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.

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

Adaptive Diagnosis of Hamiltonian Networks under the Comparison Model

2021 ◽  
pp. 2150015
Author(s):  
Wenjun Liu ◽  
Wenjun Li
Keyword(s):  
Fault Diagnosis ◽  
Upper Bound ◽  
Multiprocessor Systems ◽  
Major Goal ◽  
Comparison Model ◽  
A Value ◽  
Pmc Model ◽  
Diagnosis Algorithm ◽  
Test Outcomes

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound.

scholarly journals Integrated Sensor Fault Diagnosis and Fault-Tolerant Control for Manipulator

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Bowen Hong ◽  
Lina Yao ◽  
Zhiwei Gao
Keyword(s):  
Fault Diagnosis ◽  
Controller Design ◽  
Fault Tolerant ◽  
Adaptive Observer ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Fault ◽  
Integrated Sensor ◽  
Model Control ◽  
Constant Fault

In this paper, an integrated scheme including fault diagnosis and fault-tolerant controller design is proposed for the manipulator system with the sensor fault. Any constant fault or time-varying fault can be estimated by the fault diagnosis scheme based on the adaptive observer rapidly and accurately, and the designed parameters can be solved by the linear matrix inequality. Using the fault estimation information, a fault-tolerant controller combining the characteristics of the proportional differentiation control and the sliding model control is designed to trace the expected trajectory via the back-stepping method. Finally, the effectiveness of the above scheme is verified by the simulation results.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

scholarly journals Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 290 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Coarse Grained ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Entropy Estimation ◽  
Fault Recognition ◽  
Knn Classifier ◽  
Diagnosis Method ◽  
The Stability

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.

scholarly journals Proportional-Type Sensor Fault Diagnosis Algorithm for DC/DC Boost Converters Based on Disturbance Observer

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1412
Author(s):  
Kyunghwan Choi ◽  
Kyung-Soo Kim ◽  
Seok-Kyoon Kim
Keyword(s):  
Fault Diagnosis ◽  
Disturbance Observer ◽  
Estimation Error ◽  
State Observer ◽  
Operating Conditions ◽  
Feedback Gain ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Boost Converters ◽  
Diagnosis Algorithm

This study seeks an advanced sensor fault diagnosis algorithm for DC/DC boost converters governed by nonlinear dynamics with parameter and load uncertainties. The proposed algorithm is designed with a combination of proportional-type state observer and disturbance observer (DOB) without integral actions. The convergence, performance recovery and offset-free properties of the proposed algorithm are derived by analyzing the estimation error dynamics. An optimization process to assign the optimal feedback gain for the state observer is also provided. Finally, a fault diagnosis criteria is introduced to identify the location and type of sensor faults online using normalized residuals. The experimental results verify the effectiveness of the suggested technique under variable operating conditions and three types of sensor faults using a prototype 3 kW DC/DC boost converter.

Fault Diagnosis Method for Network Control Systems Applying SVM Disturbance Compensation

2013 ◽  
Vol 427-429 ◽  
pp. 1799-1802
Author(s):  
Jiao Meng ◽  
Qi Hua Xu ◽  
Lei Han
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
State Observer ◽  
Network Control ◽  
Ideal Condition ◽  
Disturbance Compensation ◽  
Network Control Systems ◽  
Diagnosis Method ◽  
Short Time ◽  
Short Time Delay

According to a network control system--NCS with short time-delay and packet loss, an state observer is designed firstly in this paper to obtain a state estimation error equation which is equivalent to an asynchronous dynamical system having event incidence constraint. Secondly, SVM is used to identify interferences of the NCS. Finally, making the identification result as compensation term adding to the state observer can make the residual only represent fault term under ideal condition and increase the robustness of NCS for interference, which can improve the fault diagnosis precision. The simulation results prove that the designed observer can diagnose faults effective and the disturbance compensation based on SVM has attained the expected effect.

scholarly journals Cloud-Based Fault Tolerant Control for a DC Motor System

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao He ◽  
Yamei Ju ◽  
Yang Liu ◽  
Bangcheng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Motor System ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Dc Motor ◽  
Fault Estimation ◽  
Packet Dropout ◽  
Actuator Faults ◽  
The Stability

The fault tolerant control problem for a DC motor system is investigated in a cloud environment. Packet dropout phenomenon introduced by the limited-capacity communication channel is considered. Actuator faults are taken into consideration and fault diagnosis and fault tolerant control methods towards actuator faults are proposed to enhance the reliability of the whole cloud-based DC motor system. The fault diagnosis unit is then established with purpose of obtaining fault information. When the actuator fault is detected by comparing the residual signal with a predefined threshold, a residual matching approach is utilized to locate the fault. The fault can be further estimated by a least-squares filter. Based on the fault estimation, a fault tolerant controller is designed to guarantee the stability as well as the control performance of the DC motor system. Simulation result on a DC motor system shows the efficiency of the fault tolerant control method proposed in this paper.

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 Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System

2021 ◽  
Author(s):  
Hanxin Chen ◽  
Yuzhuo Miao ◽  
Yongting Chen ◽  
Lu Fang ◽  
Li Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Particle Filter ◽  
Mechanical System ◽  
Centrifugal Pump ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Fault Identification ◽  
Integrity Assessment ◽  
Diagnosis Method

The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.

Sign in / Sign up

Export Citation Format

Share Document