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Accurate fault diagnosis of rolling bearings is necessary to ensure the safe and reliable operation of mechanical equipment. Aiming at the problem of low accuracy of rolling bearing fault diagnosis, a rolling bearing fault diagnosis algorithm based on time-frequency feature extraction and improved bat algorithm-support vector machine (IBA-SVM) mode...
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... the analysis, 2500 sample points of inner ring fault, outer ring fault, rolling element fault and normal state with the fault diameter of 0.007 inch are selected, and the waveforms in time-domain under various states can be obtained after simple processing, as shown in Fig. ...
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... can be seen from Fig. 3, the difference between the various states is not sharp enough. If the classification only relies on a feature, satisfactory results cannot be achieved; if all the feature data in the original data set are used to classify the fault, the running speed of the model will inevitably be affected, and the classification accuracy is low, ...
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... frequency is set to 48000 Hz, the starting point of the sample is randomly selected, and the length of each sample is 864. This length includes two fault cycles, which does not reduce the accuracy due to too long length, but also retains most of the data features. Fig.4 shows the frequency spectra of the four vibration time-domain signals in Fig. 3 after FFT ...
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Citations
... In Discrete Fourier Transform (DFT) engineering, FFT is an implementation method [27] in which the calculation process is simplified. It is easier to obtain frequency domain signals by performing bearing time domain FFT transformation. ...
Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.
... Time domain analysis method mainly through the extraction of rolling bearing vibration signal crest factor, kurtosis factor, root mean square factor, mean value coefficient, waveform factor, margin coefficient, impulse factor and other time domain characteristic parameters [14], and then use the time domain characteristics of the fault diagnosis [15], so the advantages of time-domain analysis in bearing fault diagnosis are its simplicity and efficiency in operation, enabling the intuitive and clear capture of fault characteristics, such as impact impulses [16]. This method is particularly suitable for the detection of early-stage bearing faults, as it can effectively identify abnormal vibration signals. ...
Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.
... Unique fault patterns are encapsulated within distinct frequency bands, rendering the frequency domain signal more adept for fault diagnosis compared to the original vibration signal [41]. Zhang et al. conducted an FFT transformation on vibration signals and derived features from the resultant spectral signals to facilitate bearing fault diagnosis [42]. On the other hand, Mao et al. applied an FFT transformation to the bearing vibration signal, and the ensuing spectral information underwent processing via a generative adversarial network (GAN) [43], which was employed for the early detection of bearing failures. ...
In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study presents a solution involving deep feature construction and an unsupervised domain adaptation strategy for rolling bearing fault diagnosis across varying working conditions. The proposed methodology commences by subjecting the original vibration signal of the bearing to a fast Fourier transform (FFT) to extract spectral information. Subsequently, an innovative amalgamation of a one-dimensional convolutional layer and an auto-encoder were introduced to construct a convolutional auto-encoder (CAE) dedicated to acquiring depth features from the spectrum. In a subsequent step, leveraging the depth features gleaned from the convolutional auto-encoder, a balanced distribution adaptation (BDA) mechanism was introduced to facilitate the domain adaptation of features from both the source and target domains. The culminating stage entails the classification of adapted features using the K-nearest neighbor (KNN) algorithm to attain cross-domain diagnosis. Empirical evaluations are conducted on two extensively used datasets. The findings substantiate that the proposed approach is capable of accomplishing the cross-domain fault diagnosis task even without labeled data within the target domain. Furthermore, the diagnostic accuracy and stability of the proposed method surpass those of various other migration and deep learning approaches.
... There is much related research on traditional vibration signals and acoustic emissions. Zhang et al. extracted features from bearing vibration signals in the time and frequency domains and applied Multidimensional Scaling (MDS) for feature selection and finally obtained highly accurate fault diagnosis results by fusing a support vector machine (SVM) [7]. Sun et al. combined In 2018, Chen, C.L.P. proposed the Broad Learning System (BLS), which is a random vector function linked neural network. ...
To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN.
... The use of vibration signal diagnosis method has been very rich. Traditional methods: short-time Fourier transform [1], wavelet packet energy, principal component analysis and so on. Shao Yimin uses kurtosis to measure the extraction quality of the filter; Hu Qiao strengthens the sensitive features extracted by wavelet transform [3]; Li Changyou adopts principal component analysis to extract the feature vector. ...
Rolling bearing vibration signals are often subject to noise interference, and early failure performance is not obvious. In this paper, An Boxplot-Hessian Algorithm is proposed. It uses Boxplot Algorithm to identify and deal with the noise signal. It uses the Hessian Algorithm to reduce data dimensionality, which has the advantage of no convexity requirement for data sets and it can overcome the presence of holes in the sample. So it can be used for feature extraction. Meanwhile, compared with the results of using the non-de-noising manifold learning algorithm by showing example. It is proved that this method can overcome the influence of noise on manifold learning algorithm and has good state recognition effect.
Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.
Traditional approaches to the intelligent fault diagnosis of rolling bearings have predominantly relied on manual expertise for feature extraction, a practice that compromises robustness. In addition, the existing convolutional neural network (CNN) is characterized by an overabundance of parameters and a substantial requirement for training samples. To address these limitations, this study introduces a novel fault diagnosis algorithm for rolling bearings, integrating a one-dimensional convolutional neural network (1DCNN) with a support vector machine (SVM) to form an enhanced 1DCNN-SVM model. This model is further refined using the sparrow search algorithm (SSA) for the optimal adjustment of the parameters of 1DCNN-SVM. Specifically, by substituting the CNN’s final softmax layer with an SVM, the model becomes better suited for processing limited data volumes. In addition, the incorporation of batch normalization and dropout layers within the CNN framework significantly augments its fault classification accuracy for rolling bearings, concurrently mitigating the risk of overfitting. The SSA is subsequently applied to refine three principal hyper-parameters: batch size, initial learning rate, and the L2 regularization coefficient, thereby overcoming the challenges associated with manually adjusting parameters, such as extended processing times and unpredictable outcomes. Empirical tests on Case Western Reserve University (CWRU) datasets revealed the model’s superior performance, with the SSA-optimized 1DCNN-SVM showcasing diagnostic accuracies over 98%, marked improvements over conventional models, and a significant reduction in processing times. This method not only marks a significant advancement in intelligent fault diagnosis for rolling bearings but also demonstrates the potential of integrating machine learning for more precise and efficient diagnostics. The SSA-1DCNN-SVM model, optimized for accuracy and minimal data use, sets a new standard in fault diagnosis, relevant for machinery health monitoring and maintenance strategies across various industries.
Fault diagnosis based on vibration signals is widely used in various industrial production processes to prevent catastrophic accidents and ensure timely repairs. However, traditional fault diagnosis methods provide limited accuracy due to their inability to visually represent fault characteristics and heavy reliance on expert involvement. In this study, we propose a time series image generation scheme that incorporates a convolutional neural network to perform data-driven analysis and open-set classification on fault data to represent faults intuitively and diagnose them more accurately. In this study, based on the basic G image, we proposed two series of extended coding methods, G-TF and TF-G to convert the one-dimensional time series signal into a grayscale image from different perspective for figurative expression, so as to adapt to different fault diagnosis scenarios. Then, a convolutional neural network is designed to maximize recognition accuracy based on faster calculation. To demonstrate the effectiveness of the proposed method, three types of faults are tested in this study, i.e., faults with motor bearings, self-priming centrifugal pumps, and hydraulic pumps. The experimental results demonstrate that, when the most suitable method is employed, the proposed approach achieves better recognition performance in diagnosing these three types of faults. The proposed method provides a new solution for future fault diagnosis tasks.
In practical engineering, due to the complex and variable working conditions of rolling bearings and the highly nonlinear characteristics of fault signals, especially in the cases of limited fault samples, it is very difficult to achieve satisfactory diagnostic results with the traditional rolling bearing fault diagnosis method. Therefore, in this paper, a two-way parallel rolling bearing intelligent diagnosis method based on multi-scale center cascaded adaptive dynamic convolutional residual network (MCADCRN) and Swin transformer (SwinT) is proposed. Firstly, the original signals are transformed into the two-dimensional time-frequency map by using continuous wavelet transform to preserve the time-frequency characteristics of the original signals. Secondly, a multi-scale center-cascaded dynamic convolutional residual block (MCDCRB) and a multi-dimensional coordinate attention mechanism (MDCAM) are designed to extract the fault features. Through multi-scale convolutional operations, MCDCRB can capture the feature information in different frequency ranges and use a cascade structure to progressively extract higher-level features. At the same time, MDCAM dynamically selects and fuses the features of different scales to reduce the information redundancy and capture the key features; next, the MCADCRN network is constructed by multiple MCDCRBs and a MDCAM to capture the local features; then, the global features of the fault information are captured by using the mechanism of the moving window self-attention in the Swin transformer network; Finally, the local features are fused with the global features and the recognition results are output. The experimental validation is carried out with two different bearing datasets, and the average diagnostic accuracy of the proposed method under variable operating conditions is 99.64%, which is 1.97, 1.53, 1.71, 1.16, and 2.84 percentage points higher than that of the five advanced methods, respectively. Under limited sample conditions, especially when there are only 50 samples, the diagnostic accuracies of the proposed method are 96.42% and 90.89%, respectively. The results verifies the effectiveness of the proposed method.
