scholarly journals Application of novelty detection methods to health monitoring and typical fault diagnosis of a turbopump

2012 ◽  
Vol 364 ◽  
pp. 012128 ◽  
Author(s):  
Lei Hu ◽  
Niaoqing Hu ◽  
Bin Fan ◽  
Fengshou Gu
Keyword(s):  
Fault Diagnosis ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Detection Methods

scholarly journals Novelty detection methods for online health monitoring and post data analysis of turbopumps

2013 ◽  
Vol 27 (7) ◽  
pp. 1933-1942 ◽  
Author(s):  
Lei Hu ◽  
Niaoqing Hu ◽  
Xinpeng Zhang ◽  
Fengshou Gu ◽  
Ming Gao
Keyword(s):  
Data Analysis ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Detection Methods

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Research on WSNs Topology and Application of Lightning Monitoring in Distribution Lines

2012 ◽  
Vol 433-440 ◽  
pp. 5573-5578
Author(s):  
Tie Liu Wang ◽  
Si Lei Shen ◽  
Jun Jie Wang
Keyword(s):  
Wireless Sensor Network ◽  
Fault Diagnosis ◽  
Sensor Network ◽  
Health Monitoring ◽  
Routing Protocol ◽  
Power Efficiency ◽  
Wireless Sensor ◽  
Environmental Sampling ◽  
Distribution Lines ◽  
Distribution Line

Wireless Sensor Network (WSN) is used for such tasks as surveillance, widespread environmental sampling, security, and health monitoring widely. In this paper, a WSNs topology is proposed for lightning monitoring of distribution lines, which decides the number of nodes, routing protocol and power efficiency. The WSNs is deployed along the distribution line with nodes mounted on tall towers, which is used to monitor the lightning activities and accomplish fault diagnosis. At last, a monitoring system based on WSN is fabricated.

Fast Fuel Variation and Identification of SOFC System Changes Using Online Health Monitoring Tools and Fault Diagnosis

2019 ◽  
Vol 91 (1) ◽  
pp. 753-759
Author(s):  
Vanja Subotic ◽  
Philipp Harter ◽  
Bernhard Stoeckl ◽  
Michael Preininger ◽  
Michails Kusnezoff ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Health Monitoring ◽  
Monitoring Tools ◽  
System Changes

Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I

2020 ◽  
Vol 20 (12) ◽  
pp. 2050138
Author(s):  
Wilson D. Sanchez ◽  
Jose V. de Brito ◽  
Suzana M. Avila
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Phase I ◽  
Damage Detection ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Detection Methods ◽  
State Of Health ◽  
Civil Structures ◽  
Synchrosqueezed Wavelet Transform

Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads. When a structure ages, it is necessary to know its state of health and make a decision of maintenance or replacement. When a structure such as a bridge or building is subjected to destructive environmental forces, determining its state of health becomes a priority since its recovery is urgently required to function normally. Structural Health Monitoring (SHM) is a technology that aims to prevent the collapse of structures and loss of human life through early diagnosis of the health status of a structure. There are a large number of damage detection methods that can be classified into (1) non-destructive testing methods, (2) dynamic characteristics-based damage detection methods, (3) dynamic response-based, (4) multi-scale damage detection method and (5) damage detection methods with consideration of uncertainties. In this work, it is implemented synchrosqueezed wavelet transform (SWT), which can be classified as a methods based on the dynamic response. To validate the robustness of the method it is identified first, the natural frequencies of the Benchmark Phase I without damage, which consists of a steel structure of 4-story [Formula: see text] bay 3D steel frame structure subjected to ambient vibrations. Subsequently, some damage patterns are validated according to IASC-ASCE SHM Task Group. The results obtained in the identification of natural frequencies are compared with those reported in literature. SWT was efficient, presenting a minimum error of 0.12[Formula: see text] and a maximum of 3.06[Formula: see text] in the identification of natural frequencies about the AISCE-ASCE group model. SWT overcomes some other damage detection methods, which are deficient in the identification of closely spaced frequencies, commonly present in many civil structures due to symmetric geometry or similar physical properties in different directions.

A data-driven method of health monitoring for spacecraft

2018 ◽  
Vol 90 (2) ◽  
pp. 435-451 ◽  
Author(s):  
Xu Kang ◽  
Dechang Pi
Keyword(s):  
Health Monitoring ◽  
Attitude Control ◽  
Principal Component ◽  
Data Driven ◽  
Detection Methods ◽  
Content Type ◽  
Orbit Control ◽  
Mode Decomposition ◽  
Squared Prediction Error ◽  
Pca Method

Purpose The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft. Design/methodology/approach This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft. Findings Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods. Practical implications The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites. Originality/value The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

Sign in / Sign up

Export Citation Format

Share Document