scholarly journals Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties

Actuators ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 128
Author(s):  
Ming Yu ◽  
Haotian Lu ◽  
Hai Wang ◽  
Chenyu Xiao ◽  
Dun Lan
Keyword(s):  
Unscented Kalman Filter ◽  
Degradation Process ◽  
Fault Isolation ◽  
Parameter Uncertainties ◽  
Intermittent Fault ◽  
Diagnosis And Prognosis ◽  
Electric Scooter ◽  
Adaptive Thresholds ◽  
Degradation Characteristic ◽  
Degradation Coefficient

This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results.

scholarly journals Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model

2020 ◽  
Vol 10 (6) ◽  
pp. 2056 ◽  
Author(s):  
Jingli Yang ◽  
Yongqi Chang ◽  
Tianyu Gao ◽  
Jianfeng Wang
Keyword(s):  
Unscented Kalman Filter ◽  
Failure Prediction ◽  
Prediction Method ◽  
Degradation Process ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Ar Model ◽  
Chebyshev Inequality ◽  
Intrinsic Mode Functions ◽  
Maximal Information Coefficient

A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions (IMFs) and a residual (Res), and the metric of maximal information coefficient (MIC) is used to select eligible IMFs to reconstruct signals. Then, the approximate entropy (ApEn)-weighted energy value of the reconstructed signals are calculated to track the degradation process of the rotating machinery. Furthermore, the Chebyshev inequality is introduced to determine the prediction starting time (PST). Finally, the auto regress (AR) model and unscented Kalman filter (UKF) algorithm are used to predict the remaining useful life (RUL) of the rotating machinery. The method is fully evaluated in a test-to-failure experiment. The obtained results show that the proposed method outperforms its counterparts on failure prediction of the rotating machinery.

scholarly journals A Novel Method for Comprehensive Quality and Reliability Optimization of High-Power DC Actuators for Renewable Energy Systems

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3633
Author(s):  
Jie Deng ◽  
Hao Chen ◽  
Xuerong Ye ◽  
Huimin Liang ◽  
Guofu Zhaia
Keyword(s):  
Life Cycle ◽  
High Power ◽  
Basis Function ◽  
Orthogonal Transformation ◽  
Quantitative Relationship ◽  
Degradation Process ◽  
Design Parameters ◽  
Particle Swarm Algorithm ◽  
Parameter Uncertainties ◽  
Novel Method

To better qualify various uncertainties in design and manufacturing, as well as to understand the time-varying degradation process, a novel method of quality and reliable design and optimization for high-power DC actuators was developed in this study that considered relevant uncertainties in design, manufacturing parameters, and the degradation process. Orthogonal transformation was used to normalize heterogeneous uncertainties and the results were quantitatively described by the hyperellipsoid set model. On the basis of the uncertainty quantitative relationship, a fast substitution model was developed for high-power DC actuators with permanent magnet output characteristics of strong non-linearity and insufficient accuracy. The response surface method was used to derive the basis function, and the error between the practical measured values and the calculation values was modified by the radial basis function model. Afterwards, a life cycle global sensitivity analysis method was put forward to determine the design parameters when parameter degradation existed during the life cycle of high-power DC actuators. Then, an optimization model was established considering parameter uncertainties and reliability constraints, and the particle swarm algorithm was used to obtain the solution. Finally, the effectiveness of the proposed method was verified by a case study of high-power DC actuators in electric vehicles.

scholarly journals Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
H. Ferdowsi ◽  
S. Jagannathan
Keyword(s):  
Fault Diagnosis ◽  
Discrete Time ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Detection Scheme ◽  
Diagnosis And Prognosis ◽  
Discrete Time Systems ◽  
Local Measurements ◽  
Time Systems ◽  
Fault Diagnosis And Prognosis

This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme.

Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems

2019 ◽  
Vol 41 (6) ◽  
pp. 1686-1698 ◽  
Author(s):  
Mao Wang ◽  
Tiantian Liang
Keyword(s):  
Kalman Filter ◽  
Attitude Control ◽  
Unscented Kalman Filter ◽  
Fault Isolation ◽  
Descriptor Systems ◽  
Fault Estimation ◽  
Multiple Model ◽  
Sensor Fault ◽  
Systems Model ◽  
Sensor Fault Estimation

Sensor fault estimation and isolation is significant for an attitude control systems model of a satellite, as it works in a complex environment. The standard unscented Kalman filter algorithm may lose its accuracy when the noise is considerable. Therefore, an adaptive filtering algorithm is proposed based on the sampled-data descriptor model. The performance of the unscented Kalman filter in sensor fault estimation is improved by the adaptive algorithm depending on innovation and the measurement residual, and its convergence is guaranteed. Combining the adaptive unscented Kalman filter with the multiple-model adaptive estimation, a sensor fault isolation method is proposed. Finally, simulation examples show that this algorithm has better estimating accuracy and isolation results.

scholarly journals Intermittent Fault Detection for Uncertain Networked Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao He ◽  
Yanyan Hu ◽  
Kaixiang Peng
Keyword(s):  
Fault Detection ◽  
Networked Systems ◽  
Unknown Input ◽  
Detection Problem ◽  
Parameter Uncertainties ◽  
Markovian Jumping ◽  
Fault Detection Filter ◽  
Intermittent Fault ◽  
Robust Fault Detection ◽  
Detection Filter

This paper investigates intermittent fault detection problem for a class of networked systems with multiple state delays and unknown input. Polytopic-type parameter uncertainty in the state-space model matrices is considered. A novel measurement model is employed to account for both the random measurement delays and the stochastic data missing (package dropout) phenomenon, which are typically resulted from the limited capacity of the communication networks. We aim to design an uncertainty-dependent fault detection filter such that, for all unknown input, all possible parameter uncertainties, and all incomplete measurements, the error between residual and weighted fault is made as small as possible. By converting the addressed robust fault detection problem into an alternative robustH∞filtering problem of a certain Markovian jumping system (MJS), a sufficient condition for the existence of the desired robust fault detection filter is derived. A residual evaluation within an incremental form is brought forward to make the whole method suitable for intermittent fault detection. A numerical example is utilized to demonstrate the effectiveness of the proposed approach.

Fault Diagnosis and Prognosis of Gas Turbine Engines Based on Qualitative Modeling

2006 ◽  
Author(s):  
Kyusung Kim ◽  
Dinkar Mylaraswamy
Keyword(s):  
Fault Diagnosis ◽  
Discrete Event ◽  
Fault Isolation ◽  
Turbine Engines ◽  
Qualitative Modeling ◽  
Gas Temperature ◽  
Start Time ◽  
Event Modeling ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

This paper presents the development of a new fault diagnosis and prognosis algorithm based on qualitative modeling, which provides improved fault isolation. A fault diagnosis and prognosis algorithm is developed to detect and identify faults in the startup components of turbine engines while the faults are still in progress. Such diagnosis and prognosis will make it possible to take proper action before the system breaks down. The evidence associated with startup component failure is based on the aggregation of three dynamic events occurring in different time windows. These events are observable from speed at peak EGT (exhaust gas temperature), peak EGT, and start time. Discrete event modeling observes the unsynchronized occurrence of events. The algorithm was tested with data collected from the field; test results were obtained for twenty-nine engines, including six engines with failed startup components. The developed fault prognosis system successfully predicts failure for all six cases. In the earliest case, alarms triggered sixteen flights before the startup component breakdown and five flights in advance for the latest case.

scholarly journals Research on two dimensional Wiener stochastic degradation model based on the wear model

2018 ◽  
Vol 169 ◽  
pp. 01037
Author(s):  
Hui Zhang ◽  
Jun Yao ◽  
Yan-Lin Zhao
Keyword(s):  
Planck Equation ◽  
Degradation Process ◽  
Analytical Form ◽  
Wear Volume ◽  
Two Dimensional ◽  
Wear Model ◽  
Degradation Model ◽  
One Dimensional ◽  
Wear Process ◽  
Degradation Characteristic

One dimensional Wiener degradation process is often used to describe the degradation of product performance. However one dimensional Wiener degradation process doesn’t sufficiently consider the relevance of multiple degradation factors and wear process, which lead to inaccurate results. To overcome these problems, a new two dimensional Wiener stochastic degradation model is proposed, which applys to the products on wear process and stochastic degradation process. Combining wear model and two dimensional Wiener stochastic degradation model, a new reliability analytical form is obtained by constructing the Fokker-Planck equation. Then using the relation among wear volume, degradation characteristic lifetime and drift parameter, parameters of two dimensional Wiener degradation model on the basic of wear model can be estimated. Compared with the existing approaches, the proposed method can effectively improve accuracy. Finally, a case study is illustrated the application and advantages of the proposed method.

scholarly journals Application of hybrid Kalman filter for improving water level forecast

2016 ◽  
Vol 18 (5) ◽  
pp. 773-790 ◽  
Author(s):  
Xuan Wang ◽  
Vladan Babovic
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Model Updating ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Data Driven ◽  
Parameter Uncertainties ◽  
Hybrid Data Assimilation ◽  
Complex Nonlinear Systems ◽  
Set Up

Numerical modeling is one of the popular means to simulate and forecast the state of oceanographic systems. However, it still suffers from some limitations, e.g., parameter uncertainties, simplification of model assumptions, and absence of data for proper boundary and initial conditions. This paper proposes a hybrid data assimilation scheme, which combines the Kalman filter (KF) with a data-driven model (local linear model (LM)), to directly correct numerical model outputs at locations without measurements. Two different types of KF (unscented Kalman filter and two-sample Kalman filter) are tested and compared. A local LM is utilized to describe the evolution of the model state and then assimilated into the KF. This in turns simplifies the application of KF for highly complex nonlinear systems such as the dynamic motion of Singapore regional water. The proposed scheme is first examined using a simple hypothetical bay experiment followed by an operational model of the Singapore Regional Model (SRM), in which both are set up in the Delft3D modeling environment. This combination of KF and data-driven model provides insights into the influence of different error covariance estimations on the model updating accuracy. This research also provides guidance to offline utilization of KF in updating of numerical model output.

Derivative-free Nonlinear Version of Extended Recursive Three-step Filter for State and Parameter Estimation during Mars Entry

2018 ◽  
Vol 71 (3) ◽  
pp. 679-696
Author(s):  
Mengli Xiao ◽  
Yongbo Zhang ◽  
Huimin Fu ◽  
Zhihua Wang
Keyword(s):  
Unscented Kalman Filter ◽  
Velocity Estimation ◽  
Arbitrary Parameter ◽  
Parameter Uncertainties ◽  
Estimation Errors ◽  
Derivative Free ◽  
Nonlinear Version ◽  
Atmosphere Density ◽  
Navigation Accuracy ◽  
Drag Ratio

Parameter uncertainties which may lead to divergence of traditional Kalman filters during Mars entry are investigated in this paper. To achieve high precision navigation, a Derivative-free Nonlinear version of an Extended Recursive Three-Step Filter (DNERTSF) is introduced, which suits nonlinear systems with arbitrary parameter uncertainties. A DNERTSF can estimate the state and the parameters simultaneously, and Jacobian and Hessian calculations are not necessary for this filter. Considering the uncertainties in atmosphere density, ballistic coefficient and lift-to-drag ratio, a numerical simulation of Mars entry navigation is carried out. Compared with the standard Unscented Kalman Filter (UKF), DNERTSF can effectively reduce the adverse effects of parameter uncertainties and achieve a high navigation accuracy performance, keeping position and velocity estimation errors at a very low level. In all, the DNERTSF in this paper shows good advantages for Mars entry navigation, providing a possible application for a future Mars pinpoint landing.

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