Virtual Sensors in the Fault Diagnosis Problem

2021 ◽  
Vol 22 (6) ◽  
pp. 298-303
Author(s):  
A. N. Zhirabok ◽  
Chkhun Ir Kim
Keyword(s):  
Fault Diagnosis ◽  
State Vector ◽  
Problem Solution ◽  
System State ◽  
Isolation And Identification ◽  
Virtual Sensors ◽  
Linear Dynamic Systems ◽  
Minimal Dimension ◽  
Feedback Matrix ◽  
Tank System

The paper is devoted to the problem of fault diagnosis (isolation and identification) in linear dynamic systems under disturbances. The performances of fault diagnosis depend on the sensors which are in the system under diagnosis. To improve the performances, additional sensors can be applied. But sometimes it is impossible to use such sensors; besides they have low reliability. In this paper, we suggest to use so-called virtual sensors instead of additional ones. To obtain such sensors,Luenberger observers can be used. Such an observer is designed in two steps. On the first step, the model of minimal dimension invariant with respect to the disturbances and estimating a predetermined component of the system state vector and some other components of the system state vector is designed. The second components are necessary to provide stability of the observer by means of generating residual and using feedback. Such components are determined during t  he process of the problem solution which is based on the canonical form of matrices describing the model. On the second step, the feedback matrix is found based on the required quality of transient. To obtain this matrix, eigenvalues are selected and coefficients of the characteristic equation are calculated. The rule to find the predetermined component of the system state vector to be estimated by vir tual obser ver is suggested. Theoretical results are illustrated by practical example of well known three tank system.

Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar

2020 ◽  
pp. 1-1
Author(s):  
Zahra Hosseinpoor ◽  
Mohammad Mehdi Arefi ◽  
Roozbeh Razavi-Far ◽  
Niloofar Mozafari ◽  
Saeede Hazbavi
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Virtual Sensors ◽  
Broken Rotor Bar

Fault diagnosis in nonlinear systems: An application to a three-tank system

2008 ◽  
Author(s):  
J. Juan Rincon-Pasaye ◽  
Rafael Martinez-Guerra ◽  
Alberto Soria-Lopez
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear Systems ◽  
Tank System

Generalization of Bass --- Gura Formula for Linear Dynamic Systems with Vector Control

2020 ◽  
pp. 41-64 ◽  
Author(s):  
A.V. Lapin ◽  
N.E. Zubov
Keyword(s):  
Vector Control ◽  
Dynamic Systems ◽  
State Vector ◽  
Pole Placement ◽  
Complex Conjugate ◽  
State Matrix ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Existence And Multiplicity ◽  
The Matrix

The compact analytic formula of calculating the feedback law (controller matrix) coefficients is developed for solving the synthesis problem of modal controller providing desired pole placement by means of the fully measured state vector in linear dynamic systems with vector control. This formula represents the generalization of the known Bass --- Gura formula, used for synthesizing modal controllers in systems with scalar control, to systems with vector control. The obtained solution is applicable to systems with state-space dimension divisible by the number of control inputs and the matrix composed of the linearly independent first block columns of the Kalman controllability matrix by a number corresponding to the quantity of the mentioned multiplicity is reversible. To use the mentioned formula, it's not required to additionally transfer the described systems of the indicated class to special canonical forms. This formula may be applied to solve both numeric and analytic problems of modal control in mentioned class, independently on a specific ratio of state-vector and control-vector dimensions as well as on existence and multiplicity of real-value poles and complex-conjugate pairs of poles in original and desirable spectrums of state matrix. The examples are considered that prove the possibility of applying the generalized block-matrix Bass --- Gura formula to calculate modal controllers for the described class of systems with vector control

Fault Diagnosis of a Sensor Network: A Distributed Filtering Approach

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Rajamani Doraiswami ◽  
Lahouari Cheded
Keyword(s):  
Fault Diagnosis ◽  
Sensor Network ◽  
Kalman Filters ◽  
Measurement Noise ◽  
Fluid System ◽  
Efficient Procedure ◽  
Sensor Actuator ◽  
Tank System ◽  
Reference Input ◽  
Filtering Approach

This paper proposes a model-based approach to develop a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and/or measurement noise. Our approach hinges on the use of a bank of Kalman filters (KF) to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is a reliable indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associated paths and leads to both the detection and isolation of any fault that occurred in the paths analyzed. The scheme is successfully evaluated on several simulated examples and on a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.

Process Fault Diagnosis Based on Process Model Knowledge: Part II—Case Study Experiments

1991 ◽  
Vol 113 (4) ◽  
pp. 627-633 ◽  
Author(s):  
R. Isermann ◽  
B. Freyermuth
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Process Models ◽  
Normal Operation ◽  
Problem Solution ◽  
Inference Mechanism ◽  
Process Knowledge ◽  
Fault Trees

A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytical problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II, case study experiments with a d.c. motor, centrifugal pump, a heat exchanger and an industrial robot show practical results of the model based fault diagnosis.

Fault diagnosis and accommodation of a three-tank system based on analytical redundancy

2002 ◽  
Vol 41 (3) ◽  
pp. 365-382 ◽  
Author(s):  
Didier Theilliol ◽  
Hassan Noura ◽  
Jean-Christophe Ponsart
Keyword(s):  
Fault Diagnosis ◽  
Analytical Redundancy ◽  
Tank System

Multi-Integral Method for Solving the Forward Dynamics of Stiff Multibody Systems

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Paul Milenkovic
Keyword(s):  
Numerical Solution ◽  
State Vector ◽  
Taylor Series Expansion ◽  
High Order ◽  
The State ◽  
System State ◽  
Time Step ◽  
Entire Interval ◽  
Free Parameters ◽  
Derivatives Of

The Hermite–Obreshkov–Padé (HOP) procedure is an implicit method for the numerical solution of a system of ordinary differential equations (ODEs) applicable to stiff dynamical systems. This procedure applies an Obreshkov condition to multiple derivatives of the system state vector, both at the start and end of a time step in the numerical solution. That condition is shown to be satisfied by the Hermite interpolating polynomial that matches the state vector and its derivatives, also at the start and end of a time step. The Hermite polynomial, in turn, can be specified in terms of the system state and its derivatives at the start of a step together with a collection of free parameters. Adjusting these free parameters to minimize magnitudes of the ODE residual and its derivatives at the end of a step serves as a proxy for matching the system state and its derivatives. A high-order Taylor expansion at the start of a time step interval models the residual and its derivatives over the entire interval. A variant of this procedure adjusts those parameters to match integrals of the system state over the duration of that interval. This is done by minimizing magnitudes of integrals of the ODE residual calculated from the extrapolating Taylor-series expansion, a process that avoids the need to determine integration constants for multiple integrals of the state. This alternative method eliminates the calculation of high-order derivatives of the system state and hence avoids loss in accuracy from floating-point round off. Numerical performance is evaluated on a dynamically unbalanced constant-velocity (CV) coupling having a high spring rate constraining shaft deflection.

scholarly journals Wave height prediction at the Caspian Sea using a data-driven model and ensemble-based data assimilation methods

2009 ◽  
Vol 11 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Ahmadreza Zamani ◽  
Ahmadreza Azimian ◽  
Arnold Heemink ◽  
Dimitri Solomatine
Keyword(s):  
Data Assimilation ◽  
Wave Height ◽  
Caspian Sea ◽  
State Vector ◽  
Coefficient Of Performance ◽  
Propagation Mechanism ◽  
System State ◽  
Extended State ◽  
The Caspian Sea ◽  
Height Prediction

There are successful experiences with the application of ANN and ensemble-based data assimilation methods in the field of flood forecasting and estuary flow. In the present work, the combination of dynamic Artificial Neural Network and Ensemble Kalman Filter (EnKF) is applied on wind-wave data. ANN is used for the time propagation mechanism that governs the time evolution of the system state. The system state consists of the significant wave height that is affected by wind speed and wind direction. The relevant inputs are selected by analysing the Average Mutual Information. By help of the observations, the EnKF will correct the output of the ANN to find the best estimate of the wave height. A combination of ANN with EnKF acts as an output correction scheme. To deal with the time-delayed states, the extended state vector is taken and the dynamic equation of the extended state vector is used in EnKF. Application of the proposed scheme is examined by using five-month hourly buoy measurement at the Caspian Sea and several model runs with different assimilation–forecast cycles.The coefficient of performance and root mean square error are used to access performance of the method.

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