Compound Fault Diagnosis Research Articles

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.

A label information vector generative zero-shot model for the diagnosis of compound faults

Diagnosis of compound faults remains a challenge owing to the coupling of fault characteristics and the exponential increment of the number of possible fault types. Current compound faults diagnostic methods often require a large number of training data for each type of compound fault. In real-world scenarios, training data of compound faults are usually difficult to acquire and sometimes even inaccessible. In contrast, single fault samples are much easier to obtain. Thus in this paper inspired by the idea of zero-shot learning, we present a novel label information vector generative zero-shot model to identify unknown compound faults, using only single fault samples as the training set. This model comprises several modules, namely label information vector (LIV) definition, feature extractor, and generative adversarial modules, respectively responsible for representing the prior knowledge of specific class labels for the single fault and compound fault, extraction of fault features, and mapping the relationship between the fault features and the fault LIVs. By adversarial training between the samples and LIVs of single faults, the model can generate compound fault features using the compound fault LIVs. Thus the unknown compound faults are identified by measuring the distance between the features extracted from the testing compound fault samples and the generated features from LIVs. The proposed method is evaluated on a self-built experimental platform. The results demonstrate that without any compound fault samples in the training set, the compound fault classification accuracy of the model reaches 78.10%.

Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA

In compound rolling bearing faults, different types of fault signals are coupled and cross-affected, which makes it difficult to separate and extract weak fault features, and making it easy to miss diagnoses or misdiagnose. Aiming at solving this problem, a compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum (MKS) and the aquila optimizer multipoint optimal minimum entropy deconvolution adjusted (AO-MOMEDA) method is proposed. Variational modal decomposition is used to reduce the noise of the original vibration signal and improve the recognition of the MKS so as to predict the fault type and preset the search range of the initial period value. A new fault feature strength index with relative amplitude difference is proposed and, combined with the kurtosis of the envelope spectrum, a multi-objective optimization function is constructed. Through the excellent optimization characteristics of the aquila optimizer, the optimal initial period value and filter length are automatically obtained. The optimized MOMEDA method is used for optimal deconvolution of different types of fault signals. Envelope demodulation of the deconvolution signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal. The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum. The feasibility of the proposed method is verified through measurements of the signals of rolling bearings with three different types of compound faults. The experimental results show that the proposed method can overcome the influence of strong fault signals and improve the accuracy of compound fault identification. Our results have application value in engineering practice.

Untrained Compound Fault Diagnosis for Planetary Gearbox Based on Adaptive Learning VMD and DSSECNN

Fault coupling and fault override are common phenomena when faults occur in different parts of the planetary gearbox. Labeled compound fault samples are very rare or even unavailable in industrial scenarios. Thus, it is a challenging task to decouple the compound fault and detect the compound fault with unequal severity only utilizing single faults. In this paper, an untrained planetary gearbox compound fault diagnosis method based on Adaptive Learning Variational Mode Decomposition (ALVMD) and Dual Scale Squeeze-and-Excitation Convolutional Neural Network (DSSECNN) is proposed. An ALVMD algorithm is established to enhance weak fault characteristics and enrich the diversity of sample information. The DSSECNN intelligent fault diagnosis model is presented, and the lightweight SENet can enhance the sensitivity of the model to channel features. Moreover, the mapping relationship between the compound fault mode and single faults is revealed through the proposed probability formula, which can reduce the dependence of the training model on compound fault samples. Experiments have been performed on the gearbox fault experiment bench in the laboratory to verify the effectiveness and generalization performance of the method. Besides, wind turbine gearbox test results show that the ALVMD-DSSECNN can achieve the highest average accuracy of 98.83% compared with other related methods in variable working conditions, which has a certain guiding significance for practical engineering.

The LST-SATM-net: A new deep feature learning framework for aero-engine hydraulic pipeline systems intelligent faults diagnosis

Accurate identification of faults in the aero-engine hydraulic pipelines is of great significance for condition monitoring of engines as well as safe and reliable operation of aircraft. However, due to the complexity of the structure of the aero-engine pipeline systems, the correlation of the coupling, and the multi-source nature of the excitation it is subjected to, the identification of failure characteristics has been an urgent problem in both engineering and academia. In addition, so there is no more qualitative formula or characteristic index to describe the fault of the aero-hydraulic pipeline. Therefore, to overcome these challenges, in this paper, a new compound faults diagnosis framework, namely Lightweight Spatial-Temporal Model Fusion Self-Attention Mechanism (LST-SATM-Net), is proposed for the aero-engine hydraulic pipeline systems. Firstly, the lightweight spatial feature extraction module is designed to extract fine-grained features from aero-engine hydraulic pipeline fault data and the bi-directional temporal feature extraction module is designed to extract coarse-grained features with temporal patterns from fine-grained features, achieving coarse and fine-grained feature fusion. Then, a self-attention mechanism is integrated into the spatial–temporal model for optimization, enabling the model to automatically learn important features from data with multiple sources of coupled vibration excitation, enhancing the performance of critical features from the correlated coupling of compound faults. The powerful noise interference is suppressed and the weighting coefficients are continuously updated to make the final decision more focused. Consequently, the recognition accuracy of the LST-SATM-Net model is improved and the feature learning of the neural network model is more flexible. The results demonstrated that compared with seven other advanced methods, the proposed LST-SATM-Net model is capable of diagnosing compound faults in the hydraulic pipeline system of aero-engine more accurately after being trained by experimental measurement data. This impressive finding indicates that the proposed LST-SATM-Net model can be applied to the actual condition monitoring of aero-engine hydraulic pipelines to reduce maintenance costs and downtime.

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.

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.

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

Gearbox compound fault diagnosis method based on deep adversarial graph convolution transfer learning network under low label ratios

Advancement in measurement techniques has dramatically contributed to the development of the modern manufacturing industry. As the primary fault causing unplanned downtime of mechanical equipment, gearbox compound faults are usually coupled by single faults with unequal severity and are difficult to obtain. In industrial scenarios, monitoring data for extreme operating conditions is not available in advance, and labeling samples is time-consuming and costly. However, current research on unseen compound fault diagnosis relies on numerous labeled samples to train the model, and few studies are conducted on transfer learning and unseen compound fault diagnosis under low label ratios. To address the issue, a gearbox compound fault diagnosis method based on deep adversarial graph convolution transfer learning network (DAGCTLN) under low label ratios is proposed. Specifically, a novel DAGCTLN model, including a feature extractor, two label classifiers, and a discriminator, is constructed to realize the diagnosis of faults in the transfer domain and unseen compound faults in the source and target domains. The feature extractor of a three-layer graph convolutional network is presented to achieve deep extraction of fault features under low label ratios. Then a domain space adversarial mechanism between the feature extractor and discriminator is used to achieve global alignment of the source and target domain features. Furthermore, two label classifiers are constructed, and the adversarial adaptation of the decision boundary is realized by maxi-min the classifier difference to achieve subdomain alignment of the same class features in all domains. Experimental results indicate that DAGCTLN can achieve the highest fault diagnosis accuracy in the transfer domain compared to state-of-the-art algorithms. The average diagnosis accuracy of compound faults in all domains can reach 98.41% even if the label ratio is only 0.1, which provides guiding significance for the safe operation and predictive maintenance of mechanical equipment.

A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.

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.

Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis.

Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel-space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.

Compound Fault Diagnosis of Harmonic Drives Using Deep Capsule Graph Convolutional Network

Harmonic drive is a key component of the industrial robot. Because of its large reduction ratio and excessive dynamic loading, various kinds of faults may occur. In particular, since the robot is an integrated system, it is not unusual to have multiple harmonic drives malfunction simultaneously, which is difficult to diagnose. In practice, these kinds of compound faults are often mislabeled as single faults causing missing repair. In this paper, we propose a deep capsule graph convolutional network (DCGCN) approach to diagnose compound faults of harmonic drives. First, the multi-sensor data is used to obtain the frequency spectrum of the fault signal and construct the label relationship map of the adjacency matrix. Second, the deep capsule network is used to learn the representation of the fault vector, and the graph convolutional network is used to learn the relationship between different single-label faults. Third, the two networks are combined to obtain diagnosing results. Finally, the dynamic routing algorithm and the margin loss function are used to optimize the DCGCN. The experimental results show that the proposed DCGCN can effectively diagnose compound faults under varying working conditions, outperforming other existing state-of-the-art methods.

Compound faults diagnosis of rotating machinery via enhanced two-layer sliding correlated kurtosis

Compound faults identification from vibration signals is still a challenge for rotating machinery because the multiple periodic impulsive fault signals may oscillate with the same frequency. In this research, a method termed enhanced two-layer sliding correlated kurtosis (TLSCK) is presented for isolating and identifying the compound defects from the monitored data containing strong background noise and compound faults. The method contains two main steps: the dual-tree complex wavelet package transform (DTCWPT) based correlated kurtogram is conducted on the raw monitored data as a frequency filtering step; further, the enhanced TLSCK method is conducted to diagnosis the compound defects from above filtered signals. The output signal of the enhanced TLSCK could locate the occurrence of interested fault impulses, while the unwanted vibration components and residual noise are well eliminated. Both numerically simulated and experimentally measured vibration data of damaged rolling bearings are analyzed via the presented method to test its performance, and the analysis results validate that the proposed method is effective in detecting compound faults of rotating machinery.

Periodic component pursuit-based kurtosis deconvolution and its application in roller bearing compound fault diagnosis

As an effective roller bearing fault diagnosis method, Adaptive Periodic Mode Decomposition (APMD) method has excellent capability of repeated transient extraction. In APMD, the Maximum Likelihood Estimation (MLE) method is used to calculate the projection energy, and the period corresponding to the maximum projection energy is taken as the main period of the present signal. When the noise is large, the noise energy will greatly interfere with the projection energy, thereby affecting the accuracy of the period estimation. To solve this problem, a Periodic Component Pursuit-based Kurtosis Deconvolution (PCPKD) method is proposed, which uses Maximum Reweighted Kurtosis Deconvolution (MRKD) to de-noise the signals that need to estimate the main periods, so as to reduce the influence of noise energy on the periodic estimation to the greatest extent. The proposed method is applied to roller bearing compound fault diagnosis, and the experimental results show that PCPKD has a good ability of signal period enhancement and can effectively segment multi frequency components.

A novel adaptive resonant band detection method based on cyclostationarity for wheelset-bearing compound fault diagnosis

Bearing condition is crucial to train operation safety. In order to accurately locate the resonant zone caused by bearing failure, a compound fault resonant band detection method is proposed. Firstly, a multi-level frequency band segmentation method based on L2/L1 norm of envelope spectrum is designed. Secondly, based on the results of frequency band segmentations, ICS2, a health index that can accurately characterize the cyclostationarity of repetitive transients, is introduced to guide the detection of fault resonant band. By using different multiples of rotation frequency as window length, the proposed method adaptively realizes multi-level band segmentation to extract the subband with the largest ICS2. Moreover, the proposed method has the capability of multiple fault diagnosis. Simulation signals and bench signals verify the effectiveness of the proposed method.

Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. Multi-class classification is used to classify faults into different single types, whereas multi-label classification classifies faults into compound types. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. Diagnosis of untrained compound faults is also a merit. In this study, input data were first preprocessed with short-time Fourier transform. Then, a model was built for classification of the state of the system based on multi-output classification. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. This study proposes an effective model based on multi-output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance.

A novel compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer

Metric transfer learning overcomes the problem of no domain distribution alienation in traditional sample transfer, and also improves the classification accuracy. However, some problems exist; for example, the target domain must have labelled samples without considering the manifold connection between data samples, and the algorithm is not robust under sparse samples. A compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer is proposed. First, the pseudo-label of the target domain is acquired through a weighted graph constructed on the source domain with known fault labels. In order to ensure the accuracy of metrics in manifold space, we adopt the method of graph label propagation (LP) to gradually spread the label information. Then, the sample data are mapped to the low-dimensional manifold space by locality preserving projections (LPP), which gives higher weight to the source domain samples close to the target domain to solve the domain offset problem. At the same time, combined with the labelled samples of the target domain in LP, the manifold distance measure is learned to minimize the intra-class distance and maximize the class spacing, eliminate the overlap phenomenon in the domain and realize the distribution alienation between classes in the domain. After each iteration, unsupervised manifold metric transfer learning is realized by increasing the range of LP and gradually reducing the error of graph LP. Experiments show that the new unsupervised transfer learning method has better fault identification ability than the semi-supervised transfer learning method. In the case of sparse training samples, it still maintains a high diagnostic accuracy, and the highest fault recognition accuracy can reach more than 97% with good robustness.

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings

Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.