scholarly journals Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5830
Author(s):  
Francisco Arellano-Espitia ◽  
Miguel Delgado-Prieto ◽  
Artvin-Darien Gonzalez-Abreu ◽  
Juan Jose Saucedo-Dorantes ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Support Vector Machine ◽  
Anomaly Detection ◽  
Experimental Test ◽  
Test Bench ◽  
Rolling Bearing ◽  
Industrial Sector ◽  
Support Vector ◽  
Industrial Systems ◽  
Unsupervised Anomaly Detection ◽  
Experimental Test Bench

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

scholarly journals Modeling and controller design for an experimental test bench for aircraft actuators

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881536 ◽  
Author(s):  
Yong Zhou ◽  
Xiaogang Zhou
Keyword(s):  
Experimental Test ◽  
Test Bench ◽  
Controller Design ◽  
Operating Conditions ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Hydraulic Actuator ◽  
Aerodynamic Forces ◽  
Ground Testing ◽  
Experimental Test Bench

The reliable and repeatable experimental ground testing of aircraft actuator is an essential phase before flight testing. It is not an easy task to simulate the alternating aerodynamic forces on actuators reasonably and accurately in a laboratory. In this article, an experimental test bench is designed to simulate the aerodynamic forces by a hydraulic actuator, which replicates the operating conditions that the actuator will encounter in service. In order to improve the force control performance, a feed-forward compensator and a fuzzy proportional–integral–derivative controller are designed. Both simulation and experimental results show that the designed method can improve the control performance.

scholarly journals EXPERIMENTAL TEST-BENCH FOR RESEACHING PROPERTIES OF FUEL-LUBRICANT MATERIALS

2013 ◽  
Vol 54 (1) ◽  
Author(s):  
Оleksii Puzik ◽  
Gennadii Zaionchkovskyi ◽  
Taras Tarasenko
Keyword(s):  
Experimental Test ◽  
Test Bench ◽  
Experimental Test Bench

scholarly journals A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Xu ◽  
Darong Huang ◽  
Tang Min ◽  
Yunhui Ou
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Support Vector ◽  
Swarm Optimization ◽  
Energy Characteristics ◽  
Wavelet Energy ◽  
Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

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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