Aero-engine Bearings Research Articles

An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set

Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan and cost. However, most current models are black-box models, such as deep learning models such as deep neural networks. The decision-making process of these models is more complex and lacks interpretability, which results in insufficient credibility of the results. Furthermore, data collected in real industrial environments can suffer from unbalanced sample categories. Moreover, the models can suffer from local ignorance in the prediction process. These problems can lead to a decrease in the prediction accuracy of the model. Therefore, a fault diagnosis method based on the interpretable belief rule base with a dynamic power set (D-HBRBP-I) is proposed in this study. First, a diagnostic model based on a belief rule base with a dynamic power set was used to address the problem of sample category imbalance and local ignorance. Second, optimizing the model via the P-CMAES algorithm with interpretability constraints can ensure the interpretability of the model after optimization. Finally, experiments were conducted on an aeroengine-bearing dataset. The results show that the proposed model effectively solves the above problem while achieving 99% accuracy.

A fault detection of aero-engine rolling bearings based on CNN-BiLSTM network integrated cross-attention

Abstract Aero-engine rolling bearings are essential for engine health, in which disruptive failures can be prevented and reduce great losses in air flight. To improve the efficiency of fault detection, an improved network, named CNN- BiLSTM -Cross-Attention (CBLCA) was proposed. The Bidirectional Long Short-Term Memory (BiLSTM) layer captures the temporal features as the input data. The cross-attention mechanism is integrated with the Convolutional Neural Networks (CNN) layer and the BiLSTM layer respectively. More important feature information can be identified with the CBLCA model. The proposed model was also validated with the open-sourced aero-engine rolling bearings data set. To improve the identification accuracy, a novel method that combines Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) is used for the data preprocessing. Each original signal sample is transformed into a feature set containing richer information, and the number of features significantly increased in the entire dataset. Compared with some existing LSTM models, such as LSTM, BiLSTM, CNN-BiLSTM, and CNN-LSTM, the classification accuracy was increased by 55%, 54%, 5%, and 7%, respectively. The processing method for vibration signals and the CBLCA model can improve the accuracy and reliability of fault diagnosis for aero-engine rolling bearings.

Prior-shape-guided photometric stereo model for 3D damage measurement of worn surfaces

Three-dimensional (3D) damage measurement often encounters limited accuracy due to the non-Lambertian effects on worn surfaces. To address this issue, a prior-shape-guided surface reconstruction model is introduced with photometric stereo vision. Initially, an illumination calibration method is constructed to mitigate the uneven brightness on worn surface images. Subsequently, a prior-shape-guided network is developed that employs the feature aggregation strategy and prior shape uncertainty to guide worn surface reconstruction. Experimental results reveal that the proposed model can effectively recover the typical morphologies of worn surfaces, and the root mean square error is reduced from 7.39 to 1.59 when compared to existing methods. Most importantly, this model has been applied to detect aero-engine bearings and achieve 3D quantitative characterization for worn damages.

RTCA-Net: A New Framework for Monitoring the Wear Condition of Aero Bearing with a Residual Temporal Network under Special Working Conditions and Its Interpretability

The inter-shaft bearing is the core component of a high-pressure rotor support system of a high-thrust aero engine. One of the most challenging tasks for a PHM is monitoring its working condition. However, considering that in the bearing rotor system of a high-thrust aero engine bearings are prone to wear failure due to unbalanced or misaligned faults of the rotor system, especially in harsh environments, such as those at high operating loads and high rotation speeds, bearing wear can easily evolve into serious faults. Compared with aero engine fault diagnosis and RUL prediction, relatively little research has been conducted on bearing condition monitoring. In addition, considering how to evaluate future performance states with limited time series data is a key problem. At the same time, the current deep neural network model has the technical challenge of poor interpretability. In order to fill the above gaps, we developed a new framework of a residual space–time feature fusion focusing module named RTCA-Net, which focuses on solving the key problem. It is difficult to accurately monitor the wear state of aero engine inter-shaft bearings under special working conditions in practical engineering. Specifically, firstly, a residual space–time structure module was innovatively designed to capture the characteristic information of the metal dust signal effectively. Secondly, a feature-focusing module was designed. By adjusting the change in the weight coefficient during training, the RTCA-Net framework can select the more useful information for monitoring the wear condition of inter-shaft bearings. Finally, the experimental dataset of metal debris was verified and compared with seven other methods, such as the RTC-Net. The results showed that the proposed RTCA-Net framework has good generalization, superiority, and credibility.

Research on Multi-Directional Spalling Evolution Analysis Method for Angular Ball Bearing

The prediction of spalling failure evolution in the lifespan of aeroengine bearings is crucial for en-suring the safe return of aircrafts after such failures occur. This study examines the spalling failure evolution process in bearings by integrating the proposed spalling region contact stress analysis model with the multi-directional subsurface crack extension analysis model. The results elucidate the general pattern of spalling expansion. Utilizing this methodology, the fatigue spalling fault evolution in bearings is thoroughly analyzed. Additionally, a two-dimensional model has been developed to simulate and analyze crack propagation in the critical direction of the spalling region, significantly enhancing the model’s computational efficiency.

Research on Temperature Rise Characteristics Prediction of Main Shaft Dual-Rotor Rolling Bearings in Aircraft Engines

Traditional aero-engine bearings rotate simultaneously with their inner and outer rings, which makes the temperature rise prediction model computationally large with low accuracy, and it cannot be accurately verified due to the means of testing. This paper presents a method for predicting the temperature rise characteristics of aero-engine bearings under composite load conditions. Firstly, the local method is used to calculate the heat generation from heat sources such as bearing spin, lubricant drag, and the differential sliding of steel ball and collar, respectively, then finite element modelling and steady-state thermal analysis are carried out for aero-engine bearings under the simultaneous action of axial and radial external loads, a double-rotor test setup is designed and the predictive model is validated, and finally, the influences of rotational speed and load on the temperature rise characteristics of the bearings are investigated. The study shows that the aero-engine bearing prediction model proposed in this paper has high accuracy; with the increase in the rotational speed of the inner ring of the bearing, the temperatures of both the inner and outer rings of the bearing increase significantly; the temperatures of the inner and outer rings of the bearing increase with the increase in the axial load, and the effect of the radial load on the temperature of the bearing is not obvious.

Prediction and analysis of paroxysmal impulse vibration in aero-engine inter-shaft bearings induced by localized faults in the outer ring

Prediction and analysis of paroxysmal impulse vibration in aero-engine inter-shaft bearings induced by localized faults in the outer ring

A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples

As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83%, 100%, and 100%, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97%. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

Fully unsupervised wear anomaly assessment of aero-bearings enhanced by multi-representation learning of deep features

The assessment of wear anomalies using vision-based techniques is crucial for automated inspections of machine health conditions. Given the diversity of worn anomalies and the time-consuming nature of labeling, recent advancements in unsupervised anomaly detection have focused on comparing extracted features with those of normal images. However, this approach faces significant challenges in constructing a comprehensive comparison sample database and extracting distinctive features from worn surfaces with varying degrees of severity. To overcome these limitations, we introduce a fully unsupervised anomaly assessment method that detects anomalies solely based on single-bearing images, without the need for unworn samples. This approach eliminates the requirement for a comparison sample database by constructing an implicit feature database from the single-bearing surfaces using pre-trained convolutional neural networks and dimensionality reduction strategies. Additionally, to address the limited differentiability of wear characteristics, we develop a feature refinement strategy that incorporates multi-representation learning and topographical distribution characteristics of worn surfaces. By integrating the statistics of the refined feature database with uncertainty distribution-based normal feature weights, we construct an anomaly distribution map. This map enables us to assess the anomaly degree of a single-bearing effectively. To validate our method, we conducted experiments using real aero-engine bearings. The results demonstrate that our proposed approach can accurately assess anomaly degrees without pre-prepared comparison samples, achieving a detection performance of 90%, which fulfills the requirements for wear anomaly assessment.

Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise

In engineering, due to strong noise interference, it is a challenging issue for decision-making of the neural network to accurately detect healthy conditions of aero-engine bearings. As such, a hierarchical health monitoring model, termed adaptive thresholding and coordinate attention-based tree-inspired network (ATCATN) is developed for the health monitoring of aero-engine bearings under strong background noise. The ATCATN integrates the advantages of the adaptive thresholding and coordinate attention-based network (ATCAN) as well as a tree structure-based method, and it is not merely a straightforward integration of two models. First, the ATCAN is constructed as the backbone network, where the coordinate attention module is developed to process one-dimensional vibration signals; meanwhile, the designed deep residual adaptive thresholding module is introduced to extract significant features from vibration signals with strong noise. Second, a two-layer tree-inspired decision layer, consisting of seed and leaf nodes, is developed to reconstruct the output layer of the ATCAN. Finally, the trained ATCATN is more consistent with maintenance cognition involving multilevel decision information by the progressive determination of fault locations and fault sizes. The experimental results and comparative analysis with seven advanced methods on two aero-engine bearing datasets show that the developed model has the following advantages: (a) model robustness under strong noise; (b) more accurate diagnosis result and (c) hierarchical diagnosis with fault locations and fault sizes. Based on the experimental results, the ATCATN model demonstrates high accuracy in progressively identifying fault locations and sizes of aero-engine bearings even under strong noise interference, which is beneficial for formulating maintenance strategies and aligns with the basic understanding of maintenance.

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network’s feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model’s domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi’an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

A novel interfacial modification strategy to improve the wear resistance of PPESK composites

High-speed and heavy-duty equipment transmission or rotation systems have experienced a significant increase in demand for wear-resistant, self-lubricating resin matrix composites. However, the interfacial interaction between the fillers and the matrix is nonetheless weak due to the fact that the composites are multi-component systems. This weak interaction negatively impacts wear resistance in practical applications. In this paper, a one-step modification method of multiple fillers based on coupling agent compounding by RSM design is proposed. The resulting PPESK composites exhibit excellent thermal (T5wt% = 517.6 °C), mechanical (Rockwell hardness = 118 HRR), and wear (ω = 0.72 × 10−15 m3/Nm) properties. In addition, PPESK composite bearings were prepared, which showed lightweight advantages, exhibited outstanding tribology performance (μ = 0.079, ω = 0.52 × 10−15 m3/Nm), and demonstrated excellent media resistance (Swelling degree (90 days, 25 ℃) = 0.45 %) in Jet fuel No.3. Therefore, these bearings have great application prospects in the field of aero-engine bearings.

Fault anomaly detection method of aero-engine rolling bearing based on distillation learning

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network. By using a small-scale student network model, the computational efficiency of the model is increased without sacrificing model accuracy. For feature-centered representation, new loss and anomaly score functions are created, and an enhanced Transformer encoder with the residual block is proposed. Then, a rolling bearing dynamics simulation method is used to obtain rich fault sample data, and the pre-training of the teacher network is completed. For anomaly detection, the training of the student network is completed based on the proposed loss function and the pre-trained teacher network, using only the vibration acceleration samples obtained from the normal state. Finally, the trained completed network and the designed anomaly score function are used to achieve the anomaly detection of rolling bearing faults. The experimental validation was carried out on two sets of test data and one set of real vibration data of a whole aero-engine, and the detection accuracy reached 100 %. The results show that the proposed method has a high capability of rolling bearing fault anomaly detection.

Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition

Implementing condition monitoring and fault diagnosis of aero-engine bearings is crucial to ensure that aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings are extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability, and (2) they do not mine fault information sufficiently or efficiently. This article proposes a Siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearing fault diagnosis under small-sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired according to the matching rules to realize the expansion of the sample size. Second, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small-sample diagnostic methods. The experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.

Microstructure and mechanical properties evolution of aviation M50 steel fabricated by the multiple electro-assisted rolling

The poor plastic forming ability of aviation M50 steel has seriously affected the high-performance manufacturing of aero-engine bearings. In this work, the multiple electro-assisted rolling (ESR) was applied by introducing the multiple electroshocking treatments into the rolling of M50 steel. The evolution of microstructure, mechanical properties, and damage of M50 steel fabricated by ESR has been investigated. The microstructure observation indicates that the dislocation density and residual stress of the ESR specimens decrease by 14.0% and 31.3%, respectively. Besides, fewer microcracks around the carbides are generated during the ESR. The mechanical property tests of ESR specimens show that the Vickers hardness is decreased by 8.7%, while the tensile elongation is increased by 33.4% without significant change in ultimate tensile strength as comparing with the CR specimens. Moreover, the TEM observation shows that subgrains are formed and higher lattice distortions are generated at grain boundaries after the ESR process. This could significantly promote the nucleation of recrystallization. After quenching and tempering, the prior austenite grain of ESR specimens is refined from 13.23 μm to 11.52 μm, while the ultimate tensile strength and Vickers hardness are increased by 12.1% and 6.1%, accompanied by an increase in elongation by 16.0%. This work develops a novel method to improve the forming limit and comprehensive properties of M50 steel by tailoring the microstructure and dislocation state, which is of great engineering significance for the manufacturing of aviation M50 bearing.

Semi-supervised hierarchical attribute representation learning via multi-layer matrix factorization for machinery fault diagnosis

Data-driven fault diagnosis methods have become a research hotspot recently. However, the following two problems are still barring them from the application: (1) Most of the existing models rely deeply on sufficient labeled samples and neglect the high cost of labeled data collection in reality; (2) The existing models usually focus on the single-level attribute of the sample and ignore the latent hierarchical fault attributes. To address these issues, a novel semi-supervised multi-layer non-negative matrix factorization (SMNMF) method is proposed in this study. The fault pattern and severity identification problems are converted into a hierarchical fault attribute representation task, which can reduce the complexity of the classification task and improve the fault diagnosis accuracy. The hierarchical attribute representations of different fault locations and sizes are learned from the time-frequency distribution (TFD) of signals by a newly constructed two-layer non-negative matrix factorization model. The graph-based semi-supervised learning method is adopted to lead the attributes of the hierarchy structure and carry out label propagation from labeled samples to unlabeled samples for more accurate fault diagnosis. The fault diagnosis experiments executed in the aeroengine bearings and a diesel engine demonstrated the feasibility and superiority of the proposed method.

Experimental study on oil supply efficiency of under-race lubrication based on radial oil scoop

Under-race lubrication is an efficient lubrication method of aeroengine bearings for achieving high DN values. In this paper, an experimental study on oil supply efficiency of under-race lubrication structure based on high rotation speed and small size radial oil scoop is carried out. The influence of rotation speed, nozzle diameter and oil temperature on oil supply efficiency are analysed. The research results can be used to guide the design of oil nozzle and under-race lubrication structure.

Fault prediction of bearings based on LSTM and statistical process analysis

Aero-engine bearing is the key component of aero-engine load-bearing transmission system, and its fault will affect the reliability and safety of aero-engine system, so it is particularly important to predict the fault of aero-engine bearings. The previous bearing researches are mainly based on the single-stage model, and the regression neural network has the problem of gradient disappearance, which leads to the uncertainty of prediction. Therefore, this paper proposes a novel model named LSS which combines the advantages of long short-term memory (LSTM) network with statistical process analysis to predict the fault of aero-engine bearings with multi-stage performance degradation. An algorithm based on the proposed model is put forward. Firstly, the time feature of bearing vibration signal is extracted and divided into multi-stage signals by statistical process analysis. Then, the multi-stage signals are input into LSS for prediction. The bearing datasets published by NASA and FEMTO-ST institute are used to prove the effectiveness of proposed method. The results show that the proposed method has higher prediction accuracy than recurrent neural network (RNN), support vector regression (SVR) and LSTM. Therefore, this method can divide the level of fault to reduce uncertainty, so as to improve prediction accuracy of the performance degradation trend and the RUL of bearings.

Equipment Operational Reliability Evaluation Method Based on RVM and PCA-Fused Features

Reliability assessment is of great significance in ensuring the safety and reducing maintenance cost of equipment. The traditional statistical method is widely used to estimate the reliability of mass equipment; however, it cannot efficiently predict the overall reliability of single or small batch equipment due to lack of failure data. This paper introduced the operational reliability concept to describe the running condition of single or small batch equipment and proposed a method based on the combination of Relevance Vector Machines (RVMs) and Principal Component Analysis (PCA) to evaluate the operational reliability. Some representative characteristic indexes of operating equipment were firstly selected, and PCA was applied to obtain a hybrid index of the equipment’s running condition. Then, a RVM prediction model was trained to predict the development of the hybrid index and corresponding probability density function (PDF). Based on this, the operational reliability of the equipment was calculated by the interval integral defined by the failure threshold and the predicted value of the hybrid index. The approach was validated using the experimental test conducted on the aero-engine rotor bearings. The results show a good agreement in the evaluations of the failure time between the proposed method and the experimental test.

Multiple-degree-of-freedom dynamic model of rolling bearing with a localized surface defect

It is well known that the dynamic response of the rolling bearing would change especially in the acceleration vibration signal when the defects occur. To accurately simulate the dynamic response of a high-speed rolling bearing with a localized surface defect, we propose a new dynamic model for the faulty rolling bearing. The proposed model considers not only the high-speed effect of the rolling element, such as centrifugal force and gyroscopic moment, but also the degrees of freedom (DOFs) of all movable components, including five-DOF inner raceway, three-DOF balls and three-DOF the cage. Then the fourth order Runge-Kutta algorithm is used to solve the nonlinear differential equations of the model for obtaining the dynamic response of the bearing. Under different radial forces, rotational speeds and defect sizes, the acceleration vibration response of inner raceway and the motion of cage considering the effect of the localized surface defect are respectively analyzed. Finally, the correctness and effectiveness of the proposed model are proved by the measured signals of the faulty aero-engine spindle bearings.