target domain). Traditional domain adaptation methods address this issue by performing feature alignment between the source and target domain, which ignores the positive guidance provided by prior knowledge. In this paper, we propose a prior knowledge-driven domain adaptation (PKDA) method for varying working condition fault diagnosis of rotating machinery, where a self-supervised learning framework is designed to integrate expert prior knowledge and structural prior knowledge. First, an expert prior knowledge guidance module is designed to extract features with physical significance. Then we align the features between the source and target domain by the max mean discrepancy metric. Besides, a progressive Shannon entropy minimization strategy is proposed to realize the feature distribution structure of intra-class compression and inter-class separation, which can effectively integrate the structural prior knowledge. In this way, PKDA can effectively utilize the prior knowledge to achieve better performance in varying working condition fault diagnosis tasks. The effectiveness of the proposed method is illustrated by an open-source rolling bearing fault dataset from Case Western Reserve University and an open-source gearbox fault dataset from Southeast University.

A novel feature extraction method: iterative Ramanujan Fourier mixture spectrum

As a classical demodulation method, envelope spectrum (ES) has been used in rotating machinery fault diagnosis. However, for strong noise or multiple faults, the feature extraction ability of ES is not outstanding, even if it is unable to extract obvious fault features. Fourier transform (FT) in ES analysis is also not suitable for analyzing non-stationary signals. Considering the shortcomings of FT, this paper defines the Ramanujan Fourier mixture transform (RFMT). The RFMT has outstanding period feature extraction ability and can accurately extract the periodic features in the envelope signal. Based on this, this paper proposes the iterative Ramanujan Fourier mixture spectrum (IRFMS) method. The IRFMS enhances the fault features in the signal by iterative envelope and selects the envelope signals with obvious fault features by cyclic kurtosis. The results of signal analysis verify that IRFMS has excellent fault feature extraction ability, which is suitable for various fields of rotating machinery fault diagnosis.

Noise-robust adaptive feature mode decomposition method for accurate feature extraction in rotating machinery fault diagnosis

Rotating machinery typically consists of multiple rotating components, and its fault signals contain not only periodic impulse components caused by local defects but also periodic noise components generated by the normal operation of other rotating parts. Especially in the case of compound faults, the vibration signals exhibit the characteristics of simultaneous coupling of multiple periodic components and multiple pulse components, greatly affecting the accuracy of compound fault diagnosis. In order to accurately separate and extract individual fault components from the rotating machinery's compound fault signals under strong periodic noise interference, this paper proposes a noise-robust adaptive feature mode decomposition method for compound fault diagnosis in rotating machinery. In addressing the challenge of existing decomposition methods, which heavily rely on accurate fault period estimation and initialization of decomposition number, an efficient strategy has been developed within the proposed method. This strategy remains effective even under intense periodic disturbances by accurately pinpointing the resonance bands induced by faults. It simultaneously acquires the essential prior knowledge necessary for mode decomposition, resolving the issue of prevailing fault period estimation methods being prone to failure in the presence of strong periodic noise. Furthermore, a feature mode decomposition method with the second-order indicators of cyclostationarity as the objective function is introduced. This, coupled with the devised parameter optimization strategy, facilitates precise decomposition of compound fault components in the presence of strong periodic noise. Finally, the robustness of the proposed method against periodic noise and its outstanding ability to extract compound fault features undergo validation through simulations and experiments, highlighting its potential for advancement in the field of rotating machinery fault diagnosis.

Graph-Guided Higher-Order Attention Network for Industrial Rotating Machinery Intelligent Fault Diagnosis

Data-driven approaches have gained great success in the field of rotating machinery fault diagnosis for its powerful feature representation capability. However, in most of the current studies, model training process requires massive fault data which is costly to gather or even unavailable in some extreme operating conditions. At the same time, structural relationships between samples are not fully exploited to facilitate the model performance. In response to these problems, a novel graph-guided higher-order attention network (GHOAN) for rotating machinery fault diagnosis is proposed in this study. Specifically, the proposed approach incorporates the advantages of improved graph attention network (GAT) model and the multi-order neighborhood feature perception to achieve richer feature representation by aggregating features from multiple neighborhood domains. In this way, effective fault diagnosis may be achieved by using fewer training samples based on vibration signal analysis. The results of experiments conducted on two benchmarking datasets and a practical experimental platform show that proposed GHOAN achieve superior performance.

A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions

In practical industrial scenarios, the variations of operating conditions such as load and rotational speed make mechanical systems subject to complex and variable environmental stresses, resulting in the distribution discrepancies of sample data. With the advantages of integrating the feature information and diagnosis knowledge, the transfer learning technique based on multiple source domains has become a stable and efficient solution to address the fault diagnosis challenge under variable operating conditions in the modern intelligent operation and maintenance. For the above discussions, a multi-source domain information fusion network (MDIFN) is proposed in this paper to obtain generalized knowledge with abundant feature information by combining the adversarial transfer learning technique with fine-grained information fusion of multiple source domains. First, an adversarial transfer network architecture is constructed in accordance with the complex feature transformation and the boundary equilibrium domain discrimination to implement feature learning and knowledge transfer of source and target domains. Then, a joint distribution domain adaptation mechanism is proposed to further facilitate the acquisition of domain invariant features. Finally, a class-related decision fusion (CDF) strategy is designed to realize the information fusion within the decision space. The fault diagnosis of rotating machinery under unknown operating conditions can be achieved by employing data under known multiple operating conditions for MDIFN training. The public Paderborn University (PU) bearing dataset and the actual mechanical comprehensive diagnosis simulation platform (MCDSP) bearing dataset from different testing rigs are considered to evaluate the cross-domain fault diagnosis performance of this method. The experimental results indicate that the method achieves an average accuracy of 95.97% on the PU dataset and 98.31% on the MCDSP dataset, which is superior to other state-of-the-art cross-domain fault diagnosis algorithms.

Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

AbstractIn the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.

A multi-scale feature weighted transfer network for unlabeled rotating machinery fault diagnosis

A multi-scale feature weighted transfer network for unlabeled rotating machinery fault diagnosis

An Improved Deep Transfer Learning Method for Rotating Machinery Fault Diagnosis Based on Time Frequency Diagram and Pretraining Model

An Improved Deep Transfer Learning Method for Rotating Machinery Fault Diagnosis Based on Time Frequency Diagram and Pretraining Model