scholarly journals Research on Fault Detection Method of FADS System

2020 ◽  
Vol 38 (6) ◽  
pp. 1210-1217
Author(s):  
Qianlei Jia ◽  
Weiguo Zhang ◽  
Jingping Shi ◽  
Guangwen Li ◽  
Xiaoxiong Liu
Keyword(s):  
Fault Detection ◽  
High Precision ◽  
Detection Method ◽  
Measurement Noise ◽  
Distribution Characteristics ◽  
Detection Problem ◽  
Chi Square ◽  
Statistical Knowledge ◽  
Noise Simulation ◽  
Simulation Results

In order to solve the fault detection problem of flush air data sensing (FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.

A Fast Gradual Fault Detection Method for Underwater Integrated Navigation Systems

2015 ◽  
Vol 69 (1) ◽  
pp. 93-112 ◽  
Author(s):  
Liu Yi-ting ◽  
Xu Xiao-su ◽  
Liu Xi-xiang ◽  
Zhang Tao ◽  
Li Yao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
System Failure ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Detection Program ◽  
Absolute Residual ◽  
Simulation Results ◽  
Short Time

Gradual fault detection is always an important issue in integrated navigation systems, and the gradual fault is the most difficult fault to detect. To detect gradual faults in a timely and precise manner in integrated navigation systems, the statistical concepts of the normalised residual mean and the sum of absolute residuals are introduced according to the characteristics of gradual system failure in this paper. The applicability of the improved residual χ2 detection method is discussed. Then, the gradual fault detection program based on the improved residual χ2 detection method is designed with the criterion of normalised residual mean and the sum of absolute residual. The simulation results and vehicle tests show that: 1) The residual of the failed sub-system can be calculated accurately with the improved residual χ2 detection method, which has strong applicability in gradual fault detection; 2) The gradual fault can be detected in a short time by using the normalised residual mean and the sum of absolute residual.

scholarly journals An Arc Fault Detection Method Based on Multidictionary Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Na Qu ◽  
Jianhui Wang ◽  
Jinhai Liu ◽  
Zhi Wang
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Detection Method ◽  
Amplitude Spectrum ◽  
Learning Method ◽  
Test Results ◽  
Training Samples ◽  
Simulation Results ◽  
Normal Work

This paper uses the dictionary learning of sparse representation algorithm to detect the arc fault. Six kinds of characteristics, that is, the normalized amplitudes of 0Hz, 50Hz, 100Hz, 150Hz, 200Hz, and 250Hz in the current amplitude spectrum, are used as inputs. The output is normal work or arc fault. Increasing the number of training samples can improve the accuracy of the tests. But if the training samples are too many, it is difficult to be expressed by single dictionary. This paper designs a multidictionary learning method to solve the problem. Firstly, n training samples are selected to form s overcomplete dictionaries. Then a dictionary library consisting of s dictionaries is constructed. Secondly, t (t≤s) dictionaries are randomly selected from the dictionary library to judge the test results, respectively. Finally, the final detest result is obtained through the maximum number of votes, that is, the modality with the most votes is the detest result. Simulation results show that the accuracy of detection can be improved.

scholarly journals A High-Speed Fault Detection, Identification, and Isolation Method for a Last Mile Radial LVDC Distribution Network

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2901 ◽  
Author(s):  
Saeed Jamali ◽  
Syed Bukhari ◽  
Muhammad Khan ◽  
Khawaja Mehmood ◽  
Muhammad Mehdi ◽  
...  
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Power Flow ◽  
Detection Method ◽  
Distribution Network ◽  
Test System ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Simulation Results ◽  
Vector Sum

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

2020 ◽  
Vol 73 (4) ◽  
pp. 776-796
Author(s):  
Chuang Zhang ◽  
Xiubin Zhao ◽  
Chunlei Pang ◽  
Yong Wang ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Sliding Window ◽  
Detection Performance ◽  
Fault Detection And Isolation ◽  
Global Navigation Satellite Systems ◽  
Improved Method ◽  
Chi Square ◽  
Satellite Systems ◽  
Local Test

Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.

A Fault Detection Method Based on Feedforward Compensation in Mechatronic Control System

2014 ◽  
Vol 664 ◽  
pp. 360-364
Author(s):  
Zhang Lei ◽  
Chang Tian Qing ◽  
Su Kui Feng
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Detection Method ◽  
External Disturbance ◽  
Disturbance Compensation ◽  
Band Filter ◽  
Simulation Results ◽  
Compensation Approach ◽  
The Relationship ◽  
Shaft Friction

For the fault detection of mechatronic control system, based on observer a feedforward compensation approach was presented. The model of a mechatronic control system was investigated and divided into two loops for disturbance compensation and fault detection. The external disturbance of the vehicle was analyzed, and the relationship between the support shaft friction moment as main disturbance element and the attitude effect of the vehicle was built by the way of data curve fitting. The adjustment coefficients were proposed respectively in nine kinds of running section of the vehicle, because disturbance was very complicated and time-variant in different section. Fault detection observer was designed to generate residuals in the inner loop, and the whole residual was calculated. The compensation was deduced in accordance with responses produced by the disturbance, so the disturbance could be compensated. Band filter was proposed for other noises. Finally, an example was presented to illustrate the proposed approach. The simulation results showed that the disturbance elements can be compensated rapidly.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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