An Algebraic Approach to Fault Detection for Surge Avoidance in Turbo Compressor

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Sayyid Mahdi Alavinia ◽  
Mohammad Javad Khosrowjerdi ◽  
Mohammad Ali Sadrnia ◽  
Hossein Kheiri ◽  
Mohammad Mehdi Fateh
Keyword(s):  
Fault Detection ◽  
Nonlinear Dynamic ◽  
Algebraic Approach ◽  
Parametric Uncertainties ◽  
Sensor Faults ◽  
Detection Approach ◽  
Highly Nonlinear ◽  
Sensor Fault Detection ◽  
Parity Space ◽  
Turbo Compressor

This paper presents an innovative algebraic sensor fault detection approach for surge avoidance in turbo compressors (TC) in the natural gas compressor stations (GCS). The main objective is surge avoidance in the presence of sensor faults in TC. In this way, the robust parity space approach for fault detection is extended to highly nonlinear dynamic of TC based on Groebner basis and elimination technique. No work has been previously reported on the use of this technique for nonlinear dynamic systems with parametric uncertainties. This algebraic approach is simulated on the Moore–Greitzer control oriented model in the presence of parametric uncertainties, disturbances, and sensor faults. Simulation results are presented to demonstrate the effectiveness of the proposed fault detection approach.

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.

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>

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

scholarly journals Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Jingjing Liu ◽  
Min Zhang ◽  
Hai Wang ◽  
Wei Zhao ◽  
Yan Liu
Keyword(s):  
Fault Detection ◽  
Clustering Analysis ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Control Loop ◽  
One Dimensional ◽  
Air Handling Unit ◽  
Sensor Fault Detection ◽  
Diagnosis Method ◽  
Detection And Diagnosis

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

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.

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.

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