Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy

Li, Huihui; Gou, Linfeng; Zheng, Hairong

doi:10.1155/2021/8868875

Cited by 9 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spectral entropy can capture the features of the signal's spectrum, including spectral concentration, randomness, regularity, and information content [35]. Instantaneous frequency and spectral entropy have wide applications in signal processing, communication systems, image processing, biomedical engineering, and other fields [36][37][38][39]. This study utilizes non-stationary signals generated by hammer strikes, thus opting to extract the signal's instantaneous frequency and spectral entropy features.…”

Section: Training Lstm Network Using Feature Signalsmentioning

confidence: 99%

Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks

Qi,

Wang,

Yang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

As an essential component of urban infrastructure construction, Polyethylene (PE) pipelines face the challenging task of underground detection due to the complex and dynamic nature of the subsurface environment, diverse installation paths, and the inherent insulating properties of PE materials. In order to address the non-excavation detection of buried PE pipelines, this paper proposes an acoustic method based on the Long Short-Term Memory (LSTM) neural network. The study begins by analyzing the propagation and reflection mechanisms of elastic waves in the pipe-soil coupling system, and a impact excitation source is designed to generate the excitation signal. After establishing the experimental environment and collecting experimental data, a comprehensive analysis is conducted, and the LSTM neural network is employed for data classification to determine the presence of buried PE pipelines. Through neural network training, accurate identification of the PE pipeline's existence and prediction of its burial depth are achieved, providing an efficient and reliable solution for buried PE pipeline detection. The practical results demonstrate the significant application prospects of the combined acoustic method and LSTM neural network in buried PE pipeline detection. This research contributes a novel solution to the field of non-destructive PE pipeline detection, with both theoretical and practical implications.

show abstract

Section: Training Lstm Network Using Feature Signalsmentioning

confidence: 99%

Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks

Qi,

Wang,

Yang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…They used PSO to optimize the proposed data-driven framework to enhance its ability to extract representative features from the original high-dimensional signal by obtaining globally optimal parameters, such as kernel size, weight coefficients of sparse terms, and weight decay coefficients. To address the problem of the poor real-time performance of CNN in directly processing one-dimensional time series signals of aero engines for fault diagnosis, Li et al [98] combined improved pattern gradient spectral entropy (IPGSE) and CNN to propose an intelligent fault diagnosis scheme for aero engine control system sensors. In order to solve the problem of insufficient adaptation of the algorithm, the PGSE empirical selection parameters were improved by using PSO to adaptively optimize the scale factor λ so that the obtained spectral entropy map can better match the classification made by the CNN model.…”

Section: Real-time Processing Of Source Data Directly Automatic Learn...mentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

View full text Add to dashboard Cite

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

show abstract

“…First proposed by Yann LeCun, the CNN was the first algorithm to train a multilayer network successfully and is one of the most widely used deep learning models [55]. CNNs can distinguish not only the broad outlines of information, but also the subtle nuances that are invisible to humans.…”

Section: Cnn-based Fault Classificationmentioning

confidence: 99%

“…A typical convolutional neural network consists of an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer, as shown in Figure 6. More details of the typical CNN structure are provided in the literature [55].…”

Section: Cnn-based Fault Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Physics-Guided Neural Network Model for Aeroengine Control System Sensor Fault Diagnosis under Dynamic Conditions

Gou

et al. 2023

Aerospace

View full text Add to dashboard Cite

Sensor health assessments are of great importance for accurately understanding the health of an aeroengine, supporting maintenance decisions, and ensuring flight safety. This study proposes an intelligent framework based on a physically guided neural network (PGNN) and convolutional neural network (CNN) to diagnose sensor faults under dynamic conditions. The strength of the approach is that it integrates information from physics-based performance models and deep learning models. In addition, it has the structure of prediction–residual–generation-fault classification that effectively decouples the interaction between sensor faults and system state changes. First, a PGNN generates the engine’s non-linear dynamic prediction output because the PGNN has the advantage of being able to handle temporal information from the long short-term memory (LSTM) network. We use a cross-physics–data fusion scheme as the prediction strategy to explore the hidden information of the physical model output and sensor measurement data. A novel loss function that considers physical discipline is also proposed to overcome the performance limitations of traditional data-driven models because of their physically inconsistent representations. Then, the predicted values of the PGNN are compared with the sensor measurements to obtain a residual signal. Finally, a convolutional neural network (CNN) is used to classify faults for residual signals and deliver diagnostic results. Furthermore, the feasibility of the proposed framework is demonstrated on an engine sensor fault dataset. The experimental results show that the proposed method outperforms the pure data-driven approach, with the predicted RMSE being reduced from 1.6731 to 0.9897 and the diagnostic accuracy reaching 95.9048%, thereby confirming its superior performance.

show abstract

Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy

Cited by 9 publications

References 25 publications

Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks

Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Physics-Guided Neural Network Model for Aeroengine Control System Sensor Fault Diagnosis under Dynamic Conditions

Contact Info

Product

Resources

About