scholarly journals Third Order Sliding Mode Observer based Robust Fault Diagnosis for Robot Manipulators

2012 ◽  
Vol 18 (7) ◽  
pp. 669-672 ◽  
Author(s):  
Mien Van ◽  
Hee-Jun Kang ◽  
Young-Soo Suh
Keyword(s):  
Fault Diagnosis ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Sliding Mode Observer ◽  
Third Order

Robust actuator and sensor fault reconstruction of wind turbine using modified sliding mode observer

2018 ◽  
Vol 41 (6) ◽  
pp. 1504-1518 ◽  
Author(s):  
Mostafa Rahnavard ◽  
Moosa Ayati ◽  
Mohammad Reza Hairi Yazdi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Rotational Speed ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Fault Reconstruction ◽  
Different Order ◽  
Aerodynamic Torque

This paper proposes a robust fault diagnosis scheme based on modified sliding mode observer, which reconstructs wind turbine hydraulic pitch actuator faults as well as simultaneous sensor faults. The wind turbine under consideration is a 4.8 MW benchmark model developed by Aalborg University and kk-electronic a/s. Rotor rotational speed, generator rotational speed, blade pitch angle and generator torque have different order of magnitudes. Since the dedicated sensors experience faults with quite different values, simultaneous fault reconstruction of these sensors is a challenging task. To address this challenge, some modifications are applied to the classic sliding mode observer to realize simultaneous fault estimation. The modifications are mainly suggested to the discontinuous injection switching term as the nonlinear part of observer. The proposed fault diagnosis scheme does not require know the exact value of nonlinear aerodynamic torque and is robust to disturbance/modelling uncertainties. The aerodynamic torque mapping, represented as a two-dimensional look up table in the benchmark model, is estimated by an analytical expression. The pitch actuator low pressure faults are identified using some fault indicators. By filtering the outputs and defining an augmented state vector, the sensor faults are converted to actuator faults. Several fault scenarios, including the pitch actuator low pressure faults and simultaneous sensor faults, are simulated in the wind turbine benchmark in the presence of measurement noises. Simulation results show that the modified observer immediately and faithfully estimates the actuator faults as well as simultaneous sensor faults with different order of magnitudes.

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

scholarly journals Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer

2019 ◽  
Vol 9 (24) ◽  
pp. 5404 ◽  
Author(s):  
Farzin Piltan ◽  
Alexander E. Prosvirin ◽  
Inkyu Jeong ◽  
Kichang Im ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Rolling Element Bearing ◽  
Fault Estimation ◽  
Detection And Identification ◽  
Rolling Element ◽  
Fuzzy Sliding Mode ◽  
Element Bearing

Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system’s dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system’s modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.

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