scholarly journals Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4750 ◽  
Author(s):  
Mengmeng Wang ◽  
Quanbo Ge ◽  
Haoyu Jiang ◽  
Gang Yao
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Learning ◽  
Aircraft Engine ◽  
Development Trend ◽  
Learning System ◽  
Harsh Environment ◽  
Learning Models ◽  
Engine Wear ◽  
Intelligent Technology ◽  
The Development Trend

An aircraft engine (aeroengine) operates in an extremely harsh environment, causing the working state of the engine to constantly change. As a result, the engine is prone to various kinds of wear faults. This paper proposes a new intelligent method for the diagnosis of aeroengine wear faults based on oil analysis, in which broad learning system (BLS) and ensemble learning models are introduced and integrated into the bagging-BLS model, in which 100 sub-BLS models are established, which are further optimized by ensemble learning. Experiments are conducted to verify the proposed method, based on the analysis of oil data, in which the random forest and single BLS algorithms are used for comparison. The results show that the output accuracy of the proposed method is stable (at 0.988), showing that the bagging-BLS model can improve the accuracy and reliability of engine wear fault diagnosis, reflecting the development trend of fault diagnosis in implementing intelligent technology.

Application Overview of Neural Network in Fault Diagnosis

2014 ◽  
Vol 697 ◽  
pp. 419-424
Author(s):  
Ze Fan Cai ◽  
Dao Ping Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Development Trend ◽  
System Structure ◽  
Bp Network ◽  
Rbf Network ◽  
The Third ◽  
The Neural Network ◽  
The Development Trend

This paper introduces the system structure of neural network in fault diagnosis, and summarizes some applications of neural network in fault diagnosis. The most commonly used neural network in fault diagnosis is BP network. The second is RBF network and the third is ART. For each neural network, the paper will discuss the neural network, and the introduce some applications. It also introduces the combination of neural networks and other techniques. In the last part, this paper points out the development trend of the neural network in fault diagnosis.

Design of Network Learning Platform Based on Cloud-Computation

2012 ◽  
Vol 488-489 ◽  
pp. 1315-1318 ◽  
Author(s):  
Yu Jiao Wang ◽  
Hai Yun Lin ◽  
Hui Rong
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Development Trend ◽  
Learning System ◽  
Network Learning ◽  
Learning Platform ◽  
Network Concept ◽  
Application Mode ◽  
Cloud Computation ◽  
The Development Trend

Cloud computing is a brand new network concept after grid computing, which is the development trend of the next generation of internet. Cloud computing, as a network application mode of low costs and high performance, is gradually influencing people’s learning, work and life. Through the analysis of cloud computing and its key technology combined with the features of network learning, a network learning system model based on cloud computing is constructed, which could realize flexible expansion according to users’ requirements.

Recent Progress of Vibration Analysis on Fault Diagnosis for Rolling Bearing

2011 ◽  
Vol 84-85 ◽  
pp. 544-547
Author(s):  
En Gao Peng ◽  
Zheng Lin Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Analysis ◽  
Recent Progress ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Development Trend ◽  
Existing Problems ◽  
Detection Approach ◽  
Diagnosis Method ◽  
The Development Trend

Rolling bearing is extensively used in various areas including shipbuilding, aircraft, mining, manufacturing, agriculture, etc. The breakdowns of the bearings may do unexpected hazardous to the machinery. Therefore, it is crucial for engineers and researchers to monitor the bearing conditions in time in order to prevent the malfunctions of the plants. Hence the vibration analysis accompanying other condition monitoring methodologies has been successfully used in the field of roller bearings fault diagnosis as well as other key components. The fault diagnosis method of roller bearing using vibration analysis was introduced in this paper. And the development trend of vibration analysis was presented. Meanwhile the main existing problems in fault diagnosis of roller bearing were discussed. It concludes that the integration of vibration analysis and other detection approach can provide effective and reliable fault diagnosis results.

Signal Diagnosis of Aircraft Engine Rotor Vibration Fault

2014 ◽  
Vol 696 ◽  
pp. 99-104 ◽  
Author(s):  
Cui Zhang ◽  
Ke Ming Wang ◽  
Peng Ran Zhao
Keyword(s):  
Evidence Theory ◽  
Aircraft Engine ◽  
Development Trend ◽  
Small Sample ◽  
Bayesian Probability ◽  
Support Vector ◽  
Engine Vibration ◽  
Vibration Mechanism ◽  
Weaker Conditions ◽  
The Development Trend

With the development of modern aviation technology, the structure of aircraft engine as the heart of the plane is more and more complicated. The problem of people’s concern is how to monitor engine condition and realize the fast fault diagnosis of engine. In this paper, the method of support vector machine (SVM) combing evidence theory decides whether a signal is a fault signal on the base of researching the engine vibration mechanism and the characteristics of rotor fault vibration signals. This method avoids the defects giving the single information though the traditional method which is unable to predict the tendency of the engine safety. Evidence theory meets weaker conditions than Bayesian probability theory. It can express "uncertain" and "unknown" directly. Therefore the paper makes information fusion in combination with evidence theory in different measuring points and different working conditions of engine. This method not only can identify small sample and nonlinear system, but also fuse information which gets more evidence samples effectively. At the same time, the posterior probability diagnosis results can predict the development trend of the fault accurately. Output in the form of probability can deepen the cognition about the present situation of the engine and better observe the safety of the trend of engine simultaneously. It can facilitate the management and protection in time.

scholarly journals Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques

2020 ◽  
Vol 30 (1) ◽  
pp. 258-272
Author(s):  
P B Mallikarjuna ◽  
M Sreenatha ◽  
S Manjunath ◽  
Niranjan C Kundur
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Short Term Memory ◽  
Aircraft Engine ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Gearbox Vibration ◽  
Long Short Term Memory

Abstract Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.

Reviews on Automobile Abnormal Sound Fault Diagnosis

2014 ◽  
Vol 989-994 ◽  
pp. 3319-3322
Author(s):  
Gui Ling Tan ◽  
De Hua Miao ◽  
Yu Ming Qi ◽  
San Peng Deng
Keyword(s):  
Fault Diagnosis ◽  
Traffic Accidents ◽  
Development Trend ◽  
Human Safety ◽  
Vehicle System ◽  
Abnormal Sound ◽  
System Components ◽  
Safety And Reliability ◽  
The Development Trend ◽  
Automotive System

Automotive abnormal sound is an important manifestation of the automotive system components early malfunction. It can effectively improve safety and reliability of the vehicle system by characteristics extraction of abnormal vehicle sound and prediction of the development trend of fault. What’s more, it can solve the problem that ECU can’t identify tire failure and other problems. As a result, it will reduce the number of traffic accidents, protect human safety and property.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

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