scholarly journals Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiping Xie ◽  
Rongchen Zhao ◽  
Jiming Zheng ◽  
Yancheng Lang
Keyword(s):  
Fault Diagnosis ◽  
Test Results ◽  
Probability Models ◽  
Multiple Features ◽  
Confidence Levels ◽  
Probability Modeling ◽  
Dc Motors ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
And Performance

This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.

The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

2011 ◽  
Vol 308-310 ◽  
pp. 88-91
Author(s):  
Hong Bo Xu ◽  
Guo Hua Chen ◽  
Xin Hua Wang ◽  
Jun Liang
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Moving Average ◽  
Arma Model ◽  
Time Varying ◽  
Stability Evaluation ◽  
Test Results ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

2014 ◽  
Vol 687-691 ◽  
pp. 761-765
Author(s):  
Chang Hong Zhang ◽  
Si Jia Cheng ◽  
Shu Hao Cao
Keyword(s):  
Support Vector Machine ◽  
Cloud Computing ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Support Vector ◽  
Machine Fault Diagnosis ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Machine Fault ◽  
The Way

The paper puts forward the way to solve the problem of SVM training on the large scale firstly, Then perform the experiment to verify the feasibility of scheme. In the last section, SVM fault diagnosis method based on the Mapreduce is put forward.

The Study of Vibration Diagnosis Method for GIS

2013 ◽  
Vol 295-298 ◽  
pp. 2429-2432
Author(s):  
Pei Ding ◽  
Zhen Hua Yan ◽  
Fei Yue Ma ◽  
Liang Zhang ◽  
Jun Hao Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Field Test ◽  
Detection Method ◽  
Detection System ◽  
Vibration Signal ◽  
Normal Operation ◽  
Vibration Test ◽  
Test Results ◽  
Vibration Detection ◽  
Diagnosis Method

Gas insulated switchgear (GIS) will generate vibration during normal operation for the electromagnetic force. There will be generate abnormal vibration when the contacts are undesirable, the guide rod stressed unevenness. Therefore, it can be effective in fault diagnosis of this machineries through the vibration test of GIS in field. The vibration detection method of GIS equipment in field is studied in this paper, the composition of the vibration detection system is described. The field test has been done using vibration detection system and the test results show that through the vibration signal detected from the GIS equipment, the vibration characteristics of GIS can be clarified and make the fault diagnosis effectively.

scholarly journals An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 133
Author(s):  
Pu Yang ◽  
Huilin Geng ◽  
Chenwan Wen ◽  
Peng Liu
Keyword(s):  
Fault Diagnosis ◽  
Window Method ◽  
Signal Features ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sliding Window Method ◽  
Diagnosis Algorithm ◽  
Soft Threshold ◽  
Fitting Ability

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

scholarly journals Open-Switch Fault Diagnosis Algorithm and Tolerant Control Method of the Three-Phase Three-Level NPC Active Rectifier

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2495 ◽  
Author(s):  
Jun-Hyung Jung ◽  
Hyun-Keun Ku ◽  
Yung-Deug Son ◽  
Jang-Mok Kim
Keyword(s):  
Fault Diagnosis ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Fault Tolerant ◽  
Control Methods ◽  
Active Rectifier ◽  
Three Phase ◽  
Open Switch ◽  
Diagnosis Method ◽  
And Performance

This paper proposes a fault diagnosis and tolerant control methods for an open-switch fault caused in a three-phase three-level neutral-point-clamped (NPC) pulse-width modulation (PWM) active rectifier. The open-switch fault in the three-level NPC active rectifier causes a distortion in the input phase current and a ripple in the DC-link capacitor voltage. Therefore, proper fault diagnosis and tolerant control methods are required to prevent additional failures and performance degradation in the rectifier system. This paper conducted a detailed analysis of the effect of the single open-switch fault on the NPC PWM active rectifier and proposed a fault diagnosis method utilizing the DC link voltage and the phase angle of the input grid voltage. Furthermore, this paper proposes a fault-tolerant control method to reduce the effect of the open-switch fault by compensating a distorted reference voltage. The effectiveness of the proposed fault diagnosis and tolerant control methods are verified through experimental results.

scholarly journals A Neutrosophic Set Based Fault Diagnosis Method Based on Multi-Stage Fault Template Data

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 346 ◽  
Author(s):  
Wen Jiang ◽  
Yu Zhong ◽  
Xinyang Deng
Keyword(s):  
Fault Diagnosis ◽  
Weighted Averaging ◽  
Neutrosophic Set ◽  
Uncertain Information ◽  
Traditional Methods ◽  
Multiple Features ◽  
Fault Type ◽  
Multi Stage ◽  
Actual Environment ◽  
Diagnosis Method

Fault diagnosis is an important issue in various fields and aims to detect and identify the faults of systems, products, and processes. The cause of a fault is complicated due to the uncertainty of the actual environment. Nevertheless, it is difficult to consider uncertain factors adequately with many traditional methods. In addition, the same fault may show multiple features and the same feature might be caused by different faults. In this paper, a neutrosophic set based fault diagnosis method based on multi-stage fault template data is proposed to solve this problem. For an unknown fault sample whose fault type is unknown and needs to be diagnosed, the neutrosophic set based on multi-stage fault template data is generated, and then the generated neutrosophic set is fused via the simplified neutrosophic weighted averaging (SNWA) operator. Afterwards, the fault diagnosis results can be determined by the application of defuzzification method for a defuzzying neutrosophic set. Most kinds of uncertain problems in the process of fault diagnosis, including uncertain information and inconsistent information, could be handled well with the integration of multi-stage fault template data and the neutrosophic set. Finally, the practicality and effectiveness of the proposed method are demonstrated via an illustrative example.

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.

scholarly journals Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 119
Author(s):  
Qing Miao ◽  
Juhui Wei ◽  
Jiongqi Wang ◽  
Yuyun Chen
Keyword(s):  
Fault Diagnosis ◽  
Upper Bound ◽  
Estimation Error ◽  
Response Speed ◽  
Adaptive Observer ◽  
Fault Identification ◽  
Fault Estimation ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
The Stability

Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the effective fault identification, is studied in this paper. Firstly, the stability and stability conditions of fault diagnosis method based on the PI observer are analyzed, and the upper bound of the fault estimation error is given. Secondly, the fault diagnosis algorithm based on adjustable nonlinear PI observer is designed and constructed, it is analyzed and we proved that the upper bound of fault estimation under this algorithm is better than that of the traditional method. Finally, the L-1011 unmanned aerial vehicle (UAV) is taken as the experimental object for numerical simulation, and the fault diagnosis method based on adaptive observer factor achieves faster response speed and more accurate fault identification results.

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