A Novel Method for Fault Diagnosis Based on PCA Enhanced by Wavelet Denoising

2013 ◽  
Vol 756-759 ◽  
pp. 3450-3454
Author(s):  
Feng Qiao ◽  
Hao Ming Zhao ◽  
Feng Zhang ◽  
Qing Ma
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Original Data ◽  
Wavelet Denoising ◽  
Fault Detection And Diagnosis ◽  
Simulation Studies ◽  
Diagnosis Method ◽  
Pca Method ◽  
Novel Method ◽  
Detection And Diagnosis

There are some disadvantages for fault detection and diagnosis with traditional Principal Component Analysis (PCA) method because of its shortcomings. It is, in this paper, presented a novel fault diagnosis method based on conventional PCA enhanced by wavelet denoising. The proposed method employs wavelet denoising to deal with the signals, which can reserve enough information of original data, and then establishes PCA model. Based on SPE and T2 statistics, abnormal situation can be detected. And the location of the fault can be recognized via contribution plots. At last, the simulation studies with Matlab are carried out to verify the correctness and effectiveness of the proposed method, the advantages of the proposed method over the conventional PCA also are shown in the simulation.

Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering

2015 ◽  
Vol 731 ◽  
pp. 395-400 ◽  
Author(s):  
Qian Qian Xu ◽  
Hai Yan Zhang ◽  
He Ping Hou ◽  
Zhuo Fei Xu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Diagnosis Method ◽  
Detection And Diagnosis

The printing machine is a sort of large-scale equipment characterized by high speed and precision. A fault diagnosis method based on kernel principal component analysis (KPCA) and K-means clustering is developed to classify the types of feeding fault. The multidimensional and nonlinear data of printed image could be reduced by KPCA to make up the deficiency of the traditional K-means clustering method. In this paper, it is experimentally verified that the classification accuracy of the combined method is higher than the traditional clustering analysis method in feeding fault detection and diagnosis. This method provides a shortcut for the determination of fault sources and realizes multi-faults diagnosis of printing machinery efficiently

Method on the Fault Detection and Diagnosis for the Railway Turnout Based on the Current Curve of Switch Machine

2013 ◽  
Vol 427-429 ◽  
pp. 1022-1027 ◽  
Author(s):  
Xue Mei Mo ◽  
Yu Fang ◽  
Yun Guo Yang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Curve Matching ◽  
Reference Curve ◽  
Current Curve ◽  
Diagnosis Method ◽  
Detection And Diagnosis ◽  
Railway Turnout

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis.

Method on Fault Detection and Diagnosis for Track Circuit Based on Main Rail Voltage

2014 ◽  
Vol 670-671 ◽  
pp. 1172-1178
Author(s):  
Da Zhuang Wu ◽  
Yu Fang ◽  
Quan Song Ma
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Curve Matching ◽  
Reference Curve ◽  
Diagnosis Method ◽  
Track Circuit ◽  
Detection And Diagnosis

This paper proposes a method of the fault detection and diagnosis for the railway circuit of ZPW-2000 system based on the main track voltage curve. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted. Based on envelope algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Then, the SVM-based fault diagnosis method is used to make sure that the fault classification could be diagnosed intelligently. The experiment results show that the proposed method can accurately identify the track circuit fault state, and the accuracy rate in the diagnosis of the fault location is above 99%, which verify the effectiveness of the method in the fault detection and diagnosis.

scholarly journals EKF-Based Actuator Fault Detection and Diagnosis Method for Tilt-Rotor Unmanned Aerial Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxin Gao ◽  
Qian Zhang ◽  
Jiyang Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Fault Detection And Diagnosis ◽  
Actuator Fault ◽  
Tilt Rotor ◽  
Aerial Vehicles ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Detection And Diagnosis

Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as cruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator failure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and financial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators. Therefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault detection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation (MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis algorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose the fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF observers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The residuals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault type can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that the FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor.

An Integrated Fault Diagnosis Method Based on the ICA-SVM

2015 ◽  
Vol 740 ◽  
pp. 523-526
Author(s):  
Zhen Ping Ji ◽  
Xiao Jie Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Yield ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Level Control ◽  
Monitoring Model ◽  
Diagnosis Method ◽  
Pca Method ◽  
Tank Water

For sets of measurements does not follow a Gaussian distribution, the conventional principal component analysis (PCA) method has the disadvantage of low diagnostic yield. An integrated fault diagnosing method based on the independent component analysis (ICA) and support vector machine (SVM) was proposed. The observed data is preprocessed and feature extracted by ICA and a monitoring model was developed. When the fault is detected, SVM is adopted to classifying and diagnosing the type of faults. It is applied for fault diagnosing in the Three-Tank water level control system. The simulation results show that the fault diagnosis rates of this method is 99.8%, which can effectively detect and diagnose the fault.

scholarly journals Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Rui Tian ◽  
Fuyang Chen ◽  
Shiyi Dong
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
High Speed ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Principal Component ◽  
Air Gap ◽  
Fault Features ◽  
Diagnosis Method ◽  
Pca Method

Taking the traction motor of CRH2 high-speed train as the research object, this paper proposes a diagnosis method based on random forest and XGBoost for the compound fault resulting from stator interturn short circuit and air gap eccentricity. First, the U-phase and V-phase currents are used as fault diagnosis signal and then the Savitzky–Golay filtering method is used for the noise deduction from the signal. Second, the wavelet packet decomposition is used to extract the composite fault features and then the high-dimensional features are optimized by the principal component analysis (PCA) method. Finally, the random forest and XGBoost are combined to detect composite faults. Using the experimental data of CRH2 semiphysical simulation platform, the diagnosis of different fault modes is completed, and the high diagnosis accuracy is achieved, which verifies the validity of this method.

scholarly journals Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4140
Author(s):  
Weiguo He ◽  
Deyang Yin ◽  
Kaifeng Zhang ◽  
Xiangwen Zhang ◽  
Jianyong Zheng
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Naive Bayes ◽  
Fault Detection And Diagnosis ◽  
Fine Tuning ◽  
Pv Systems ◽  
Bayes Model ◽  
Pv Array ◽  
Diagnosis Method ◽  
Detection And Diagnosis

With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

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