Gearbox Fault Diagnosis Method Research Articles

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

In a high-noise environment and with a limited number of faulty samples, it becomes challenging to extract a sufficient amount of useful fault information, which makes gear fault diagnosis more difficult. To address these issues, this paper proposes a fault diagnosis method for planetary gearboxes based on intrinsic feature extraction and attention mechanism. The method utilizes the complementary ensemble empirical mode decomposition algorithm to perform modal decomposition on the fault vibration signal, obtaining a series of modal components. By comparing and selecting the modal components that contain a significant amount of fault features, they are then transformed into two-dimensional images with time–frequency properties using wavelet transform. Additionally, a neural network model based on attention mechanism and large-scale convolution is proposed. The preprocessed images are inputted into the network for feature extraction. During this process, the large-scale convolution with residual structure maximizes the retention of effective feature information, while the attention network further filters the features. Finally, the selected features are used for fault classification. The model is validated using the gear datasets from Southeast University and the University of Connecticut. A comparison is made with the Pro-MobileNetV3, channel attention and multiscale convolutional neural network, multiscale dynamic adaptive residual network, and CBAM-ResNeXt50 models. It is found that the accuracy reaches 100% before adding Gaussian noise and 99.68% after adding noise, which is significantly higher than that of other models.

Research on Gearbox Fault Diagnosis Method Based on VMD and Optimized LSTM

The reliability of gearboxes is extremely important for the normal operation of mechanical equipment. This paper proposes an optimized long short-term memory (LSTM) neural network fault diagnosis method. Additionally, a feature extraction method is employed, utilizing variational mode decomposition (VMD) and permutation entropy (PE). Firstly, the gear vibration signal is subjected to feature decomposition using VMD. Secondly, PE is calculated as a feature quantity output. Next, it is input into the improved LSTM fault diagnosis model, and the LSTM parameters are iteratively optimized using the chameleon search algorithm (CSA). Finally, the output of the fault diagnosis results is obtained. The experimental results show that the accuracy of the method exceeds 97.8%.

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper.

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Accurate diagnosis of gearbox faults is vital to maintain the stability and dependability of rotating machinery. In practical engineering scenarios, the installation of multiple sensors is often necessary to monitor numerous variables and gather extensive information. To effectively utilize the fault information from multiple sensors and improve fault diagnosis performance, this paper presents a gearbox fault diagnosis method using generalized minimum entropy deconvolution (GMED) and main frequency center extraction (MFCE). Firstly, the introduced GMED enhances the fault signals from multiple sensors through the maximization of standardized moments, offering advantages such as stability, wide applicability, and minimal parameter tuning requirements. Subsequently, the proposed MFCE is employed to extract features. MFCE selects main spectral lines and derives a novel feature metric named main frequency center. This not only further reduces the influence of interfering components, but also requires the extraction of only 2 n features (where n is the number of sensors) to achieve a high fault recognition rate, while remaining compatible with multiple classical classifiers. Finally, the effectiveness and robustness of the proposed method are demonstrated through experimental results on two gearbox fault datasets.

A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenarios

Gearboxes operate in harsh environments. Cloud-based techniques have been previously adopted for fault diagnosis in Gearboxes. Cloud-based fault diagnosis methods are prone to time delays and loss of information. Therefore, edge computing-based fault diagnosis becomes an option. However, with limited hardware resources for edge devices, balancing the diagnostic capabilities of the model with operating performance becomes a challenge. This paper proposes a lightweight convolutional neural network for gearbox fault diagnosis in edge computing scenarios to achieve an accurate diagnosis and lightweight deployment of models. By constructing the Mel-Frequency Cepstral Coefficients (MFCC) feature matrix of input data, the methodology can suppress noise interference and improve diagnostic accuracy. Based on the structural re-parameterization, the model structure transforms from multiple branches at training time to a single branch at inference time. This improves the inference speed of the model and reduces the hardware cost when the model is deployed while ensuring that the diagnostic capability of the model remains unchanged. Validation experiments were conducted on a public dataset and a custom experimental device, using the NVIDIA Jetson Xavier NX kit as the edge computing platform. According to the experiment result, after extracting the MFCC feature matrix, the average diagnostic accuracy rate in the noisy environment of the presented methodology is improved by 12.22% and 9.44%, respectively. After structural re-parameterization, the Memory of the model decreases by 52.58%, and the inference speed is increased by 38.83%.

Fault diagnosis of gearbox based on Fourier Bessel EWT and manifold regularization ELM

The novel fault diagnosis method of gearbox based on Fourier Bessel series expansion-based empirical wavelet transform (FBEWT) and manifold regularization extreme learning machine (MRELM) is proposed to obtain excellent fault diagnosis results of gearbox in this paper. A new feature extraction strategy based on Fourier Bessel series expansion-based empirical wavelet transform is used to capture the key non-stationary features of the vibrational signal of gearbox, and significantly improve the diagnosis ability of gearbox. The ELM with manifold regularization is proposed for fault diagnosis of gearbox. In order to outstand the superiority and stability of the proposed FBEWT and manifold regularization ELM, the balanced dataset and unbalanced dataset, respectively, are used. The experimental results testify that FBEWT-MRELM are more superior and stable than FBEWT-ELM, EWT-MRELM, and EWT-ELM regardless of balanced dataset and unbalanced dataset.

Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network.

The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

Fault Diagnosis Method of Planetary Gearbox Based on ISSA-VME and Multi-scale Fuzzy Entropy

To solve the problem of noise interference and difficulty in feature extraction of vibration signals of planetary gearbox, propose a fault diagnosis method of planetary gearbox based on improved sparrow search algorithm optimized variational mode extraction (ISSA-VME) and multi-scale fuzzy entropy (MFE). The vibration signal was decomposed by ISSA-VME, and the multi-scale fuzzy entropy of the original signal was calculated according to the IMF component. The fault feature set composed of MFE was applied to the SVM classifier optimized by the improved sparrow search algorithm for fault pattern recognition. The experimental results demonstrate that compared with ICEEMDAN and VMD methods, ISSA-VME has a better effect and higher efficiency on vibration signal decomposition. The identification rate of missing teeth, cracks, wear, and other faults of planetary gearbox sun gear by the proposed method reaches 97.50%.

Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability.

Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine

The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. This paper proposes a gearbox fault diagnosis method based on Refined Time-Shifted Multiscale Reverse Dispersion Entropy (RTSMRDE), t-distributed Stochastic Neighbour Embedding (t-SNE), and the Sparrow Search Algorithm Support Vector Machine (SSA-SVM). First, the proposed RTSMRDE was used to calculate the multiscale fault features. By incorporating the refined time-shift method into Multiscale Reverse Dispersion Entropy (MRDE), errors that arose during the processing of complex time series could be effectively reduced. Second, the t-SNE algorithm was utilized to extract sensitive features from the multiscale, high-dimensional fault features. Finally, the low-dimensional feature matrix was input into SSA-SVM for fault diagnosis. Two gearbox experiments showed that the diagnostic model proposed in this paper had an accuracy rate of 100%, and the proposed model performed better than other methods in terms of diagnostic performance.

Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion

Traditional methods of gearbox fault diagnosis rely heavily on manual experience. To address this problem, our study proposes a gearbox fault diagnosis method based on multidomain information fusion. An experimental platform consisting of a JZQ250 fixed-axis gearbox was built. An acceleration sensor was used to obtain the vibration signal of the gearbox. Singular value decomposition (SVD) was used to preprocess the signal in order to reduce noise, and the processed vibration signal was subjected to short-time Fourier transform to obtain a two-dimensional time–frequency map. A multidomain information fusion convolutional neural network (CNN) model was constructed. Channel 1 was a one-dimensional convolutional neural network (1DCNN) model that input a one-dimensional vibration signal, and channel 2 was a two-dimensional convolutional neural network (2DCNN) model that input short-time Fourier transform (STFT) time–frequency images. The feature vectors extracted using the two channels were then fused into feature vectors for input into the classification model. Finally, support vector machines (SVM) were used to identify and classify the fault types. The model training performance used multiple methods: training set, verification set, loss curve, accuracy curve and t-SNE visualization (t-SNE). Through experimental verification, the method proposed in this paper was compared with FFT-2DCNN, 1DCNN-SVM and 2DCNN-SVM in terms of gearbox fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (98.08%).

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 fault diagnosis method for variable speed gearbox bearing based on SET improved multi-source ridge line

Due to the noise interference and frequency ambiguity of gearbox vibration signal under variable speed, the traditional instantaneous frequency estimation method is prone to frequency mutation and low ridge line fusion accuracy. This paper proposes an improved method of extracting multi-source time–frequency ridge lines based on time–frequency analysis by exploring the relationship between the vibration signal of the gearbox without a tachometer and the fluctuation law of the bearing rotation frequency. In the proposed method, the Variable Mode Decomposition (VMD) filter and Synchroextracting Transform (SET) are first used to analyze the time frequency of the gearbox vibration signal. Secondly, the local peak search algorithm obtains multiple time–frequency ridge lines from low-frequency and high-frequency resonance frequency bands. Thirdly, the synchronization process is carried out based on the maximum energy spectrum in the time spectrum. The multi-source ridge line extraction algorithm is improved with reduced frequency mutation and optimized ridge line fusion accuracy. Finally, the optimized speed curve is used to track the vibration signal order of the gearbox. The bearing fault characteristic order is obtained from the angular order spectrum, and the effectiveness of the proposed method is verified by the simulation and the experiment.

Gearbox fault diagnosis method based on deep learning multi-task framework

PurposeIn gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.Design/methodology/approachTo diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.FindingsExperiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.Originality/valueVibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Fault diagnosis of gearbox based on adaptive wavelet de-noising and convolution neural network

In this paper, in order to solve the problem that it is difficult to carry out accurate fault diagnosis for gearbox under noise environment, complete ensemble imperial mode decomposition with adaptive noise analysis (CEEMDAN) is used to solve the sample entropy of the original signal and each intrinsic mode function (IMF) component, adaptive wavelet is adopted to decompose and reconstruct IMF with large sample entropy for noise reduction, and first layer wide convolution kernel deep convolution neural network (WDCNN) and long short term memory (LSTM) are used to extract the basic digital features of the reconstructed signal and the correlation features between the features. Therefore, a new fault diagnosis method for gearbox under noise environment is proposed. Taking the public data set of Jiangsu Qiangpeng Diagnostic Engineering Co., Ltd as the research object, the experiments were carried out with the method proposed in this paper. The experimental results show that the proposed method has high accuracy and strong anti-noise ability. Under the environment of no noise and low noise, the fault diagnosis accuracy of the gearbox is 100%; even if the signal to noise ratio is −4 dB, the fault diagnosis accuracy of the gearbox can still reach 99.97%. Therefore, this paper provides a method support for gearbox fault diagnosis under noise environment.

A Novel Planetary Gearbox Fault Diagnosis Method for Nuclear Circulating Water Pump With Class Imbalance and Data Distribution Shift

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient samples and ideal operating environments. However, in practice, it is hard to acquire adequate fault samples from high-reliability equipment, thus inducing challenges in applying DL for intelligent diagnosis. Moreover, data distribution shift occurs due to working load variation or environment noise interference, severely degrading DL models’ performance. Aiming at the above problems, this article proposes a method called Q networks calibrated ensemble (QCE). Specifically, two improved deep Q networks are employed to enhance the attention to critical feature samples and fault samples by quantifying feature distribution and utilizing fault prior knowledge. Afterward, the calibrated Choquet integral is adopted to achieve the models’ ensemble, further enhancing the generalization ability and robustness of the diagnosis results. The proposed method has been verified on the nuclear circulating water pump (NCWP) test bench. Results indicate the superiority and reliability of the QCE method. In addition, results when working load changes demonstrate the outstanding generalization ability of the proposed method for cross-domain fault diagnosis with class imbalance. Furthermore, gradient-weighted class activation mapping (Grad-CAM) visualization verifies that the proposed method effectively learns fault features instead of noise.

An Anti-Noise Gearbox Fault Diagnosis Method based on Multi-Scale Transformer Convolution and Transfer Learning

Gearbox fault diagnosis methods based on deep learning usually require a large amount of sample data for training, and these data are usually ideal experimental data without noise. However, due to the influence of complex environmental factors, a large number of effective fault samples may not be available and the sample data can be interfered with by noise, which affects the identification accuracy of fault diagnosis methods and the stability of diagnosis results. To improve the resistance to noise while achieving high diagnosis accuracy, a multi-scale Transformer convolution network (MTCN) based on transfer learning is proposed in this paper. Concretely, a multi-scale coarse-grained procedure is incorporated to capture different and complementary features from multiple scales and filter random noises to some extent. Meanwhile, the Transformer composed of an attention mechanism is utilized to extract high-level and effective features and the transfer learning strategy is applied to overcome the limitation of insufficient fault samples for model training. Finally, the experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method has higher accuracy and robustness under noisy environments compared with previous methods.