Growing Structure Multiple Model Systems for Anomaly Detection and Fault Diagnosis

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Kenneth Marko ◽  
Jun Ni
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
System Development ◽  
Simulation Software ◽  
Model Systems ◽  
Multiple Model ◽  
Detection Scheme ◽  
Engine Simulation ◽  
Local Decision ◽  
Residual Errors

A new anomaly detection scheme based on growing structure multiple model system (GSMMS) is proposed in this paper to detect and quantify the effects of anomalies. The GSMMS algorithm combines the advantages of growing self-organizing networks with efficient local model parameter estimation into an integrated framework for modeling and identification of general nonlinear dynamic systems. The identified model then serves as a foundation for building an effective anomaly detection and fault diagnosis system. By utilizing the information about system operation region provided by the GSMMS, the residual errors can be analyzed locally within each operation region. This local decision making scheme can accommodate for unequally distributed residual errors across different operational regions. The performance of the newly proposed method is evaluated through anomaly detection and quantification in an electronically controlled throttle system, which is simulated using a high-fidelity engine simulation software package provided by a major automotive manufacturer for control system development.

A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays

2021 ◽  
Vol 234 ◽  
pp. 113950
Author(s):  
Chenxi Li ◽  
Yongheng Yang ◽  
Kanjian Zhang ◽  
Chenglong Zhu ◽  
Haikun Wei
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Diagnosis Technique

LogNADS: Network anomaly detection scheme based on semantic representation

2021 ◽  
Author(s):  
Xu Liu ◽  
Weiyou Liu ◽  
Xiaoqiang Di ◽  
Jinqing Li ◽  
Binbin Cai ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Semantic Representation ◽  
Detection Scheme ◽  
Network Anomaly Detection

Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

2011 ◽  
Vol 382 ◽  
pp. 163-166
Author(s):  
Qing Xin Zhang ◽  
Jin Li ◽  
Hai Bin Li ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Wavelet Packet ◽  
Frequency Component ◽  
Wavelet Neural Network ◽  
Simulation Software ◽  
Monitoring Methods ◽  
Stator Current ◽  
Fundamental Frequency Component

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

scholarly journals Decreased calmodulin recruitment triggers PMCA4 dysfunction and pancreatic injury in cystic fibrosis

2020 ◽  
Author(s):  
Tamara Madácsy ◽  
Árpad Varga ◽  
Noémi Papp ◽  
Barnabás Deák ◽  
Bálint Tél ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Intracellular Signaling ◽  
Pancreatic Injury ◽  
Exocrine Pancreatic Function ◽  
Model Systems ◽  
Multiple Model ◽  
Calmodulin Binding ◽  
Ductal Cells ◽  
Pancreatic Damage ◽  
Intracellular Ca

ABSTRACTExocrine pancreatic damage is a common complication of cystic fibrosis (CF), which can significantly debilitate the quality of life and life expectancy of CF patients. The cystic fibrosis transmembrane conductance regulator (CFTR) has a major role in pancreatic ductal ion secretion, however, it presumably has an influence on intracellular signaling as well. Here we describe in multiple model systems, including iPSC-derived human pancreatic organoids from CF patients, that the activity of PMCA4 is impaired by the decreased expression of CFTR in ductal cells. The regulation of PMCA4, which colocalizes and physically interacts with CFTR on the apical membrane of the ductal cells, is dependent on the calmodulin binding ability of CFTR. Moreover, CFTR seems to be involved in the process of the apical recruitment of calmodulin, which enhances its role in calcium signaling and homeostasis. Sustained intracellular Ca2+ elevation in CFTR KO cells undermined the mitochondrial function and increased apoptosis. Based on these, the prevention of sustained intracellular Ca2+ overload may improve the exocrine pancreatic function and may have a potential therapeutic aspect in CF.

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

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