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Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks' inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Accurate estimation of the position of the fault source in the aircraft engine is the key to achieve engine structural health monitoring (SHM). In this paper, a convolutional neural network and graph convolutional network (CNN–GCN)-based dual-sensor acoustic emission (AE) localization method is proposed for locating the fault source in the engine casing with multi-part coupling features. Firstly, the time–frequency map data sets of AE signals at different locations are established by using continuous wavelet transform to analyze the effect of multi-part coupling features on AE signals. Secondly, combined with its reverberation mode, multi-modal and dispersion characteristics, the effectiveness of CNN–GCN model is trained, verified and tested. Finally, the sensitivity of the localization results to the sensor types is analyzed, and the sensor combination mode with high localization accuracy is obtained. These results show that the proposed method in this paper can be used as an effective means for locating the fault source of the engine casing with complex coupling interface features. [ABSTRACT FROM AUTHOR]

Thrust constitutes a pivotal performance parameter for aircraft engines. Thrust, being an indispensable parameter in control systems, has garnered significant attention, prompting numerous scholars to propose various methods and algorithms for its estimation. However, research methods for estimating the thrust of the micro-turbojet engines used in unmanned aerial vehicles are relatively scarce. Therefore, this paper proposes a thrust estimator for micro-turbojet engines based on DBO (dung beetle optimization) utilizing bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN). Furthermore, the efficacy of the proposed model is further validated through comparative analysis with others in this paper. [ABSTRACT FROM AUTHOR]

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions. [ABSTRACT FROM AUTHOR]

In order to realize the remaining useful life (RUL) prediction of mechanical equipment under different operating conditions, a domain adaption residual separable convolutional neural network (DRSCN) model is proposed in this paper. In the DRSCN model, instead of the traditional convolutional layer, a residual separable convolutional module is developed to improve the feature extraction ability of the model. Moreover, a multi-kernel maximum mean discrepancy metric function and an adversarial learning mechanism are embedded in the DRSCN model to enhance its ability to resist domain shifts, thus improving the cross-domain RUL prediction accuracy of the model. The effectiveness of the DRSCN model is verified on an aircraft engine dataset. The experimental results show that the proposed model can realize high-accuracy RUL prediction. [Display omitted] • A DRSCN model is proposed for cross-domain RUL prediction. • MMD is embedded in the DRSCN model to enhance its generalization performance. • Adversarial learning mechanism is adopted to realize domain adaption of the model. • A residual separable convolution module is proposed to replace convolutional layer. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of the aircraft engine based on historical data plays a pivotal role in formulating maintenance strategies and mitigating the risk of critical failures. None the less, attaining precise RUL predictions often encounters challenges due to the scarcity of historical condition monitoring data. This paper introduces a multiscale deep transfer learning framework via integrating domain adaptation principles. The framework encompasses three integral components: a feature extraction module, an encoding module, and an RUL prediction module. During pre-training phase, the framework leverages a multiscale convolutional neural network to extract distinctive features from data across varying scales. The ensuing parameter transfer adopts a domain adaptation strategy centered around maximum mean discrepancy. This method efficiently facilitates the acquisition of domain-invariant features from the source and target domains. The refined domain adaptation Transformer-based multiscale convolutional neural network model exhibits enhanced suitability for predicting RUL in the target domain under the condition of limited samples. Experiments on the C-MAPSS dataset have shown that the proposed method significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurately evaluating the remaining useful life (RUL) of aircraft engines is crucial for ensuring operational safety and reliability, and serves as a critical foundation for making informed maintenance decisions. In this paper, a novel prediction framework is proposed for forecasting the RUL of engines, which utilizes a dual-frequency enhanced attention network architecture built upon separable convolutional neural networks. First, the information volume criterion (IVC) index and information content threshold (CIT) equation are designed, which are applied to quantitatively quantify the degradation features of the sensor and remove redundant information. In addition, this paper introduces two trainable frequency-enhanced modules, namely the Fourier transform module (FMB-f) and the wavelet transform module (FMB-w), to incorporate physical rules information into the prediction framework, dynamically capture the global trend and local details of the degradation index, and further improve the prediction performance and robustness of the prediction model. Furthermore, the proposed efficient channel attention block generates a unique set of weights for each possible vector sample, which establishes the interdependence among different sensors, thereby augmenting the prediction stability and precision of the framework. The experimental demonstrate that the proposed RUL prediction framework can deliver accurate RUL predictions. • The IVC and CIT are used to quantify and eliminate degradation information. • A dual-frequency block(FEB-f and FEB-w) with trainable parameters is proposed. • FEB-w and FEB-f jointly trained with SDFEPN for dynamic coordination. • The integration of physical rules into the SDFEPN enhances the interpretability. [ABSTRACT FROM AUTHOR]

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach: The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM. Findings: In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained. Originality/value: In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models. [ABSTRACT FROM AUTHOR]

The automation of inspections in aircraft engines is an ever-increasing growing field of research. In particular, the inspection and quantification of coating damages in confined spaces, usually performed manually with handheld endoscopes, comprise tasks that are challenging to automate. In this study, 2D RGB video data provided by commercial instruments are further analyzed in the form of a segmentation of damage areas. For this purpose, large overview images, which are stitched from the video frames, showing the whole coating area are analyzed with convolutional neural networks (CNNs). However, these overview images need to be divided into smaller image patches to keep the CNN architecture at a functional and fixed size, which leads to a significantly reduced field of view (FOV) and therefore a loss of information and reduced network accuracy. A possible solution is a downsampling of the overview image to decrease the number of patches and increase this FOV for each patch. However, while an increased FOV with downsampling or a small FOV without resampling both exhibit a lack of information, these approaches incorporate partly different information and abstractions to be utilized complementary. Based on this hypothesis, we propose a two-stage segmentation pipeline, which processes image patches with different FOV and downsampling factors to increase the overall segmentation accuracy for large images. This includes a novel method to optimize the position of image patches, which leads to a further improvement in accuracy. After a validation of the described hypothesis, an evaluation and comparison of the proposed pipeline and methods against the single-network application is conducted in order to demonstrate the accuracy improvements. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To guarantee the safe operation of machinery and reduce its maintenance costs, estimating its remaining useful life (RUL) is a crucial task. Hence, in this study, a multi-scale memory-enhanced prediction method is proposed to describe fully characteristics of the data. This method is based on a deep learning algorithm and is designed to estimate the RUL of aircraft engines. To handle the complex and multi-fault operating conditions with uncertain properties in RUL estimation, a hybrid model that combines a multi-scale deep convolutional neural network and long short-term memory is presented. Experimental verification was carried out with the Commercial Modular Aero-Propulsion System Simulation dataset from NASA. Compared with multi-scale deep convolutional and long short-term memory networks, the hybrid model performed more efficiently. Furthermore, compared with other state-of-the-art methods, the multi-scale memory-enhanced prediction method can achieve better prognostics, especially for equipment with multiple operating conditions and failure modes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Portable interpretation system based on diffuse reflectance spectrum is developed. • Feature fusion strategy for reflectance spectra with sub-band features is explored. • The proposed FDSTNet improves accuracy and noise immunity while reducing complexity. • TransCAM-based model interpretation methods are used to pinpoint feature wavelengths. • FDSTNet is visualized for processing spectrum of temperature indicating paints. The color of temperature indicating paint (TIP) is commonly used in temperature measurement testing of aircraft engine hot end components. In this paper, an end-to-end convolutional neural network utilizing DSTNet, is proposed for accurate temperature interpretation of TIP. Furthermore, the DSTNet is optimized by fusion of reflectance spectrum and sub-band statistical features, resulting in the creation of FDSTNet. The FDSTNet achieves highly accurate temperature interpretation. When the temperature error is 0℃, the interpretation accuracy on KN3A, KN6 and KN8 samples is 98 %, 97 % and 94 %, respectively. When the temperature error is ±10℃, the accuracy on the engine blade is 88 %. In addition, a 1D convolutional neural network interpretability method based on TransCAM, is proposed. This approach analyzes the process of learning the reflectance spectrum of DSTNet, and localizes the characteristic wavelength by visualizing the class activation mapping curves of each convolutional layer of DSTNet. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction is of great significance for maintaining the reliability and safety of industrial equipment. To address the challenges faced by existing methods in simultaneously extracting local and global degradation information from monitoring data. This paper proposes a Two-Stream Convolution Augmented Transformer (TACT) model based on L 2 regularization constraint. Specifically, we design the parallel multi-scale Convolution Neural Network (CNN) and Transformer module to combine the local modeling ability of CNN and the global modeling ability of Transformer to improve the overall architecture of RUL prediction model. Moreover, the two-stream network based on the parallel structure also realizes the synchronous extraction of different time steps and sensor features in the sequence. Then, in the process of model training, the prediction reliability constraint is fused, the delay prediction constraint term is introduced, and the L 2 regularization loss function is constructed. Finally, extensive experiments on the commercial modular aero-propulsion system simulation (C-MAPSS) show that our model provides competitive performance in terms of Root-Mean-Square Error (RMSE) and Score metrics. Compared to the state-of-the-art method based on Recurrent Neural Network (RNN) or CNN and its variants, Score is reduced by at least 2.71% and RMSE by at least 3.13%. Compared to the Transformer-based improved method, the Score is decreased by at least 4.54% and the RMSE is decreased by at least 2.78%. The effectiveness of the proposed method is demonstrated. • A hybrid end-to-end RUL prediction network based on a tandem combination of CNN and Transformer is proposed. • A two-stream structure is used to extract spatial and temporal features simultaneously. • The MSE loss function incorporating L 2 regularization constraint is proposed. [ABSTRACT FROM AUTHOR]

• aircraft engine remaining useful life prediction method with the real engine flight data • multilayered deep convolutional neural network architecture and a long short-term memory network with regression output • comparison of the various deep neural network training options • analysis of the real turbofan engine operational data as an input data for the deep neural network, in order to train, validate and test machine learning algorithms in predicting engine remaining useful life • case studies for method verification based on the real-life engine operational data not the simulated one Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one. [ABSTRACT FROM AUTHOR]

A number of deep learning models have been proposed to capture the inherent information in multivariate time series signals. However, most of the existing models are suboptimal, especially for long-sequence time series prediction tasks. This work presents a causal augmented convolution network (CaConvNet) and its application for long-sequence time series prediction. First, the model utilizes dilated convolution with enlarged receptive fields to enhance global feature extraction in time series. Secondly, to effectively capture the long-term dependency and to further extract multiscale features that represent different operating conditions, the model is augmented with a long-short term memory network. Thirdly, the CaConvNet is further optimized with a dynamic hyperparameter search algorithm to reduce uncertainties and the cost of manual hyperparameter selection. Finally, the model is extensively evaluated on a predictive maintenance task using the turbofan aircraft engine run-to-failure prognostic benchmark dataset (C-MAPSS). The performance of the proposed CaConvNet is also compared with four conventional deep learning models and seven different state-of-the-art predictive models. The evaluation metrics show that the proposed CaConvNet outperforms other models in most of the prognostic tasks. Moreover, a comprehensive ablation study is performed to provide insights into the contribution of each sub-structure of the CaConvNet model to the observed performance. The results of the ablation study as well as the performance improvement of CaConvNet are discussed in this paper. • Proposed an innovative causal augmented ConvNet for long sequence prediction. • Integrate both long-term and short-term dependencies within each sensor via LSTM network. • Model optimized with a robust hyperparameter selection to fast-track real-world application. • Improved RUL prediction obtained at all monitoring point compared with benchmark models. [ABSTRACT FROM AUTHOR]

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for multivariate time series data analysis with special emphasis on industrial sensor data sets. The approach applies deep convolutional neural networks as a base architecture, incorporating a generalization of the dilated convolution operation on the receptive fields. The dilation operation allows for the aggregation of distributed information in the input space compared to standard convolution operation. The proposed dilation methodology allows for a trainable selection and ignorance of individual sensor features, based on their relevance to the prediction task. Furthermore, arbitrary patterns in the input feature space, including in the temporal dimension of the multivariate time series data can be extracted. In contrast to the standard dilation methodology, the proposed generalized dilation technique is end-to-end differentiable and hence can be trained with off the shelf gradient descent optimizers. Two methodologies have been proposed for the resulting constrained optimization problem namely, the Barrier Function and Top-K sampling approach. We apply the dilated convolutional neural networks to remaining useful lifetime (RUL) estimation problems where degradation recognition over a longer time horizon is crucial for precise estimation. We test the approach on two challenging benchmark datasets, namely the PRONOSTIA Bearing Dataset and the C-MAPSS Aircraft Engine Dataset for RUL prediction. The experimental results obtained for RUL estimation show the superior prediction capability of the proposed generalized dilation methodologies and constitute a new state of the art compared to previous results in literature. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remaining useful life (RUL) prediction of aircraft engines is significant in the health monitoring, operation, and maintenance of aircraft. Capturing more comprehensive device degradation trends at different time scales and extracting long-term dependencies effectively among elements in long time series are two challenges in the field of aircraft engine RUL estimation. To address the aforementioned challenges, this paper proposes a novel multiscale Hourglass-Transformer (MHT) aircraft engine RUL prognostics. Specifically, an hourglass-shaped multiscale feature extractor (HME) is designed based on one-dimensional convolutional neural network, which can scale the time sequence into multi-time scales for feature fusion. Then, a transformer network is employed to further extract features from the fused feature map and output the RUL. To enhance inter-scale data attention, a pyramid self-attention mechanism is employed in both the encoder and decoder. Finally, the superiority and effectiveness of this approach are verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Furthermore, the robustness and generalization capability of this method are further validated on New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset. • A novel multiscale Hourglass-Transformer is developed for aircraft engine RUL prognostics. • An hourglass-shaped multiscale feature extractor is designed based on one-dimensional convolutional neural network. • A transformer network is employed to further extract features. • Pyramid self-attention mechanism is adopted to reduce space complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch. [ABSTRACT FROM AUTHOR]

Affected by the transmission path, it is very difficult to diagnose the vibration signal of the rolling bearing on the aircraft engine casing. A fault diagnosis method based on convolutional neural network is proposed for the weak vibration signal of the casing under the excitation of rolling bearing fault. Firstly, the processing method of vibration signal is studied. Through comparison and analysis, it is found that the fault characteristics of rolling bearing are more easily expressed by continuous wavelet scale spectrum, and a better recognition rate is obtained. Finally, the experiment was carried out with an aero-engine rotor tester with a casing, and the method based on wavelet scale spectrum and convolutional neural network was used for diagnosis. The results were compared with the support vector machine method. The results show that the method has a high recognition rate for the weak fault signals of different fault types collected on the aero engine case, and its fault recognition rate reaches 95.82 %, which verifies the superiority and potential of the method for rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications. [ABSTRACT FROM AUTHOR]

A typical aircraft engine consists of fans, compressors, turbines, and so on, and each is made of multiple layers of blades. Discovering the site of damages among the large number of blades during aircraft engine maintenance is quite important. However, it is impossible to look directly into the engine unless it is disassembled. For this reason, optical equipment such as a videoscope is used to visually inspect the blades of an engine through inspection holes. The videoscope inspection method has some obvious drawbacks such as the long-time attention on microscopic video feed and high labor intensity. In this research, we developed a damage recognition algorithm using convolutional neural networks and some image-processing techniques related to feature point extraction and matching in order to improve the videoscope inspection method. The image-processing techniques were mainly used for the preprocessing of the videoscope images, from which a suspected damaged region is selected after the preprocessing. The suspected region is finally classified as damaged or normal by the pre-trained convolutional neural networks. We trained the convolutional neural networks 2000 times by using data from 380 images and calculated the classification accuracy using data from 40 images. After repeating the above procedure 50 times with the data randomly divided into training and test groups, an average classification accuracy of 95.2% for each image and a damage detectability of 100% in video were obtained. For verification of the proposed approach, the convolutional neural network part was compared with the traditional neural network, and the preprocessing was compared with the region proposal network of the faster region–based convolutional neural networks. In addition, we developed a platform based on the developed damage recognition algorithm and conducted field tests with a videoscope for a real engine. The damage detection AI platform was successfully applied to the inspection video probed in an in-service engine. [ABSTRACT FROM AUTHOR]

Sensors of an aircraft engine have the problems of multiple kinds of faults and difficulty to diagnose. Moreover, the diagnosis performance cannot meet practical demands. Therefore, this paper proposes a novel approach for sensor fault diagnosis integrating the advantages of selective ensemble learning, siamese convolutional neural network (SCNN), and Bayesian interval estimation (BIE). The Bayesian interval estimation with hypothesis testing is developed with real-time data update to reduce the confidence interval. The attention mechanism is introduced to enhance fault diagnosis performance. Moreover, ensemble selection learning with Akaike information criteria is proposed to obtain optimal SCNNs. The theorems of convergence and optimal confidence data in ensemble learning are proven. Furthermore, this paper develops a novel improved learning algorithm based on AdaMod (Adaptive and momental bound method) and SGD (stochastic gradient descent), and achieves a new fault diagnosis algorithm to improve the diagnosis performance. The sensor data of aircraft engines from China Eastern Airlines is adopted for validation and experimental results show that proposed approach reaches an excellent tradeoff between sensor diagnosis performance, confidence interval, and runtime. The ablation studies are performed to analyze the role of leaner's components. The proposed method improves training accuracy by 6.2%, fault diagnosis performance by 5.1%, fault prediction mean result by 4.6%, and fault prediction variance is reduced by 38.1% than the state-of-the-art methods. • A novel diagnosis approach combining selective ensemble SCNN and Bayesian interval estimation (BIE) is proposed. • The Bayesian interval estimation method with hypothesis testing is developed to support better fault diagnosis with data update. • The theorems of convergence and optimal confidence are proved. • The improved learning algorithm based on AdaMod and SGD is developed. • A novel algorithm is adopted for diagnosing sensor faults of aircraft engine to improve the diagnosis performance. [ABSTRACT FROM AUTHOR]

Removing noise from health signals is critical in gas path diagnostics of aircraft engines. An efficient noise filtering/denoising method should remove noise without using future data points, preserve important changes, and promote accurate diagnostics without time delay. Machine learning (ML)-based methods are promising for high fidelity, accuracy, and computational efficiency under the motivation of Intelligent Engines. However, previous ML-based denoising methods are rarely applied in actual engineering practice because they cannot accommodate time series and cannot effectively capture important changes or are limited by the time delay problem. This paper proposes a convolutional neural network denoising auto-encoder (CNN-DAE) method to build a denoising auto-encoder structure. In this structure, a convolutional operation is used to accommodate time series, and causal convolution is introduced to solve the problem of using future data points. The proposed denoising method is evaluated against NASA's propulsion diagnostic method evaluation strategy (prodimes) software. It has been proved that the proposed method can accommodate time series, remove noise for improved denoising accuracy, and preserve the important changes for enhanced diagnostic information. NASA's blind test case results show that kappa coefficient of a common diagnostic method using the processed data is 0.731 and is at least 0.046 higher than the other diagnostic methods in the open literature. Processing health signals using the proposed method would significantly promote accurate diagnostics without time delay. The proposed method could support intelligent condition monitoring systems by exploiting historical information for improved denoising and diagnostic performance. [ABSTRACT FROM AUTHOR]

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability. • Predictive maintenance integrating probabilistic data-driven RUL prognostics. • Reliable probabilistic RUL prognostics using CNNs with Monte Carlo dropout. • Stochastic reward processes for maintenance planning. • Single and Multi-component predictive maintenance planning. • Maintenance planning using RUL prognostics for aircraft turbofan engines. [ABSTRACT FROM AUTHOR]

Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial role in prognostics health management technology. Conventional convolutional neural network and long-short-term memory network have notable limitations in the size of convolution in processing temporal data and the associations between non-adjacent data when predicting the RUL, respectively. Although the proposal of the Transformer provides an opportunity to solve the shortcomings mentioned above, Transformer still has some limitations. Precisely, the Transformer model awaits in-depth research focusing on vital local regions and decreasing computational complexity. In this sense, this paper proposes a novel integrated multi-head dual sparse self-attention network (IMDSSN) based on a modified Transformer to predict the RUL. From two sparse perspectives, the proposed IMDSSN includes a multi-head ProbSparse self-attention network (MPSN) and a multi-head LogSparse self-attention network (MLSN). Specifically, MPSN is designed to filter out the primary function of the dot product operation, thereby improving computational efficiency. Furthermore, considering the data inside the whole time window, a comprehensive logarithmic-based sparse strategy in MLSN is proposed to reduce the amount of computation. An aircraft turbofan engine dataset is used to verify the proposed IMDSSN, which demonstrates that the IMDSSN is better than some conventional approaches. • An MPSN is designed to filter out the dot product operation with the main role. • A comprehensive logarithmic sparse strategy in MLSN is proposed for RUL prediction. • The MPSN and MLSN are integrated to obtain great RUL prediction performance. • Research results show the IMDSSN-based RUL prediction approach is excellent. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction for aircraft engines is crucial to enabling predictive maintenance. Current RUL predictions for aircraft engines mainly focus on model-based and data-driven methods that employ a single model or algorithm. Few studies on RUL prediction have been conducted by using an ensemble method that combines prediction results from multiple algorithms. As an emerging frontier technology, ensemble learning has become a topic of interest in the field of RUL prediction because it can achieve better prediction performance than single model. In this study, a health-state-related (HSR) ensemble deep learning method that considers different degradation laws of the aircraft engine is proposed for RUL prediction. First, a health baseline is constructed and lifetime degradation is divided into several health states to represent different degradation laws. The Mahalanobis distance to the health baseline is utilized to recognize the current health state of the aircraft engine. Second, three deep learning methods, namely stacked autoencoder, convolutional neural network and long short-term memory, are selected as member algorithms and trained on different health states. Thus, different member algorithm sets are constructed for different health states, learning different degradation laws in different health states. Third, self-adaptive ensemble weight sets for different health states are calculated by applying ridge regression, which can comprehensively utilize the prediction results of each algorithm model in different health states. A case study is conducted by using a dataset of the PHM data challenge to demonstrate the effectiveness of the proposed method. The experiment result shows that the proposed HSR ensemble deep learning method can considerably improve prediction performance compared with methods that are based on a single prediction algorithm and ensemble learning method that does not consider the health state. • A health state-related ensemble deep learning framework is proposed for aircraft engine RUL prediction. • Health states that represents different degradation law is classified with Mahalanobis space-based health baseline. • Diversified member algorithms sets are constructed to learn multiple degradation laws. • Self-adaptive ensemble weights sets are calculated to make use of advantages of member algorithms to the fullest. • The proposed method can significantly improve prediction performance and universality comparing with traditional ensemble method. [ABSTRACT FROM AUTHOR]

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles. • Predictive maintenance of aircraft engines integrating imperfect RUL prognostics. • Obtaining RUL prognostics for turbofan engines using Convolutional Neural Networks. • Remaining Useful Life prognostics with C-MAPSS degradation data of turbofan engines. • Based on RUL prognostics, proposing an alarm policy to trigger maintenance tasks. • Analysis of costs for predictive maintenance with imperfect RUL prognostics. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) estimation has been intensively studied, given its important role in prognostics and health management (PHM) of industry. Recently, data-driven structures such as convolutional neural networks (CNNs), have achieved outstanding RUL prediction performance. However, conventional CNNs do not include an adequate mechanism for adaptively weighing input features. In this paper, we propose a double attention-based data-driven framework for aircraft engine RUL prognostics. Specifically, a channel attention-based CNN was utilized to apply greater weights to more significant features. Next, a Transformer was used to focus attention on these features at critical time steps. We validated the effectiveness of the proposed framework on benchmark datasets for aircraft engine RUL estimation. The experimental results indicate that the proposed double attention-based architecture outperformed the existing state-of-the-art (SOTA) algorithms. The double attention-based RUL prediction method can detect the risk of equipment failure and reduce loss. • A double attention-based method for RUL prediction of aircraft engines is developed. • The proposed framework makes use of channel attention and temporal attention. • The end-to-end model learns from raw data and outputs RUL prognostics results. • The presented method improves RUL prediction performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Pipe structures are at the base of the entire industry. In the industry structure, heat and vibration are transmitted in each pipe. The minimum distance between each pipe is significant to the security. The assembly error and the deformation of the pipeline positions after multiple runs are significant problems. The reconstruction of the multi-pipe system is a critical technical difficulty in the complex tube system. In this paper, a new method for the multi-pipes structure inspection is presented. Images of the tube system are acquired from several positions. The photogrammetry technology calculates positions, and the necessary coordination of the structure is reconstructed. A convolution neural network is utilized to detect edges of tube-features. The new algorithm for tube identification and reconstruction is presented to extract the tube feature in the image and reconstruct the 3D parameters of all tubes in a multi-pipes structure. The accuracy of the algorithm is verified by simulation experiments. An actual engine of the aircraft is measured to verify the proposed method. [ABSTRACT FROM AUTHOR]