scholarly journals A Complex Fault Diagnostic Approach of Active Distribution Network Based on SBS-SFS Optimized Multi-SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Keyan Liu ◽  
Weijie Dong ◽  
Huanyu Dong ◽  
Jia Wei ◽  
Shiwu Xiao
Keyword(s):  
Fault Diagnosis ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Superior Performance ◽  
Diagnostic Approach ◽  
Fault Identification ◽  
Support Vector ◽  
Distributed Power ◽  
Fault Type ◽  
Fault Diagnostic

After renewable energy distributed generator (DG) is connected to the power grid, traditional diverse-electric-information-based fault diagnosis approaches are not suitable for an active distributed network (ADN) due to the weak characteristics of fault current. Thus, this paper proposes a comprehensive nonformula fault diagnostic approach of ADN using only voltage as input. In the preprocess, sequential forward selection (SFS) and sequential backward selection (SBS) are utilized to optimize the input feature matrix of the sample in order to reduce the information redundancy of multiple measuring points in ADN. Then, a single “1-a-1” support vector machine (SVM) classifier is used for fault identification, and a multi-SVM, with radial basis function (RBF) as the kernel function, is applied to identify the location and fault type. To prove the proposed method is adaptable for ADN, two direct drive fans are used as a DG to test the IEEE 33 node model at every 10% of the line under three operating conditions that include all cases of distributed power generation in ADN. Results comparing real-time and historical data show that the proposed multi-SVM model reaches an average fault type diagnosis accuracy of 97.27%, with a fault identification accuracy of 96%. A backpropagation neural network is then compared to the proposed model. The results show the superior performance of the SBS-SFS optimized multi-SVM. This model can be usefully applied to the fault diagnosis of new energy sources with distributed power access to distribution networks.

A Fault Diagnosis of Suck Rod Pumping System Based on SVM

2013 ◽  
Vol 307 ◽  
pp. 285-289 ◽  
Author(s):  
Wei Wu ◽  
Yu Zhou ◽  
Hang Xin Wei
Keyword(s):  
Fault Diagnosis ◽  
Support Vector ◽  
Oil Well ◽  
Pump System ◽  
Indicator Diagram ◽  
Invariant Moments ◽  
Pumping System ◽  
System A ◽  
Fault Type ◽  
Shape Characteristics

Aiming at the defects of fault diagnosis in the traditional method for sucker rod pump system, a new method based on support vector machine (SVM) pump fault diagnosis is proposed. Through studying the theory of invariant moment and the shape characteristics of pump indicator diagram, seven invariant moments is extracted from the indicator diagram as a pumping unit well condition of the characteristic parameters. Then these parameters are pretreatment, and it makes up seven eigenvector which are regarded as the input eigenvector of the SVM. The experiment indicates that the method can not only detect the fault of the pumping oil well but also can recognize the fault type of it, which is very effective for safety protection and fault diagnosis of the pumping oil.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

Online Diagnostics of Mechanical and Electrical Faults in Induction Motor Using Multiclass Support Vector Machine Algorithms Based on Frequency Domain Vibration and Current Signals

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motor ◽  
Sampling Rate ◽  
Computation Time ◽  
Current Data ◽  
Operating Conditions ◽  
Support Vector ◽  
Electrical Faults

This paper demonstrates the development of a flexible fault diagnosis methodology that can detect up to ten different faults in the induction motor (IM), simultaneously. The major IM electrical faults, such as the broken rotor bar (BRB), phase unbalance (PUF), and stator winding fault (SWF), and mechanical faults, such as bearing fault (BF), unbalanced rotor (UR), bowed rotor (BR), and misaligned rotor (MR), are considered with different fault severities for the diagnosis. The experiments are conducted with three varying loads and seven different speeds, and the frequency domain vibration and current data are acquired at a relatively low sampling rate of 1 kHz. Several statistical features are extracted and then the best feature-set is selected using the wrapper model. Thereafter, a data classification tool based on the support vector machine (SVM) is used for the fault characterization. Initially, a multi-fault diagnosis is performed by training and testing the SVM at the same operating conditions (i.e., load and speed). The performance of the classifier is found to be very good at all IM operating conditions. The main focus of this study lies in overcoming the fault diagnosis, where the data are unavailable at required operating conditions. This is accomplished by employing interpolation and extrapolation strategies for different loads and speeds. The proposed methodology not only solves practical problem of unavailability of data at different operating conditions but also shows good performance and takes low computation time, which are vital requirements of an online intelligent condition monitoring system.

Synchro Phasor Measurement Based Fault Analysis of a Parallel Transmission Line

2014 ◽  
Vol 984-985 ◽  
pp. 996-1004
Author(s):  
D. Miruthula ◽  
Ramachandran Rajeswari
Keyword(s):  
Transmission Line ◽  
Transmission System ◽  
Fault Analysis ◽  
Operating Conditions ◽  
Measurement Unit ◽  
Fault Identification ◽  
Parallel Transmission ◽  
Inference System ◽  
Fault Type ◽  
Phasor Measurement

This paper presents a new method to classify transmission line shunt faults and determine the fault location using phasor data of the transmission system. Most algorithms employed for analyzing fault data require that the fault type to be classified. The older fault-type classification algorithms are inefficient because they are not effective under certain operating conditions of the power system and may not be able to accurately select the faulted transmission line if the same fault recorder monitors multiple lines. An intelligent techniques described in this paper is used to precisely detect all ten types of shunt faults that may occur in an electric power transmission system (double-circuit transmission lines) with the help of data obtained from phasor measurement unit. This method is virtually independent of the mutual coupling effect caused by the adjacent parallel circuit and insensitive to the variation of source impedance. Thousands of fault simulations by MATLAB have proved the accuracy and effectiveness of the proposed algorithm. This paper includes the analysis of fault identification techniques using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System based protection schemes. The performances of the techniques are examined for different faults on the parallel transmission line and compared with the conventional relay scheme. The results obtained shows that ANFIS based fault identification gives better performance than other techniques.

A Research of MM-SVM Technique for Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 2633-2637
Author(s):  
Hong Yin ◽  
Shu Qiang Yang ◽  
Guo Ming Li ◽  
Ping Yin ◽  
Song Chang Jin
Keyword(s):  
Fault Diagnosis ◽  
Satellite Telemetry ◽  
Fault Identification ◽  
Support Vector ◽  
Physical Relationship ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Multiple Support ◽  
Multiple Support Vector Machines ◽  
Satellite Equipment

With the satellite development of our country, higher accuracy and stability are requires, which makes the control systems becoming more complex and requiring more telemetry parameters. Data mining techniques do not consider the physical relationship between the various components, but use of satellite telemetry parameters of the satellite states the purpose of fault identification. In this paper, we give a model based on multiple support vector machines (MM-SVM) technology satellite fault diagnosis method. The experiment shows that our method is effective in satellite equipment fault diagnosis

scholarly journals Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Liu ◽  
Songyong Liu ◽  
Qianjin Dai ◽  
Xiao Yu ◽  
Daoxiang Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Operating Conditions ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fault Detection And Identification ◽  
Vector Machines

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

scholarly journals Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Chunheng Zhao ◽  
Yi Li ◽  
Matthew Wessner ◽  
Chinmay Rathod ◽  
Pierluigi Pisu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Industrial Applications ◽  
Operating Conditions ◽  
Support Vector ◽  
Svm Classifier

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

Control Change Cause Analysis-Based Fault Diagnostic Approach

2017 ◽  
Vol 12 (6) ◽  
pp. 1182-1191 ◽  
Author(s):  
Gang-Gang Wu ◽  
◽  
Zong-Xiao Yang ◽  
Gen-Sheng Li ◽  
Lei Song
Keyword(s):  
Fault Diagnosis ◽  
Risk Index ◽  
Diagnostic Approach ◽  
Control Analysis ◽  
Change Analysis ◽  
Cause Analysis ◽  
Vehicle Engine ◽  
Control Change ◽  
Fault Diagnostic ◽  
And Control

How to identify the fault causes quickly and improve the efficiency of maintenance, which can reduce the fault disaster, has always been one of the key problems in equipments fault diagnosis. In this paper, a new qualitative fault diagnostic approach based on control change cause analysis (3CA) is proposed to identify the fault causes and fault risk index, which can be utilized to control the risk of equipment fault. We employed an existing method that was events and conditional factors analysis (ECFA+) to identify the analysis objects of 3CA, and put forward integrated methods including first principle-best practices approach, barrier failure analysis and prioritization rating code (PRC) matrix to accomplish control analysis, change analysis and significance rating of 3CA respectively, and those technical methods could be used to build the procedure diagram of identifying the content in each column of 3CA worksheet. According to the procedure of 3CA, we built a worksheet of 3CA for a vehicle engine fault, then fault causes and significance rating on behalf of the rating of fault risk index were determined. Meanwhile fault risk index had also been used to rank the fault causes, accomplishing fault diagnosis and verifying the availability or this method for fault diagnosis. The proposed approach can be able to identify fault causes of different fault modes that they have different risk index, and provide the fault causes rating that is the foundations of troubleshooting, which can mitigate and control fault disaster.

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