Automatic Sensor Data Validation: Improving the Quality and Reliability of Rig Data

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.

Download Full-text

Streaming Sensor Data Validation in Networked Infrastructure Systems through Synergic Auto and Cross Similarity Discovery and Analysis

Computing in Civil Engineering 2019 ◽

10.1061/9780784482445.044 ◽

2019 ◽

Author(s):

Lu Xing ◽

Amin Rasekh ◽

M. Ehsan Shafiee ◽

Lina Sela ◽

Ami Preis

Keyword(s):

Sensor Data ◽

Data Validation ◽

Infrastructure Systems

Download Full-text

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Sensors ◽

10.3390/s20185264 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5264

Author(s):

Srikanth Sagar Bangaru ◽

Chao Wang ◽

Fereydoun Aghazadeh

Keyword(s):

Data Quality ◽

Activity Recognition ◽

Sensor Data ◽

Measurement Unit ◽

Activity Classification ◽

Workforce Shortage ◽

Safety And Health ◽

Construction Activity ◽

Sensor Models ◽

Quality And Reliability

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.387 ◽

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.

Download Full-text

A probabilistic approach to sensor data validation

10.2514/6.1992-3163 ◽

1992 ◽

Cited By ~ 6

Author(s):

T. BICKMORE

Keyword(s):

Probabilistic Approach ◽

Sensor Data ◽

Data Validation

Download Full-text

Sensor Data Validation in a Developmental Aero Gas Turbine Engine

Volume 2: Structures and Dynamics; Renewable Energy (Solar, Wind); Inlets and Exhausts; Emerging Technologies (Hybrid Electric Propulsion, UAV, ...); GT Operation and Maintenance; Materials and Manufacturing (Including Coatings, Composites, CMCs, Additive Manufacturing); Analytics and Digital Solutions for Gas Turbines/Rotating Machinery ◽

10.1115/gtindia2017-4841 ◽

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.

Download Full-text