scholarly journals Sensor Data Validation and Reconstruction in Water Networks: A Methodology and Software Implementation

2016 ◽  
pp. 88-93
Author(s):  
Diego García ◽  
Joseba Quevedo ◽  
Vicenç Puig ◽  
Miquel Àngel Cugueró
Keyword(s):  
Software Implementation ◽  
Sensor Data ◽  
Data Validation ◽  
Water Networks

An Adaptive Fuzzy Diagnosis System for On-Line Sensor Data Validation

1998 ◽  
Vol 31 (10) ◽  
pp. 131-135 ◽  
Author(s):  
A.N. Boudaoud ◽  
M.H. Masson
Keyword(s):  
Sensor Data ◽  
Data Validation ◽  
Diagnosis System ◽  
Adaptive Fuzzy ◽  
On Line ◽  
Fuzzy Diagnosis

An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks

2000 ◽  
Author(s):  
Pong-Jeu Lu ◽  
Ming-Chuan Zhang ◽  
Tzu-Cheng Hsu ◽  
Jin Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Success Rate ◽  
Neural Nets ◽  
Sensor Data ◽  
Trend Detection ◽  
Influence Coefficient ◽  
Data Validation ◽  
Engine Operation ◽  
Artificial Neural

Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60% success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both 4-input and 8-input ANN diagnoses achieve high scores which satisfy the minimum 90% requirement. It is surprising to find that the success rate of the 4-input diagnosis is almost as good as that of the 8-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Auto-associative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

2013 ◽  
Vol 347-350 ◽  
pp. 387-391
Author(s):  
Wen Hua Hu ◽  
Bao Feng Guo ◽  
Dong Fang Xue
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic System ◽  
Sensor Data ◽  
Data Validation ◽  
Characteristic Parameters ◽  
Proposed Model ◽  
Diagnosis Rate ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fault Characteristic

Aiming at many fault characteristic parameters of modern radar and the problem that general diagnosis method is difficult to locate fault accurately. The dynamic mathematical model of radar equipment fault diagnosis system is built. The uncertainty theoretic analysis of fault diagnosis result is studied from two aspects, data processing channel and fault model. This paper combines sensor data validation with detection fusion technology, and then puts forward the fault diagnosis technology based on sensor data validation and detection fusion. The structure of the diagnostic system model is given. Case analysis proves that the proposed model and method has a significant role in improving detection performance, reducing the Bayes risk and improve fault diagnosis rate.

Sensor Data Validation in a Developmental Aero Gas Turbine Engine

2017 ◽  
Author(s):  
Usha Srinivasan ◽  
Muthuveerappan Nagalingam
Keyword(s):  
Gas Turbine ◽  
Performance Monitoring ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Sensor Data ◽  
Physical Parameters ◽  
Main Body ◽  
Data Validation ◽  
Turbine Engine ◽  
Validation Tool

Diagnosis, isolation and corrective action of incipient faults in a developmental Aero Gas Turbine Engine, mandates advanced warning of the emerging faults by apt and timely degradation monitoring in order to mitigate catastrophic failure. This calls for cautious performance monitoring during the test runs by instrumenting the engine under test with multiple sensors to acquire the physical parameters like temperature, pressure, flow and speed which are indicative of the engine degradation. Sensor reliability is critical to engine control and performance monitoring. Sensor data which serves as the primary key for assessing the engine behaviour needs to be validated before its use in determining the degradation. Particularly in a developmental engine under test, the accuracy and reliability of measurements creates the basis for understanding the engine behaviour, in order to evaluate its performance. This paper targets to develop validation tool to ensure that only trusted sensor measurements are used for engine performance computation by weeding out the erroneous data. The pre-processing of data to ensure its accuracy also serves as a “need for maintenance indicator” to warn the operator for sensor breakdowns, wearing or deterioration and detect calibration needs. Development and validation of the LabVIEW based “Sensor Data Validation Tool (SDVT)” using the actual test run data constitutes the main body of this paper along with concluding remarks which brings out the validation results and the required maintenance action.

scholarly journals A methodology and a software tool for sensor data validation/reconstruction: Application to the Catalonia regional water network

2016 ◽  
Vol 49 ◽  
pp. 159-172 ◽  
Author(s):  
Miquel À. Cugueró-Escofet ◽  
Diego García ◽  
Joseba Quevedo ◽  
Vicenç Puig ◽  
Santiago Espin ◽  
...  
Keyword(s):  
Software Tool ◽  
Sensor Data ◽  
Data Validation ◽  
Water Network ◽  
Regional Water

Sensor data validation using gray models

2003 ◽  
Vol 42 (1) ◽  
pp. 9-17 ◽  
Author(s):  
K.M. Tsang
Keyword(s):  
Sensor Data ◽  
Data Validation
Sign in / Sign up

Export Citation Format

Share Document