Motor Bearing Fault Diagnosis Research Articles

A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning

To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD)-based signal reconstruction method is first introduced to extract features representing motor bearing faults. A feature matrix construction method based on improved information entropy is then proposed to quantify these fault features. Finally, a fault diagnosis algorithm architecture integrating a multi-scale convolutional neural network (MSCNN) with attention mechanisms and a bidirectional long short-term memory network (BiLSTM) is developed. The experimental results for four fault states show that this model can effectively extract fault features from original vibration signals and, compared to other fault diagnosis models, offer high diagnostic accuracy and strong generalization, maintaining high accuracy even under varying speeds and noise interference.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

Abstract Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the improved deep convolutional generative adversarial network-multiscale convolutional neural network with mixed attention (IDCG-MAMCNN) model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.

Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles.

Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors

The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis.

Bearing Fault Diagnosis Based on SMA-VMD and CNN-LSTM

To address the challenges of manual parameter setting dependency and low accuracy in bearing fault diagnosis using Variational Mode Decomposition (VMD), a method integrating Slime Mould Algorithm (SMA), VMD, and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network is proposed. Firstly, the SMA is employed to optimize the critical parameters of VMD using minimum information entropy as the fitness function, which resolves the issues related to manual parameter settings in VMD. Subsequently, the optimal parameter combination is used to process the bearing signals and extract the relevant Intrinsic Mode Functions (IMF). Finally, these IMFs are input into the CNN-LSTM network for fault diagnosis. The results demonstrate that this method significantly enhances the accuracy of motor bearing fault diagnosis, further improving the fault diagnosis accuracy.

Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current-Vibration Signals.

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current-vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current-vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.

Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU

Fault diagnosis of traction motor bearing is of great significance to improve the reliability and safety of high-speed electric multiple units (EMU). While the fault diagnosis method based on cross-domain adaptation has been successful in scenarios involving speed or load fluctuations, existing methods ignore the independence and diversity of features, resulting in unsatisfactory diagnostic results under multi-scene learning, thereby reducing the generalization ability. Moreover, the development of complex models is time-consuming, and their computational efficiency is low. To address these issues, this study proposes a novel cross-domain adaptation model based on sparse filtering (SFCDA), which consists of only two fully connected (FC) layers. Firstly, pre-training is conducted to utilize the soft reconstruction penalties to constrain the weights of sparse filtering and improve the independence of features. The weights of unsupervised training are used to initialize the parameters of the first FC layer of the SFCDA model. Secondly, a multiple sparse regularization (MSR) algorithm is proposed and used to constrain the SFCDA. Then, fine-tuning is conducted, in which the structural alignment function is used to measure the distribution distance between the source and target domain data. Minimizing the kernel norm can improve the diversity of features and enhance the robustness. Finally, the effectiveness of SFCDA in multi-scene learning is proved theoretically. It is validated in three fault diagnosis scenes in four different bearing datasets. The results show that the suggested approach is more straightforward and has a better fault diagnosis effect than the state-of-the-art domain adaptive approaches.

TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network

Motor bearing fault diagnosis is essential to guarantee production efficiency and avoid catastrophic accidents. Deep learning-based methods have been developed and widely used for fault diagnosis, and these methods have proven to be very effective in accurately diagnosing bearing faults. In this paper, study the application of generative adversarial networks (GANs) in motor bearing fault diagnosis to address the practical issue of insufficient fault data in industrial testing. Focus on the auxiliary classifier generative adversarial network (ACGAN), and the data expansion is carried out for small datasets. This paper present a novel transformer network and auxiliary classifier generative adversarial network (TRA-ACGAN) for motor bearing fault diagnosis, where the TRA-ACGAN combines an ACGAN with a transformer network to avoid the traditional iterative and convolutional structures. The attention mechanism is fully utilized to extract more effective features, and the dual-task coupling problem encountered in classical ACGANs is avoided. Experimental results with the CWRU dataset and the PU dataset in the field of motor bearing fault diagnosis demonstrate the suitability and superiority of the TRA-ACGAN.

Imbalanced deep transfer network for fault diagnosis of high-speed train traction motor bearings

Transfer learning-based fault diagnosis methods have been increasingly utilized for major equipment, including high-speed trains, turbine machines, and aircraft engines. However, most traditional transfer methods based on implicitly balanced data only consider feature shift. When applied to high-speed train traction motor bearing fault diagnosis, the cross-domain generalization ability of these transfer methods is weakened by label shift. Due to the complex operating conditions of high-speed trains, these transfer methods often fail under multiple operating conditions, resulting in reduced cross-domain diagnostic accuracy when faced with feature shift and label shift simultaneously. Therefore, we propose the imbalanced deep transfer network (IDTN) to tackle the aforementioned problem in cross-domain fault diagnosis of high-speed train traction motor bearings. Firstly, IDTN overcomes the influence of imbalanced distributions in source domain samples through deep imbalanced learning. Then, batch nuclear-norm maximization is introduced to enhance the prediction discriminability and diversity of the target domain samples. Finally, case studies of the high-speed train traction motor bearing fault dataset and the Case Western Reserve University bearing fault dataset are conducted. Experimental results prove the effectiveness and superiority of IDTN in the cross-domain fault diagnosis field with both feature shift and label shift.

Bearing fault diagnosis via fusing small samples and training multi-state Siamese neural networks

Recently, deep learning techniques have been widely applied to fault diagnosis due to their outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis methods is highly dependent on the quantity and quality of the training data. In practical scenarios, it is challenging to obtain sufficient high-quality training data for fault diagnosis tasks due to complex environments, which would affect the effectiveness of the deep learning methods. In this paper, a new fault diagnosis method is proposed for motor bearing fault diagnosis under small samples. The Siamese neural networks (SNNs) are employed to extract the fault features. A multi-stage training strategy is proposed to train the SNNs with the aim to prevent the training stagnation problem and handle the small sample problem. A multi-source feature fusion network is developed to make full use of the multi-source sensor data by fusing the extracted fault features for further fault diagnosis. The proposed method is applied to motor bearing fault diagnosis on two real-world datasets. Experimental results demonstrate the effectiveness and feasibility of the introduced method for motor bearing fault diagnosis under small samples.

Time-shift denoising Combined with DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current Signals

Time-shift denoising Combined with DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current Signals

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data

Through the application of deep learning and multi-sensor data, fault features can be automatically extracted and valuable information can be integrated to tackle intricate challenges in motor bearing fault diagnosis. Most existing fusion models focus primarily on the original time series signal with information extraction largely restricted to the time domain (without extensions into multiple transformation domains). Also, in most fusion models, the sensor fusion level is kept relatively simple which could lead to the oversight of correlations and complementarities among the information. To enhance the recognition capability of diagnostic network features, in this paper, we propose a novel framework for motor bearing fault diagnosis from the perspectives of multi-transformation domain and multi-source data fusion. Within this framework, feature extraction and fusion from various source data are achieved in the time domain, frequency domain, and time–frequency domain. Distinct independent networks are set up within these domains: one network is designated for overseeing feature fusion, while the others are dedicated to extracting features from individual sensors. To support the extraction of pivotal features across multiple fusion layers in various transformation domains, several fusion nodes are inserted between the layers of the multiple feature extraction networks and the feature summarization network. Furthermore, a channel attention mechanism is introduced as a fusion strategy that serves to pinpoint the significance of different features, thus enhancing the efficiency of feature extraction. Experimental evaluation reveals the efficacy of the proposed model and highlights its noteworthy performance attributes such as scalability and universality.

Retracted: Research on Motor Bearing Fault Diagnosis Based on the AdaBoost Algorithm and the Ensemble Learning with Bayesian Optimization in the Industrial Internet of Things

Retracted: Research on Motor Bearing Fault Diagnosis Based on the AdaBoost Algorithm and the Ensemble Learning with Bayesian Optimization in the Industrial Internet of Things

An adaptive morphological filtering and feature enhancement method for spindle motor bearing fault diagnosis

The unbalanced electromagnetic force and unqualified assembly of the spindle motor lead to weak vibration energy and the difference in fault features in different life cycles and different bearing individuals. Compared with single fault diagnosis and the datasets that come from the test bench, compound faults diagnosis of the spindle motor is a challenging task. To solve it, an improved filtering and feature enhancement method combined with the merits of AMF and TEO is proposed. Firstly, the adaptive morphological filtering (AMF) method is developed by adaptively constructing the size of the structural element (SE) for remaining in corresponding SE with the component of interest, which can reduce interference from background noise. Still, the periodic fault impulse, especially the incipient fault signals, is easily modulated by other fault-unrelated harmonic components. Thus, the filtered signals are processed by the Teager energy operator (TEO) for enhancing the faint transient impact compositions. Finally, the effectiveness of the method is verified by simulation and fault motor matched with NTN ceramic bearing and FAG metal bearing respectively, where the motor matched with NTN ceramic bearing is an early failure case and the other is a late failure case. Besides, compared with some traditional methods, the result proves that the proposed method has better performance under the actual engineering scenarios for different degrees of fault feature identification.

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

A novel motor bearing fault diagnosis method based on a deep sparse binary autoencoder and principal component analysis

Due to the complex and variable operating conditions of motor bearings, it is difficult for a deep autoencoder (DAE) to effectively extract valuable fault features from the raw vibration signal, which makes it difficult to identify faults. To enhance the extraction ability of the deep features of a network model and improve the accuracy of fault identification, this paper proposes a fault diagnosis method for motor bearings based on a deep sparse binary autoencoder and principal component analysis (PCA). Firstly, a deep sparse binary autoencoder is constructed by combining an autoencoder with a binary processor to improve the ability to extract deep features. Secondly, principal component analysis is used to fuse high-dimensional features to reduce dimensionality and eliminate redundant information existing in the deep features. Finally, fused deep features are input into a Softmax classifier to train the intelligent fault diagnosis model. The proposed method is validated on a rolling bearing dataset. Compared with existing methods, the experimental results show that this method can effectively extract robust features from the original vibration signals and improve the fault diagnosis results.

A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.