scholarly journals A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

2017 ◽  
Vol 17 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Pyung Soo Kim
Keyword(s):  
Fault Detection ◽  
Hardware Implementation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Noise Levels ◽  
Filter Structure ◽  
Sensor Fault Detection ◽  
Finite Memory ◽  
Simulation Results ◽  
Residual Generation

Abstract In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

scholarly journals A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

2016 ◽  
Author(s):  
Pyung Soo Kim
Keyword(s):  
Fault Detection ◽  
Motor System ◽  
Hardware Implementation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Filter Structure ◽  
Sensor Fault Detection ◽  
Multiple Sensor ◽  
Finite Memory ◽  
Residual Generation

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

scholarly journals Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation

2020 ◽  
Vol 53 (2) ◽  
pp. 86-91
Author(s):  
Benjamin Jahn ◽  
Michael Brückner ◽  
Stanislav Gerber ◽  
Yuri A.W. Shardt
Keyword(s):  
Fault Detection ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Parity Space ◽  
Residual Generation

A Neural Network Ensemble-Based Approach to Sensor Fault Detection

2011 ◽  
Vol 467-469 ◽  
pp. 923-927
Author(s):  
Ai She Shui ◽  
Wei Min Chen ◽  
Li Chuan Liu ◽  
Yong Hong Shui
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Sensor Faults ◽  
Neural Network Ensemble ◽  
Sensor Fault ◽  
Detection Approach ◽  
Tank System ◽  
Sensor Fault Detection ◽  
Feedback Control Systems

This paper focuses on the problem of detecting sensor faults in feedback control systems with multistage RBF neural network ensemble-based estimators. The sensor fault detection framework is introduced. The modeling process of the estimator is presented. Fault detection is accomplished by evaluating residuals, which are the differences between the actual values of sensor outputs and the estimated values. The particular feature of the fault detection approach is using the data sequences of multi-sensor readings and controller outputs to establish the bank of estimators and fault-sensitive detectors. A detectability study has also been done with the additive type of sensor faults. The effectiveness of the proposed approach is demonstrated by means of three tank system experiment results.

Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults

2003 ◽  
Vol 125 (3) ◽  
pp. 634-641 ◽  
Author(s):  
C. Romesis ◽  
K. Mathioudakis
Keyword(s):  
Fault Detection ◽  
Gas Turbines ◽  
Probabilistic Neural Network ◽  
Engine Performance ◽  
Performance Model ◽  
Test Cases ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Component Faults

The diagnostic ability of probabilistic neural networks (PNN) for detecting sensor faults on gas turbines is examined. The structure and the features of a PNN, for sensor fault detection, are presented. It is shown that with the proposed formulation, a powerful tool for sensor fault identification is produced. A particular feature of the PNN produced is the ability to detect sensor faults even in the presence of engine component malfunction, as well as on deteriorated engines. In such situations, the size of bias that can be identified increases. The way to establish the limits of sensor bias that can be detected is presented along with results from application to test cases with realistic noise magnitudes. The diagnostic procedure proposed here is also supported by an engine performance model. The data used for setting up and testing the PNN are generated by such a model.

A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment

2019 ◽  
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
Enzo Losi
Keyword(s):  
Fault Detection ◽  
Simulated Data ◽  
Health State ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Machine Monitoring ◽  
Fault Detection And Classification ◽  
Sensor Fault Detection ◽  
Single Sensor ◽  
Monitoring And Diagnostics

Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS), previously developed by the authors, is improved in this paper to detect and classify different fault classes. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called I-DCIDS, can identify seven classes of fault, i.e. out of range, stuck signal, dithering, standard deviation, trend coherence, spike and bias. Fault detection is performed by means of basic mathematical laws that require some user-defined input parameters, i.e. acceptability thresholds and windows of observation. This paper presents in detail the I-DCIDS methodology for sensor fault detection and classification. Moreover, this paper reports some examples of application of the methodology to simulated data to highlight its capability to detect sensor faults which can be commonly encountered in field applications.

scholarly journals Fault Detection and Isolation Based on Dynamic Observers Applied to Gas Turbine Control Sensors

1998 ◽  
Author(s):  
S. Simani ◽  
P. R. Spina ◽  
S. Beghelli ◽  
R. Bettocchi ◽  
C. Fantuzzi
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Actual System ◽  
Analytical Redundancy ◽  
Residual Functions ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In order to prevent machine malfunctions and to determine the machine operating state, it is necessary to use correct measurements from actual system inputs and outputs. This requires the use of techniques for the detection and isolation of sensor faults. In this paper an approach based on analytical redundancy which uses dynamic observers is suggested to solve the sensor fault detection and isolation problem for a single-shaft industrial gas turbine. The proposed technique requires the generation of classical residual functions obtained with different observer configurations. The diagnosis is performed by checking fluctuations of these residuals caused by faults.

Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults

2002 ◽  
Author(s):  
C. Romessis ◽  
K. Mathioudakis
Keyword(s):  
Fault Detection ◽  
Gas Turbines ◽  
Probabilistic Neural Network ◽  
Engine Performance ◽  
Performance Model ◽  
Test Cases ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Component Faults

The diagnostic ability of Probabilistic Neural Networks (PNN) for detecting sensor faults on gas turbines is examined. The structure and the features of a PNN, for sensor fault detection, are presented. It is shown that with the proposed formulation, a powerful tool for sensor fault identification is produced. A particular feature of the PNN produced is the ability to detect sensor faults even in the presence of engine component malfunction, as well as on deteriorated engines. In such situations, the size of bias that can be identified increases. The way to establish the limits of sensor bias that can be detected is presented along with results from application to test cases with realistic noise magnitudes. The diagnostic procedure proposed here is also supported by an engine performance model. The data used for setting up and testing the PNN are generated by such a model.

scholarly journals Extended Kalman observer based sensor fault detection

2019 ◽  
Vol 9 (3) ◽  
pp. 1546
Author(s):  
Noura Rezika Bellahsene Hatem ◽  
Mohammed Mostefai ◽  
Oum El Kheir Aktouf
Keyword(s):  
Fault Detection ◽  
Power Calculation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
External Disturbances ◽  
Instantaneous Power ◽  
Detection Approach ◽  
Sensor Fault Detection ◽  
Estimate System ◽  
Instantaneous Energy

<p>This article discusses the Kalman observer based fault detection approach. The calculation of the residues can detect faults, but if there are noises, uncertainties become very important. To reduce the influence of these noises, a calculation of the instantaneous energy of the residues gave a better precision. The Kalman observer was used to estimate system performance and eliminate unknown noise and external disturbances. Instantaneous Power Calculation (IPCFD) based fault detection can detect potential sensor faults in hybrid systems. The effectiveness of the proposed approach is illustrated by the main application.</p>

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