Compound Fault Research Articles

Research on composite fault diagnosis technology of underwater vehicle actuator with positioning error constraint

When diagnosing the composite fault of the actuator, the characteristics of the motion force of the underwater vehicle are not analyzed, and there are diagnostic errors, resulting in the low accuracy of the diagnosis technology. In order to solve this problem and improve the operation safety of underwater vehicle actuators, this paper proposes a compound fault diagnosis technology for underwater vehicle actuators under positioning error constraints. Analyze the motion force of the underwater robot actuator, control the motion of the underwater robot actuator according to the analysis results, and extract real-time data parameters according to the control results. Under the constraint of positioning error, the composite fault features of the underwater robot actuator are divided, and the diagnosis model is built according to the deep fusion of the features to complete the fault diagnosis. The experimental results show that the technology can diagnose the composite fault data of the actuator, and the positioning error of the horizontal axis and the horizontal axis can be significantly improved, which can improve the diagnosis effect of the composite fault of the actuator and to the improvement of underwater robot running safety of actuators provide certain reference.

A study on the decoupling of helical gear compound uniform wear and tooth root crack faults via gear condition indicators based on dynamic modeling

Diagnosis of compound faults plays a significant role in evaluating the health status of a gearbox. The difficulty of compound fault diagnosis is due to the entanglement of fault symptoms. In this work, diagnosis of single and compound uniform wear and tooth root crack via gear condition indicators have been studied analytically. A three-dimensional dynamic model that can include several design parameters and working conditions has been employed to model the dynamic of compound faults for the first time. The effect of fault severity, noise, working conditions, and design parameters on the diagnosis efficiency have been investigated. The qualitative and quantitative assessment of fault diagnosis has been accomplished via t-SNE and Generalized Discriminative Value (GDV) measures, respectively. The results show that the gear condition indicators are more suitable to assess the general health state of the gearbox. However, their ability to detect the type of fault and decouple compound faults are limited. Furthermore, design parameters of the gear transmission system, such as helix angle and gear width, can highly affect the fault diagnosis efficiency.

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

Identification of compound faults of rolling bearing grounded on 1D-LBP and first-order difference of vibration signal

Identification of compound faults of rolling bearing grounded on 1D-LBP and first-order difference of vibration signal

Compound fault separation and diagnosis method for online rolling bearings based on RSEUnet and 1DCNN

Compound fault signals interfere with one another, resulting in an inconspicuous feature extraction that requires sophisticated signal processing techniques and expert experience. However, good online diagnostic methods are not available to carry out this process. This paper proposes a method based on Residual Connection and Squeeze-and-Excitation Unet (RSEUnet) and one-dimensional convolutional neural network (1DCNN). The process includes fault separation and diagnosis. First, the feature extraction module of the RSEUnet network introduces an attention mechanism and a residual connection that adaptively assigns various weights to different channels. This model is used to train the maps of the fault signal after time-frequency transformation. Ideal binary masks with excellent performance are the training targets to complete the intelligent separation of compound faults. Second, the 1DCNN is used as a feature learning model to efficiently learn the features of single faults from time-domain signals. An embedded system consisting of a Jetson Nano and a signal acquisition circuit is then built to perform online diagnosis. The test is carried out on the fault experimental platform. Results show that the method has an accuracy of 99.71%, making it highly suitable for the diagnosis of bearing compound faults.

Intelligent Compound Fault Diagnosis of Roller Bearings Based on Deep Graph Convolutional Network.

The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings.

A Transient Fault-signal Extraction Scheme for Bearing Compound Fault Intelligent Diagnosis based on Vibration Signals

As a compound fault of bearing is characterized by complexity, disproportion, and interaction, its fault diagnostic accuracy tends to decline sharply. To solve this problem, the present study proposes a transient fault-signal extraction scheme for bearing compound fault intelligent diagnosis. First, the single fault vibration and compound fault vibration signals are transformed into the time-frequency domain by wavelet transform. Then, according to the normal condition signal, the transient fault signal of the single signal and compound signal is extracted through the positive k sigma principle. Next, the single fault signal symptom parameters are calculated to build the fault diagnostic model. Thereafter, the symptom parameters of the extracted compound fault transient signal are brought into the diagnostic model to obtain the model output result. Finally, according to the developed fault diagnosis discrimination criterion, the method can diagnose the compound fault successfully. The effectiveness of the proposed method is validated by bearing fault vibration signals under various conditions. The results show that the diagnostic method has superior performance in intelligently diagnosing the bearing compound fault.

MJAR: A novel joint generalization-based diagnosis method for industrial robots with compound faults

Compound faults inevitably occur in multi-joint industrial robots resulting in excessive vibration. Intelligent diagnosis for the occurrence and position of fault joints can efficiently reduce the maintenance cost. Compared with laboratory disassembly parts, joint diagnosis of in-situ industrial robots is more challenging due to coupling of multiple subsystems and the industrial noise interference. This paper proposes a novel joint generalization-based fault diagnosis method for industrial robots via the multi-joint attention residual network (MJAR) model. Each joint signals are independently input to parallel residual convolution with unidirectional matrix kernel (ResCUM) in multi-joint decoupling attention (MJDA) module, which is exclusively designed combining residual and attention mechanism to provide decoupling capability of compound faults. And the multi-spatial reconstruction (MSR) module based on sparse sampling is design to provide multi-scale feature extraction adapted to real industrial signals. MJAR can diagnose unseen compound fault combinations without using any transfer strategy as the robot joint generalization-based fault diagnosis (RJGFD) framework. Integrated experiment results show that the MJAR is superior to some advanced congener DL methods.

Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal decomposition method, which can decompose a complex signal into several components and extract the periodic characteristics of the signal. However, the mode generation method adopted by RFMD does not consider the physical meaning of the component signal, which makes over-decomposition when dealing with real-life gear signals with complex modulation characteristics, thus destroying the integrity of the signal sideband, increasing the difficulty of subsequent analysis, and even losing key fault information. The iterative envelope-segmentation algorithm combines the modulation characteristics of the local fault gear signal and divides the original signal into a limited number of dominant frequency bands containing the modulation region in the Fourier spectrum, thereby ensuring that the obtained frequency bands contain rich fault information. Based on the above algorithm, a new adaptive decomposition method is proposed in this paper, which is adaptive spectrum segmentation Ramanujan decomposition (ASSRD). ASSRD uses fault envelope harmonic noise ratio as the index to evaluate the fault information content of component signals and uses it to assist the iterative envelope-segmentation algorithm to complete the adaptive segmentation of the Fourier spectrum. Finally, based on the segmentation result, the inverse RFT reconstruction of each frequency band is performed. Thus, the signal is decomposed into a finite number of component signals containing rich fault information. In addition, through the experiment on the gear simulation signal and the measured crack fault gear signal, the ASSRD method is compared with the original RFMD method and the existing ensemble empirical mode decomposition (EMD), variational mode decomposition, empirical wavelet transform, and singular spectrum decomposition method, verifying the feasibility and superiority of ASSRD in gear fault diagnosis. Besides, a comparative experiment based on compound faults diagnosis is carried out, in which ensemble EMD, Fourier decomposition method, empirical wavelet transform, and sparse decomposition are involved. The results show that the proposed method can extract the local fault information in the gear signal more effectively, and the performance is better than the comparison method.

Broad zero-shot diagnosis for rotating machinery with untrained compound faults

Compound fault diagnosis of rotating machinery is of great significance for the operational reliability and security of manufacturing equipment. Since the possible compound fault types increase exponentially, the compound faults appear in the test phase may not be covered during training, posing great challenge for machine health monitoring. Recently, several methods attempt to construct the semantic space for untrained compound faults. However, the semantic space suffers from the low-fidelity to identify the untrained compound faults. Besides, most of these methods only focus on untrained compound faults, ignoring more common single faults in the test set. To address these issues, a novel broad zero-shot diagnosis method (BZSD) is proposed to identify both single faults and untrained compound faults. Firstly, the multiresolution permutation entropy is presented to identify single faults and preliminarily screen out the untrained compound faults, which can prevent the compound fault from being biased towards the trained single fault. Then, a high-fidelity semantic space is constructed to classify the pre-screened compound faults. The proposed fault semantics are close to the ground truth semantics, which is conducive to improving diagnostic accuracy. The experiments demonstrate the effectiveness and superiority of the BZSD for rotating machinery with untrained compound faults.

An Intelligent Diagnosis Approach Combining Resampling and CWGAN-GP of Single-to-Mixed Faults of Rolling Bearings Under Unbalanced Small Samples

Rolling bearing is a key component with the high fault rate in the rotary machines, and its fault diagnosis is important for the safe and healthy operation of the entire machine. In recent years, the deep learning has been widely used for the mechanical fault diagnosis. However, in the process of equipment operation, its state data always presents unbalanced. Number of effective data in different states is different and usually the gap is large, which makes it difficult to directly conduct deep learning. This paper proposes a new data enhancement method combining the resampling and Conditional Wasserstein Generative Adversarial Networks-Gradient Penalty (CWGAN-GP), and uses the gray images-based Convolutional Neural Network (CNN) to realize the intelligent fault diagnosis of rolling bearings. First, the resampling is used to expand the small number of samples to a large level. Second, the conditional label in Conditional Generative Adversarial Networks (CGAN) is combined with WGAN-GP to control the generated samples. Meanwhile, the Maximum Mean Discrepancy (MMD) is used to filter the samples to obtain the high-quality expanded data set. Finally, CNN is used to train the obtained dataset and carry out the fault classification. In the experiment, a single, compound and mixed fault cases of rolling bearings are successively simulated. For each case, the different sets considering the imbalance ratio of data are constructed, respectively. The results show that the method proposed significantly improves the fault diagnosis accuracy of rolling bearings, which provides a feasible way for the intelligent diagnosis of mechanical component with the complex fault modes and unbalanced small data.

A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis.

This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability.

Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

In general, gearbox is prone to occur compound fault due to its harsh working environment and its fault vibration signal contains multi-components which correspond to each gearbox parts. As the multi-components are often coupled with each other and accompanied by strong noise which brings great difficulties to diagnose fault, however, the existing diagnosis methods are mainly applied on single fault rather than the entire gearbox health maintenance, therefore, this paper presents a gearbox compound fault diagnosis method and develops a diagnosis system which has potential value for gearbox health maintenance. In specific, on account of the morphological difference between multi-components, this paper uses resonance sparse signal decomposition (RSSD) to decompose the fault vibration signal into high and low resonance components respectively for achieving gearbox compound fault separation. Furthermore, as for low resonance component containing rolling bearing fault information, a weak fault feature extraction algorithm based on singular value decomposition (SVD) and cepstrum pre-whitening stochastic resonance is proposed, besides, aiming at the high resonance component containing gear fault information, an early gear fault warning algorithm based on local mean decomposition and two-dimensional approximate entropy of chaotic oscillator is also given. Finally, a gearbox fault diagnosis system, which has the ability such as the gearbox vibration signal acquisition, fault indicator warning, health status evaluation, fault signal storage is developed. Simulation validation and comparison prove the effectiveness of proposed method in this paper.

Fault diagnosis of rolling bearing based on cross-domain divergence alignment and intra-domain distribution alienation

When the current transfer learning algorithm is applied to the field of bearing fault diagnosis under different working conditions, it only focuses on reducing the cross-domain distance or the distribution difference within the domain, and does not consider the domain tilt. When the fault samples are scarce, the degradation of recognition ability is more obvious. A fault diagnosis method for rolling bearings based on cross-domain divergence alignment and intra-domain distribution alienation (CDDA-IDDA) is proposed. Firstly, aiming at the cross-domain tilt in the domain data space of variable working conditions, the overall divergence matrix of source domain and target domain is constructed, and the cross-domain divergence alignment is carried out. Then, aiming at the overlapping phenomenon of categories in the domain, based on the distribution adaptation weighted conditional distribution, the spatial distribution of different categories in the same domain is further alienated. Finally, the regularization term is introduced under the framework of structural risk minimization. On the basis of fully retaining the internal structure of the data, a multi-classifier with strong transfer ability is obtained by iteration. Experiments show that the proposed method is better than some mainstream transfer learning algorithms in multi-fault, multi-degree recognition and compound fault diagnosis. In addition, the proposed method still has high diagnostic accuracy when there are few labeled training samples. When the ratio of labeled source domain samples to unlabeled target domain is 1:50 (16 labeled data), the average accuracy of the transfer task reaches 97.78 %.

Compound faults identification of rolling bearing based on one-dimensional mixed binary pattern

To address the difficulty in extracting the characteristics of combined failure of rolling bearings, a novel fault identification method, one-dimensional mixed binary pattern (1D-MBP), is proposed. Firstly, regarding that variance can better highlight the partial weak failure information of sequence than median, the binarization of vibration signals based on one-dimensional local binary pattern (1D-LBP) is carried out with variance as criterion. Meanwhile, binarization sequence is converted to decimal sequence as the local conversion signal (LCS). Secondly, considering that failure information of rolling bearings is reflected in partial and global texture, the paper proposed the method of gaining global weighted mean value by weighting partial mean value with partial energy. Binarize vibration signals based on one-dimensional global binary pattern (1D-GBP) with obtained global weighted mean value as criterion and convert the obtained binarized sequence into decimal as the global conversion signal (GCS). Thirdly, signals are reconstructed by obtained LCS and GCS to get 1D-MBP signal. Finally, combined failure types of bearing can be identified from 1D-MBP signals based on TEAGER energy spectrum (TES). The result indicates that the 1D-MBP not only can effectively control noise components in vibration signal but also can precisely identify the combined failure types of rolling bearings.

Paired ensemble and group knowledge measurement for health evaluation of wind turbine gearbox under compound fault scenarios

To simultaneously identify the specific single or compound faults and evaluate the health condition of wind turbine gearbox accurately, a novel health evaluation method is proposed based on paired ensemble (PE) and group knowledge measurement (GKM). In this method, PE is constructed for the compound fault diagnosis, and the fault probability distribution is derived and translated into the status membership function to quantify the fault statue membership accurately. Then, a fuzzy derivation method named GKM is proposed to estimate the fault influence weights for gearbox’s behavior, and abundant condition-based factors can be considered such as decision-making style and expert authority level. Meanwhile, the health indicator is defined by the ratio system to evaluate the health condition of wind turbine gearbox by integrating estimation information. The effectiveness of the proposed method is validated on a compound fault test platform of wind turbine gearbox. The experimental results indicate that compared with the existing methods, the proposed method is competitive in terms of diagnostic accuracy and evaluation reliability.

Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

In production practice, the signal gathered by a sensor often includes strong ambient noise, and its composition is complex. Focusing on the problem that traditional methods are difficult to separate and extract fault frequencies from strong background noise, a novel compound fault blind extraction method based on improved sparse component analysis (ISCA) and improved maximum correlation kurtosis deconvolution (IMCKD)-named ISCA‐IMCKD- is suggested. Initially, the signal that the sensor has collected is shifted into time-frequency area signal by short-time Fourier transform (STFT). In addition, the single source domain characteristic data is screened by improved single source point detection to determine the number of sources. Second, the ISCA method is optimized by using cosine distance improved fuzzy C-means clustering, which is utilized to further process the characteristic data to calculate the mixing matrix. Moreover, the estimated source signal is initially extracted according to the membership degree of clustering results. Finally, the estimated source signal is shifted into the time area by inverse STFT transform, and the IMCKD is employed to enhance the characteristics of the projected source signals. Meanwhile, the initially estimated source signal is completely separated, and the defect frequencies of the composite faults are finally extracted by envelope analysis. Simulation experiments and measured data are employed to certify the viability of the proposed means. The defect detection of rolling bearings is finished while the time cost is significantly saved.

Application of Demodulation Frequency Band Optimization in Compound Fault Detection of Rolling Element Bearing Based on Fast Spectrum Correlation

The key to demodulation analysis is determining the appropriate demodulation frequency band. However, the coupling of compound faults significantly increases the difficulty. How to determine the fault-related demodulation frequency band is a challenge for the application of demodulation analysis in compound fault detection. To address this issue, a demodulation frequency band optimization method based on fast spectral correlation (Fast-SC) is proposed to detect compound faults of rolling element bearing in this paper. Firstly, the bi-variable map of vibration signal is obtained by Fast-SC. Secondly, the bi-variable map is sliced according to the theoretical fault-related frequency along the carrier frequency axis, obtaining a curve of correlation value versus carrier frequency, which is regarded as the target spectrum correlation curves (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scc</sub> ). Thirdly, the carrier frequency corresponding to the maximum value of the T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scc</sub> obtained from each component is used to determine the initial demodulation frequency band. Fourthly, the crest of envelope spectrum (EC) is used to optimize the demodulation frequency band. Finally, the characteristic frequencies are exposed by utilizing the envelope demodulation on the frequency bands of each component. Simulation and experiment show that the proposed method is effective for the detection of rolling element bearing compound faults. Compared with maximum correlated kurtosis deconvolution(MCKD) and variational mode decomposition(VMD), the calculation cost of the proposed method is significantly reduced to about 6.6% of the above two methods.

A new mechanical compound fault diagnosis method based on morphological filter and RLS

Mechanical compound fault diagnosis is a thorny issue in the industry. To overcome this problem, a method named morphological filter—recursive least squares (MF-RLS) is proposed in this paper. In the proposed method, MF-RLS is used to sequentially separate the different fault impulse features by decomposing the observed signal into a series of iterative morphological filtering components (IMFCs). First, the measured signal is decomposed into different scales by the multi-scale MF. Then, the product of kurtosis and envelope harmonic-to-noise ratio index is used to select the best IMFC. Finally, the IMFC is input to RLS to separate other fault features. After continuous iterations, the separation and extraction of the compound fault impulse features are achieved. The simulation and experiment of the mechanical compound fault have verified the effectiveness of the proposed method.

Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet.

Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model's ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.