Large Tanning Machine Fault Detection Algorithm Research

2014 ◽  
Vol 602-605 ◽  
pp. 2035-2037
Author(s):  
Yi Li
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
External Environment ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Traditional Methods ◽  
Detection Process ◽  
Machine Fault ◽  
Random Fluctuations

In fault detection process of large tanning machine, fault signal fluctuations are susceptibly caused by interference of external environment. The traditional methods are difficult to accurately classify fault detection of such random fluctuations, resulting in latter detection with low accuracy. To avoid these shortcomings, support vector algorithm based on least squares is proposed for fault detection of large tanning machine. Experimental results show that the algorithm can improve the accuracy of fault detection.

Application of Fuzzy Least Squares Support Vector Machines in Landslide Deformation Prediction

2012 ◽  
Vol 594-597 ◽  
pp. 2402-2405 ◽  
Author(s):  
Wei Chen ◽  
Xiao Xiao ◽  
Jian Zhang
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Experimental Results ◽  
Support Vector ◽  
Deformation Prediction ◽  
Vector Machines ◽  
Landslide Deformation ◽  
Accuracy Of Prediction

Aiming at the problem that the Least Squares Support Vector Machines(LSSVM) was sensitive to noises or outliers, fuzzy idea was used to the Least Squares Support Vector Machines.The Fuzzy Least Squares Support Vector Machines(FLSSVM) was proposed and was applied to the Landslide Deformation Prediction. Experimental results show that this method can improve the accuracy of prediction and be effectively applied to landslide deformation prediction.

scholarly journals A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector Machine Classification

2021 ◽  
Author(s):  
Priyajit Biswas ◽  
Tuhina Samanta
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Correlation Coefficient ◽  
Detection Algorithm ◽  
Support Vector ◽  
Operating Characteristics ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Abstract Sensor nodes are tiny low-cost devices, prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector Machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then we used SVM to classify faulty readings from normal reading. After classification, faulty readings are discarded. We used network simulator NS-2.35 and Matlab for evaluation of our proposed method. We evaluated our fault detection algorithm using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC).

Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

2018 ◽  
Vol 22 (7) ◽  
pp. 1114-1119
Author(s):  
Hongbing Meng ◽  
Keyword(s):  
Fault Detection ◽  
Data Streams ◽  
Data Stream ◽  
Error Probability ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Traditional Methods ◽  
Detection Algorithms ◽  
Parallel Data ◽  
Soft Fault

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

scholarly journals Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals

2020 ◽  
Vol 10 (3) ◽  
pp. 5803-5807
Author(s):  
S. S. Rafiammal ◽  
D. N. Jamal ◽  
S. K. Mohideen
Keyword(s):  
Learning Algorithm ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Detection Accuracy ◽  
Severe Injuries ◽  
Hardware Implementations ◽  
Field Programmable ◽  
Health Related

Reconfigurable circuit designs for automatic seizure detection devices are essential to prevent epilepsy affected people from severe injuries and other health-related problems. In this proposed design, an automatic seizure detection algorithm based on the Linear binary Support Vector Machine learning algorithm (LSVM) is developed and implemented in a Field-Programmable Gate Array (FPGA). The experimental results showed that the mean detection accuracy is 86% and sensitivity is 97%. The resource utilization of the implemented design is less when compared to existing hardware implementations. The power consumption of the proposed design is 76mW at 100MHz. The experimental results assure that a physician can make use of this proposed design in detecting seizure events.

scholarly journals A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM

2021 ◽  
Author(s):  
Liam Biddle ◽  
Saber Fallah
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Control Systems ◽  
Autonomous Vehicle ◽  
Detection Algorithm ◽  
Support Vector ◽  
Isolation And Identification ◽  
Multiple Faults ◽  
Predictive Algorithm ◽  
Prediction Approach

AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.

scholarly journals Multiresolution Rotational Symmetry Detection via Radius-Based Frieze-Expansion

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Gang Pan ◽  
Di Sun ◽  
Yarui Chen ◽  
Chuanlei Zhang
Keyword(s):  
Image Processing ◽  
Computer Graphics ◽  
Affine Transformation ◽  
Rotational Symmetry ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Human Interaction ◽  
Effective Algorithm ◽  
Symmetry Detection ◽  
Detection Process

Rotational symmetry is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an effective algorithm for automatic symmetry recognition. In this paper, we present a rotational symmetry detection algorithm, which is easy to use and can determine both the center and the radius of the rotational symmetry supporting region without human interaction. Our algorithm is derived from frieze-expansions approach and improved through a radius-based expansion idea. Multiresolution pyramid is used to accelerate this detection process. We also discuss a solution to deal with rotational symmetry detection under slight affine transformation. Experimental results show that the method is effective for most nature images with rotational symmetry.

scholarly journals Study on the Effect of Preprocessing Methods for Spam Email Detection

2019 ◽  
Vol 4 (1) ◽  
pp. 109
Author(s):  
Fariska Zakhralativa Ruskanda
Keyword(s):  
Support Vector Machine ◽  
Communication Technology ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Detection Techniques ◽  
Negative Impacts ◽  
Email Spam ◽  
Classifier Algorithms

The use of email as a communication technology is now increasingly being exploited. Along with its progress, email spam problem becomes quite disturbing to email user. The resulting negative impacts make effective spam email detection techniques indispensable. A spam email detection algorithm or spam classifier will work effectively if supported by proper preprocessing steps (noise removal, stop words removal, stemming, lemmatization, term frequency). This research studies the effect of preprocessing steps on the performance of supervised spam classifier algorithms. Experiments were conducted on two widely used supervised spam classifier algorithms: Naïve Bayes and Support Vector Machine. The evaluation is performed on the Ling-spam corpus dataset and uses evaluation metrics: accuracy. The experimental results show that different preprocessing steps give different effects to different classifier.

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