Intelligent Fault Diagnosis Method Research Articles

A cross-domain intelligent fault diagnosis method based on deep subdomain adaptation for few-shot fault diagnosis

A cross-domain intelligent fault diagnosis method based on deep subdomain adaptation for few-shot fault diagnosis

An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis

In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.

Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

Rotating machinery often runs under large-speed-fluctuation (LSF) conditions, which results in severe data distribution domain shift for intelligent fault diagnosis methods. However, this challenge is rarely discussed in current studies. Hence, motivated by the active imagination of a human being, a new tool named Actively Imaginative Data Augmentation (AIDA) is constructed to solve machinery intelligent diagnosis under LSF conditions. Two adversarial training steps, namely, knowledge learning and sample imagining, are included in AIDA. In knowledge learning, a deep model is trained to learn the classification knowledge. In sample imagining, the parameters of the deep model are fixed and samples are generated via inversely training the model. As a result, diversified samples and an intelligent deep model adapting to the LSF condition are obtained by alternately carrying out the two steps. Moreover, a detailed discussion is given to interpret the process of actively imagining samples in the proposed AIDA, in which some measures are designed and the feature visualization is conducted. Experimental results show the effectiveness and superiority of AIDA in machinery diagnosis under LSF conditions, and the good performance of AIDA is due to the diversified dataset generated by changing the degrees and directions of at each sample imagining.

Prediction of Bearing Vibration Fault State based on Fused Bi-LSTM and SVM

Machinery failure prediction and equipment health management is the key to reduce even prevent the occurrence of significant failures. In recent years, deep learning based intelligent fault diagnosis methods have become a hot topic of research in the field of machinery and equipment health management, especially the evaluation of the equipment health indicators and failure mode identification issues in complex environments. In this paper, a fused bidirectional long and short term memory (BiLSTM) and support vector machine (SVM) recurrent neural network deep learning algorithm is proposed to solve the problem of fast fault diagnosis of mechanical bearing vibration in complex environments. The algorithm uses a BiLSTM memory network as a feature extractor to capture fault features in the raw one dimensional (1-D) time series data, and then a SVM is used at the end of the network instead of the traditional softmax function as a classifier discriminator to further improve the accuracy of diagnosis. The experimental results indicate that the proposed algorithm model has higher diagnostic accuracy and faster diagnosis speed, and more suitable for fast diagnosis and real time detection of faults in complex environments.

Fault Diagnosis of High-Speed Railway Turnout System Based on Improved Group Decision-Making

Turnout system is very important in the safety of railway system. However, the traditional maintenance method of turnouts is mainly based on human experience and basic intelligent model. Aiming at the problems existing in the current intelligent fault diagnosis method of turnout equipment, this paper uses the improved group decision-making method to dynamically and iteratively adjust and optimize the authority of each decision-making expert in the group decision-making method, so as to improve the prediction ability of the existing model, reduce the number of cases of prediction errors and further improve the safety of the turnout system.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

Dynamic model-driven intelligent fault diagnosis method for rotary vector reducers

The diagnosis of faults in rotary vector (RV) reducers using machine data-driven artificial intelligence (AI) models plays an important role, but it is difficult to obtain complete fault sample labeled data. Without labeled data, AI-based intelligent fault diagnosis models will fail. To solve the problem of data scarcity, a lumped parameter model of an RV reducer is developed to produce a sufficient training sample for AI models. First, a lumped parameter model of the healthy RV reducer is constructed and updated by the Pearson correlation coefficient (PCC) technique to obtain an agreeable dynamic model with a certain precision. Then, mathematical expressions of numerous fault modes with different fault severities are inserted into the model to calculate the fault samples. The simulated failure samples serve as training samples of AI-based intelligent models. Finally, CNN, VGG and ResNet are selected as the representatives of AI model, and then unknown fault samples are identified by applying data from real-time machinery. The experimental results suggest that the present method can be used to overcome the problem of insufficient fault samples in RV reducers.

Research on Rolling Bearing Fault Diagnosis Based on DRS Frequency Spectrum Image and Deep Learning

Deep learning is gradually being widely used in fault diagnosis now, because deep learning networks are more advantageous in processing data, especially image data. However, research using frequency spectra image of fault signals as inputs to deep learning networks are extremely rare in the field of fault diagnosis. Therefore, a brand-new intelligent fault diagnosis method is proposed in this paper which combines discrete random separate (DRS) frequency spectrum images with deep learning networks (DRSFSI-DL). To investigate the fault diagnosis effects of the method mentioned above, several deep learning networks are utilized for comparisons, such as GoogLeNet, residual network, and Inception_ResNet_v2. The vibration fault frequency spectrum images processed by the DRS method are input to train several deep learning networks. Under the same circumstance of deep learning networks, the fault diagnosis using the DRS frequency spectrum image (DRSFSI), is also compared to the fault diagnosis using traditional frequency spectrum, including the power spectrum density (PSD) and cepstrum. The fault diagnosis results show that the proposed method has a better classification accuracy than the PSD image and cepstrum image, with the same deep learning networks. The fault diagnosis accuracy can reach up to 100.00% for some deep learning networks with better generalization performance than the PSD image and cepstrum image. Lastly, the proposed method is further verified using the brand-new bearing fault dataset, and excellent accuracy and generalization ability are achieved.

Intelligent fault diagnosis method using small fault samples driven by digital data and feature enhancement

Intelligent fault diagnosis method using small fault samples driven by digital data and feature enhancement

Intermittent Fault Diagnosis and Prognosis for Steer-by-Wire System Using Composite Degradation Model

For automotive manufacturers, intelligent fault diagnosis and prognosis techniques to ensure reliability of their products become increasingly important with the introduction of Industry 4.0. In this paper, an intelligent intermittent fault diagnosis and prognosis method is proposed for steer-by-wire (SBW) system based on composite degradation model. First, a nonlinear bond graph model of the SBW system is established, by which an integrated fault signature matrix combining analytical redundancy relations and dedicated observers is developed to enhance the fault isolability. Second, in order to identify the features (i.e., fault appearing and disappearing instants) of each intermittent fault, an improved whale optimization algorithm is proposed by introducing nonlinear convergence factor, Cauchy-Gaussian mixture mutation and greedy selection strategy. After that, the composite degradation model is established to predict the remaining useful life of the component with intermittent fault, where duration feature and frequency feature are considered with the aid of observation window. Finally, the effectiveness of the proposed approach is validated by simulation and experimental results.

A Hybrid Feature Selection and Multi-Label Driven Intelligent Fault Diagnosis Method for Gearbox

Gearboxes are utilized in practically all complicated machinery equipment because they have great transmission accuracy and load capacities, so their failure frequently results in significant financial losses. The classification of high-dimensional data remains a difficult topic despite the fact that numerous data-driven intelligent diagnosis approaches have been suggested and employed for compound fault diagnosis in recent years with successful outcomes. In order to achieve the best diagnostic performance as the ultimate objective, a feature selection and fault decoupling framework is proposed in this paper. That is based on multi-label K-nearest neighbors (ML-kNN) as classifiers and can automatically determine the optimal subset from the original high-dimensional feature set. The proposed feature selection method is a hybrid framework that can be divided into three stages. The Fisher score, information gain, and Pearson’s correlation coefficient are three filter models that are used in the first stage to pre-rank candidate features. In the second stage, a weighting scheme based on the weighted average method is proposed to fuse the pre-ranking results obtained in the first stage and optimize the weights using a genetic algorithm to re-rank the features. The optimal subset is automatically and iteratively found in the third stage using three heuristic strategies, including binary search, sequential forward search, and sequential backward search. The method takes into account the consideration of feature irrelevance, redundancy and inter-feature interaction in the selection process, and the selected optimal subsets have better diagnostic performance. In two gearbox compound fault datasets, ML-kNN performs exceptionally well using the optimal subset with subset accuracy of 96.22% and 100%. The experimental findings demonstrate the effectiveness of the proposed method in predicting various labels for compound fault samples to identify and decouple compound faults. The proposed method performs better in terms of classification accuracy and optimal subset dimensionality when compared to other existing methods.

A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism

Fault diagnosis of rolling bearings helps ensure mechanical systems’ safety. The characteristics of temporal and interleaved noise in the bearing fault diagnosis data collected in the industrial field are addressed. This paper proposes a hybrid intelligent fault diagnosis method (WKN-BiLSTM-AM) based on WaveletKernelNetwork (WKN) and bidirectional long-short term memory (BiLSTM) network with attention mechanism (AM). The WKN model is introduced to extract the relevant impact components of defects in the vibration signals, reduce the model training parameters and facilitate the processing of signals containing noise. Then, the fusion of spatial-temporal features is achieved by combining BiLSTM networks to compensate for the lack of individual networks that ignore the dependent information between discontinuous sequences. Finally, the AM module is introduced to improve the feature coding performance of BiLSTM and fault diagnosis accuracy. Comparison and validation between the proposed WKN-BiLSTM-AM method and other state-of-the-art models are given on the Case Western Reserve University and Paderborn University datasets. The experimental results verify the effectiveness of the proposed model in bearing fault diagnosis, and the model’s generalization capability.

MATLAB-based fault diagnosis of industrial rotor-bearing systems

Continuous monitoring of critical rotor-bearing systems is crucial in order to prevent machine downtime, which would otherwise lower the overall output and quality. Complex modern machinery demands an upgraded intelligent fault diagnosis method that leaves minimal room for human error. This paper presents a MATLAB-based condition monitoring and fault diagnosis method for rotating machines used in sugar factories. The vibration responses are acquired through the use of data acquisition and the fast Fourier transform (FFT) analyser on real industrial machines. These signals are supplied as the input to a specially developed MATLAB program for processing in order to detect the fault and help to suggest remedies. The simple and user-friendly approach saves time and increases the effectiveness of condition monitoring in the reduction of downtime and the avoidance of catastrophic failure in industrial machines.

An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks

An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

Broad learning system (BLS) has been widely applied in the field of fault diagnosis because of its high computational efficiency, simple structure, and strong interpretability. However, traditional BLS cannot extract deep level fault features. Meanwhile, some fault samples are difficult to obtain, which leads to the imbalance of samples and further affects the diagnostic results of BLS. To solve these problems, an improved BLS fault diagnosis method based on data enhancement and multi-domain feature fusion is proposed. First, to solve the problem of sample imbalance, some false samples are generated through deep convolutional generative adversarial networks. Second, to solve the problem of poor feature extraction ability of BLS, the multi-domain feature extraction and feature optimization based on ReliefF algorithm are carried out for the enhanced samples. Compared with traditional BLS, the improved BLS effectively solves the problem of sample imbalance and greatly improves the diagnostic accuracy. The proposed method is then testified on the rolling bearing fault simulation test bench. The results show that, samples generated by the proposed method are highly similar to the real samples. In addition, the diagnostic accuracy of the BLS after multi-domain feature extraction and optimization is improved by about 19.67%, which proves the effectiveness of the method. This method provides a new perspective in fault diagnosis and could further expand the application of BLS in fault diagnosis.

An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network

A bearing is a key component in rotating machinery. The prompt monitoring of a bearings’ condition is critical for the reduction of mechanical accidents. With the rapid development of artificial intelligence technology in recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success in the field of bearing condition monitoring. However, most algorithms are developed based on computer platforms that focus on analyzing offline, rather than real-time, signals. In this paper, an edge intelligence diagnosis method called S-AlexNet, which is based on a parameter transplantation convolutional neural network (CNN), is proposed. The method deploys the lightweight IFD method in a low-cost embedded system to monitor the bearing status in real time. Firstly, a lightweight IFD algorithm model is designed for embedded systems. The model is trained on a PC to obtain optimal parameters, such as the model’s weights and bias. Finally, the optimal parameters are transplanted into the embedded system model to identify the bearing status on the edge side. Two datasets were used to validate the performance of the proposed method. The validation using the CWRU dataset shows that the proposed method achieves an average prediction accuracy of 94.4% on the test set. The validation using self-built data shows that the proposed method can identify bearing operating status in embedded systems with an average prediction accuracy of 99.81%. The results indicate that the proposed method has the advantages of high recognition accuracy, low model complexity, low cost, and high portability, which allow for the simple and effective implementation of the edge IFD of bearings in embedded systems.

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

Intelligent fault diagnosis method for rolling bearing using WMNRS and LSSVM

A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.

A feature pseudo-fusion method for intelligent fault diagnosis of electro-hydraulic switch machine inspired by contrastive learning

The railway system is an important part of the transportation system. Its scheduling process is carried out by the switch machines. The accuracy of determining the health status of the switch machines is related to the operational efficiency and reliability of the whole system. However, manual fault diagnosis for these machines is always unstable and expensive. The intelligent fault diagnosis (IFD) method can perform accurate fault diagnosis at low cost and high efficiency, but requires a large amount of labeled data. In this case, this study realizes the Feature Pseudo-Fusion (FPF) of left and right oil pressure signals of the electro-hydraulic switch machine. It uses contrastive learning to regularize the feature representation of original signals. Based on FPF, a fault diagnosis method applicable to electro- hydraulic switch machines is constructed. This method reduces the need for labeled data laterally without introducing additional measurement content to the field signal acquisition system. The effectiveness of FPF and the superiority of the fault diagnosis method have been verified through experiments.

Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor

The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method.