Aeroengine intelligent gas path simulation and diagnosis based on feature fusion and meta learning

Aimed at the characteristics of huge systems, complicated distributed structures and difficult fault diagnosis of large equipments, an extensible system of distributed online monitoring and multi-level intelligent fault diagnosis based on hybrid wireless network techniques was presented and designed. The entire system includes four hierarchy's subsystems. The first subsystem is monitoring sensors and Zigbee network. It is responsible to gain various data of equipments in a non-intrusive mode. The second one is monitoring and transmission network. It consists of embedded system ARM920T and relative multiple single-chips, and is responsible to receive and integrate all information of the previous subsystem. The third one is online and offline monitoring subsystem based on wireless protocol 802.11b. The final one is multi-level intelligent diagnosis and management system. The results of practical operation show that, because of the using of hybrid wireless network, embedded system and multilevel intelligent diagnosis, the system may be flexibly deployed, have powerful capability of monitoring, diagnosis and management, and can implement expeditious and accurate fault positioning and diagnosis for large equipment system.

In real industrial environments, it is difficult to obtain a large amount of monitoring data of the equipment to be diagnosed under specific working conditions. At the same time, the distribution of training and test data often differs greatly due to the variation of working conditions and operation modes. Directly using small sample data for training will lead to the degradation of model accuracy, while using different working conditions data for joint training may lead to the degradation of diagnostic accuracy due to domain differences. Therefore, how to ensure the accuracy of cross-domain fault diagnosis under small sample conditions becomes a challenge that must be faced. In this paper, an end-to-end deep transfer learning method based on convolutional neural networks (CNN) is proposed to solve the cross-domain diagnosis problem. Specifically, the method uses CNN to extract the source domain data features and build a pre-trained model; subsequently, the model is fine-tuned using a small amount of data from the target domain according to the transfer learning strategy to obtain the final intelligent diagnostic model. In addition, experiments using a publicly available dataset demonstrate the usability and effectiveness of the proposed method in this study.

In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

To improve diagnosis accuracy for gear fault diagnosis under imbalanced and small sample conditions, a method combining the Un-threshold Recurrence Plots - Conditional Variational Autoencoder-Mean Generative Adversarial Network (URP-CVAE-MGAN) combined with Dropkey-Vision Transformer (DViT) is proposed. First, gear vibrational signals are transformed into Recurrence Plots (RP) images to extract more fault features without threshold effect. Then, a conditional variable and mean feature difference function are incorporated into VAE-GAN to improve the quality and diversity of generated samples, balancing the imbalanced and small sample sets. Dropkey is applied to the diagnosis model Vision Transformer to capture more fault information, improving diagnosis accuracy across various fault types and severities for gear. Finally, the proposed method is verified based on two datasets, demonstrating a significant accuracy improvement of up to 7.84% under the imbalanced and small samples, and confirming its feasibility and superiority.

PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

Optimization configuration of gas path sensors using a hybrid method based on tabu search artificial bee colony and improved genetic algorithm in turbofan engine

ABSTRACTIn order to maintain the safe operation of various types of equipment, the health status of main components should be monitored in real-time, and the demand for intelligent fault diagnosis algorithm has increased sharply. However, the traditional intelligent diagnosis algorithm is based on the manual method for signal feature extraction, which has high requirements for expert experience and poor generality. A convolutional neural network, with big data as its engine, is the most effective pattern classification algorithm at present. In this paper, the convolutional neural network is applied to time-domain vibration signal fault diagnosis, taking the bearing as an example, and an intelligent diagnosis method of bearing based on convolution neural network is proposed. The proposed method does not need manual feature extraction, and can automatically complete feature extraction and automatic fault recognition. The convolutional neural network has three convolutional layers. We use data enhancement techniques for the input raw signal and convert the one-dimensional original time-domain vibration signal into a two-dimensional signal. The model shows good results on CWRU dataset and the recognition accuracy of the algorithm in the CWRU bearing database is more than 96%.

Intelligent fault diagnosis of rolling bearings is crucial for ensuring the safe operation of rotating machinery. To address the issues of excessive data dependency, insufficient interpretability, and scarcity of fault samples in current intelligent diagnosis methods, this paper proposes a few-shot intelligent bearing fault diagnosis method that integrates dynamics simulation and a data-driven approach. First, a nonlinear bearing-rotor system dynamics model is established, and fault impact responses are quantified based on Hertzian contact theory. This allows for the expansion of training samples with simulated data, effectively mitigating the problem of insufficient experimental data. Next, the continuous wavelet transform (CWT) is used to convert vibration signals into time-frequency images, and a ResNet-BiLSTM network is constructed to extract spatiotemporal fusion features, further enhancing the model’s feature extraction capability. Finally, a SVM classifier is employed to achieve high-accuracy fault recognition under limited sample conditions. Experimental results show that in a scenario with a training set (98 simulated samples +2 real samples per class) and a test set (100 real samples per class), the proposed method achieves an average recognition accuracy of 99.00%. This study provides an intelligent diagnosis solution for data-scarce scenarios that combines both physical interpretability and high accuracy.

This paper presents an intelligent combustion fault diagnosis approach for V-type marine diesel engines with uneven firing intervals based on the instantaneous crankshaft speed (ICS). First, experimental ICSs of a 16-cylinder marine diesel engine under normal and different degrees of fault conditions are investigated to obtain the most sensitive fault features. A new polar representation method named cyclic-polar-map (CPM) is proposed to effectively demonstrate the fault features. The quantitative radii of the CPM are used to indicate the location and degree of fault. Then, a support vector machine (SVM)-based intelligent fault diagnosis system is developed for automatically detecting the combustion faults with the features extracted from the proposed CPM and the frequency analysis of ICS. In addition, for the SVM training set is not always available in practice, a dynamic model of the crankshaft system is developed to simulate different fault patterns. Finally, the efficiency of the proposed intelligent diagnosis approach is validated by experimental data. The results show that the proposed SVM-based intelligent diagnosis system using both CPM features and frequency domain features of the ICS can obtain good accuracies under different fault degrees at various speed and load conditions.

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

The complexity and diversity of equipments presented in the development of modern industrial technology brings the wide application of deep learning in fault diagnosis. However, the multi-source heterogeneous data collected from different sensors applied in industrial production cannot be used by traditional neural networks directly, which registers a difficulty to fault diagnosis based on deep learning. In addition, with only one of the data used, traditional deep learning fault diagnosis methods ignores the correlation between heterogeneous data, which may result in the loss of useful information and the accuracy of fault diagnosis. To solve the above problems, a fault diagnosis framework based on deep learning for multi-source heterogeneous data fusion is proposed. In the feature learning stage, stack self-encoder (SAE) and convolution neural network (CNN) are used to extract deep features from one-dimensional vibration data and two-dimensional image data respectively, and then feature fusion fault diagnosis is carried out. It solves the problem that traditional neural network can't make full use of multi-source heterogeneous data, and improves the accuracy of fault diagnosis. What's more, the bearing data of Case Western Reserve University are used to verify the validity of the diagnosis method proposed in this paper.

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

This paper proposes a new method for fault diagnosis of new energy transmission system based on combined BiLSTM-1DCNN model. The EV dataset is preprocessed and converted into a format acceptable to the model. The BiLSTM neural network is used to extract temporal features, and the 1DCNN network is used to capture the spatial features of the data. The combination of the two networks can effectively improve the accuracy and efficiency of fault diagnosis. Comparing the proposed method with traditional machine learning methods, it is found that the proposed method has higher accuracy and faster convergence speed. Sensitivity analysis is used to study the impact of different hyperparameters on model performance. The results show that the performance of the model is affected by the number of layers, learning rate and window size in the 1DCNN network. In addition, the experiments and datasets of this study are from ADVISOR of the US Department of Energy (DOE). The proposed combined BiLSTM-1DCNN model provides a promising method for fault diagnosis of new energy drive systems, which can improve the efficiency and accuracy of fault diagnosis.

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too

Train operation safety is the most important and the most basic requirement. Locomotive traction motor is the train operation of traction power equipment, whose reliability relates directly to the train operation safety. And locomotive traction motor fault diagnosis is to ensure the reliability of the traction motor scooter important technique means. Through the locomotive pulling motor failure diagnosis method's research, the traction motor typical fault type has been summarized, the main intelligent diagnosis method principle has been narrated, the main principles of the intelligent diagnosis, diagnostic procedures, and their advantages and disadvantages are described in detail, the existing problems in the field and future trends are pointed out finally.