scholarly journals A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence

2021 ◽  
Vol 11 (2) ◽  
pp. 797
Author(s):  
Ge Zhang ◽  
Qiong Yang ◽  
Guotong Li ◽  
Jiaxing Leng ◽  
Long Wang
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Local Optimum ◽  
Detection Rates ◽  
Kl Divergence ◽  
Projection Vector ◽  
Leibler Divergence ◽  
Incipient Faults ◽  
On Line ◽  
Probability Density Function Pdf

Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.

scholarly journals A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1194
Author(s):  
Ge Zhang ◽  
Qiong Yang ◽  
Guotong Li ◽  
Jiaxing Leng ◽  
Mubiao Yan
Keyword(s):  
Time Complexity ◽  
High Sensitivity ◽  
Rayleigh Quotient ◽  
Anomalous Behavior ◽  
Local Optimum ◽  
Distribution Models ◽  
Kl Divergence ◽  
Leibler Divergence ◽  
Incipient Faults ◽  
Generalized Rayleigh Quotient

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.

scholarly journals Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

2019 ◽  
Vol 42 (6) ◽  
pp. 1225-1238 ◽  
Author(s):  
Wahiba Bounoua ◽  
Amina B Benkara ◽  
Abdelmalek Kouadri ◽  
Azzeddine Bakdi
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Control Charts ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Process ◽  
Multivariate Gaussian Distribution ◽  
Tennessee Eastman Process ◽  
Leibler Divergence

Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.

scholarly journals On-Line Fault Detection and Compensation of Hydraulic Driven Machines Using Modelling Techniques

2008 ◽  
Vol 12 (2) ◽  
pp. 111-115
Author(s):  
Chrissanthi Angeli ◽  
◽  
Avraam Chatzinikolaou ◽  
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Detection Methods ◽  
Time Data ◽  
Actual System ◽  
Incipient Faults ◽  
On Line ◽  
Modelling Techniques ◽  
Drive And Control ◽  
And Control

The development of on-line fault detection methods for drive and control systems is of main importance for the modern production technology. Modelling information improves the reliability of the diagnostic method when it is involved in a fault detection system. In this paper, the use of modelling information for the fault detection of hydraulic driven machines as well as for the compensation of incipient faults is presented. For this purpose a suitable implementation environment was developed that allows the on line interaction of real time data and simulation results and makes possible their direct effect to the actual system.

scholarly journals Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1372 ◽  
Author(s):  
Baghli ◽  
Delpha ◽  
Diallo ◽  
Hallouche ◽  
Mba ◽  
...  
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Statistical Moments ◽  
Motor Speed ◽  
Current Time ◽  
Fault Detection And Classification ◽  
Leibler Divergence ◽  
Open Switch

This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.

scholarly journals Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

2007 ◽  
Vol 2 (2) ◽  
pp. 185 ◽  
Author(s):  
Jesús Mina ◽  
Cristina Verde
Keyword(s):  
Fault Detection ◽  
Large Scale ◽  
Principal Component ◽  
False Alarms ◽  
Large Scale Systems ◽  
Linear Dynamic Systems ◽  
Dynamic Principal Component Analysis ◽  
On Line ◽  
Components Analysis ◽  
Monitoring Phase

The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.

scholarly journals A Fault Detection Method Based on CPSO-Improved KICA

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 668 ◽  
Author(s):  
Mingguang Liu ◽  
Xiangshun Li ◽  
Chuyue Lou ◽  
Jin Jiang
Keyword(s):  
Fault Detection ◽  
Fitness Function ◽  
Pso Algorithm ◽  
Independent Component ◽  
Local Optimum ◽  
Tennessee Eastman Process ◽  
Detection Rates ◽  
Kernel Independent Component Analysis ◽  
Optimal Kernel ◽  
Kernel Parameters

In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR).

scholarly journals A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ge Zhang ◽  
Qiong Yang ◽  
Guotong Li ◽  
Jiaxing Leng
Keyword(s):  
Discriminant Analysis ◽  
Fault Detection ◽  
Linear Discriminant Analysis ◽  
Historical Data ◽  
Detection Methods ◽  
Linear Discriminant ◽  
Projection Vector ◽  
Optimal Projection ◽  
Incipient Faults ◽  
F Distribution

Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

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