Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

2018 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Lucrezia Manservigi ◽  
Giovanni Bechini ◽  
Mauro Venturini
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Anomaly Classification ◽  
Monitoring And Diagnostics ◽  
Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Thomas Hubauer ◽  
Alin Murarasu
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Anomaly Detection ◽  
Gas Turbine ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Industrial Applications ◽  
Single Sensor ◽  
Integrated Diagnostics ◽  
Anomaly Classification

Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine (GT) industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation. To this purpose, a comprehensive and straightforward approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e., the anomaly detection algorithm (ADA) and the anomaly classification algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, intersensor statistical analysis (sensor voting), and single-sensor statistical analysis. Anomalies in the time series are identified by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude, and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes. The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens GT in operation. The results show that the DCIDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.

A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)

2017 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Thomas Hubauer ◽  
Alin Murarasu
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Anomaly Detection ◽  
Gas Turbine ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Industrial Applications ◽  
Single Sensor ◽  
Integrated Diagnostics ◽  
Anomaly Classification

Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation. To this purpose, a comprehensive and straightforward approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e. the Anomaly Detection Algorithm (ADA) and the Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, inter-sensor statistical analysis (sensor voting) and single-sensor statistical analysis. Anomalies in the time series are identified by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes. The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens gas turbine in operation. The results show that the DICDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.

Modern opportunities for preparation of ultra-pure water for feeding high-performance boiler plants

2020 ◽  
pp. 74-84
Author(s):  
A.A. Filimonova ◽  
◽  
N.D. Chichirova ◽  
A.A. Chichirov ◽  
A.A. Batalova ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
High Performance ◽  
Waste Heat ◽  
Pure Water ◽  
Combined Cycle ◽  
Feed Water ◽  
Operational Characteristics ◽  
Treatment Equipment

The article provides an overview of modern high-performance combined-cycle plants and gas turbine plants with waste heat boilers. The forecast for the introduction of gas turbine equipment at TPPs in the world and in Russia is presented. The classification of gas turbines according to the degree of energy efficiency and operational characteristics is given. Waste heat boilers are characterized in terms of design and associated performance and efficiency. To achieve high operating parameters of gas turbine and boiler equipment, it is necessary to use, among other things, modern water treatment equipment. The article discusses modern effective technologies, the leading place among which is occupied by membrane, and especially baromembrane methods of preparing feed water-waste heat boilers. At the same time, the ion exchange technology remains one of the most demanded at TPPs in the Russian Federation.

scholarly journals Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

2015 ◽  
Vol 7 (10) ◽  
pp. 12680-12703 ◽  
Author(s):  
Borja Rodríguez-Cuenca ◽  
Silverio García-Cortés ◽  
Celestino Ordóñez ◽  
Maria Alonso
Keyword(s):  
Anomaly Detection ◽  
Point Cloud ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Point Cloud Data ◽  
Cloud Data

Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

2019 ◽  
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
Enzo Losi
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Data Cleaning ◽  
Sensitivity Analyses ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Optimal Setting ◽  
Integrated Diagnostics

Abstract Sensor fault detection is a crucial aspect for raw data cleaning in gas turbine industry. To this purpose, a comprehensive approach for Improved Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named I-DCIDS) was developed by the authors to detect and classify several classes of fault. For single-sensors or redundant/correlated sensors, the I-DCIDS methodology can identify seven classes of fault, i.e. Out of Range, Stuck Signal, Dithering, Standard Deviation, Trend Coherence, Spike and Bias. Since the considered faults are detected by means of a methodology which relies on basic mathematical laws and user-defined parameters, sensitivity analyses are carried out in this paper on I-DCIDS parameters to derive some rules of thumbs about their optimal setting. The sensitivity analyses are carried out on four heterogeneous and challenging datasets with redundant sensors installed on Siemens gas turbines.

A Review on Gas Turbine Anomaly Detection for Implementing Health Management

2016 ◽  
Author(s):  
Ningbo Zhao ◽  
Xueyou Wen ◽  
Shuying Li
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Health Management ◽  
Vibration Signal ◽  
Source Information ◽  
Rapid Improvement ◽  
Recent Developments ◽  
Data Driven Approach ◽  
On Line

With the rapid improvement of equipment manufacturing technology and the ever increasing cost of fuel, engine health management has become one of the most important parts of aeroengine, industrial and marine gas turbine. As an effective technology for improving the engine availability and reducing the maintenance costs, anomaly detection has attracted great attention. In the past decades, different methods including gas path analysis, on-line monitoring or off-line analysis of vibration signal, oil and electrostatic monitoring have been developed. However, considering the complexity of structure and the variability of working environments for engine, many important problems such as the accurate modeling of gas turbine with different environment, the selection of sensors, the optimization of various data-driven approach and the fusion strategy of multi-source information still need to be solved urgently. Besides, although a large number of investigations in this area are reported every year in various journals and conference proceedings, most of them are about aeroengine or industrial gas turbine and limited literature is published about marine gas turbine. Based on this background, this paper attempts to summarize the recent developments in health management of gas turbines. For the increasing requirement of predict-and-prevent maintenance, the typical anomaly detection technologies are analyzed in detail. In addition, according to the application characteristics of marine gas turbine, this paper introduces a brief prospect on the possible challenges of anomaly detection, which may provide beneficial references for the implementing and development of marine gas turbine health management.

The NOx Emission Levels of Unconventional Fuels for Gas Turbines

1977 ◽  
Vol 99 (4) ◽  
pp. 575-579
Author(s):  
W. S. Y. Hung
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Nox Emission ◽  
Heterogeneous Combustion ◽  
Emission Model ◽  
Unconventional Gas ◽  
Gas Turbine Combustors ◽  
Combustion Systems ◽  
Good Agreement

An experimentally verified NOx emission model for gas turbines has been reported previously. The model has been modified to determine the NOx emission levels of various fuels as compared to No. 2 distillate oil. The NOx emission levels of various conventional and unconventional gas turbine fuels of interest are predicted. The predicted NOx emission levels for these fuels, including methanol, ethanol, propane, and hydrogen, are in good agreement with available laboratory and field data from stationary, aircraft, and automotive gas turbine combustors. The predicted results should be applicable to other fuel-lean, heterogeneous combustion systems.

scholarly journals Anomaly Detection on Gas Turbine Fuel System Using a Sequential Symbolic Method

2017 ◽  
Author(s):  
Fei Li ◽  
Hongzhi Wang ◽  
Guowen Zhou ◽  
Daren Yu ◽  
Jianzhong Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Detection Methods ◽  
Fuel System ◽  
Detection Model ◽  
Anomalous Data ◽  
Symbolic Sequences ◽  
Finite State ◽  
Strong Ability

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is to evaluate posterior probabilities of observing symbolic sequences and most probable state sequences they may locate. Hence an estimating based model and a decoding based model are used to identify anomalies in two different ways. Experimental results indicates that these two models have both ideal performance overall, and estimating based model has a strong ability in robustness, while decoding based model has a strong ability in accuracy, particularly in a certain range of length of sequence. Therefore, the proposed method can well facilitate existing symbolic dynamic analysis based anomaly detection methods especially in gas turbine domain.

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
Enzo Losi
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Sensor Fault ◽  
Fault Detection And Classification ◽  
Sensor Fault Detection ◽  
Optimal Setting ◽  
Monitoring And Diagnostics ◽  
Development And Validation

Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics, since raw data cleaning represents a key process in the gas turbine industry. To this end, this paper presents a comprehensive approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS), which was previously developed by the authors and has been substantially improved and validated by means of field data. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called improved-DCIDS (I-DCIDS), can identify seven classes of faults, i.e., out of range, stuck signal, dithering, standard deviation, trend coherence, spike, and bias. First, this paper details the I-DCIDS methodology for sensor fault detection and classification. The methodology uses basic mathematical laws that require some user-defined configuration parameters, i.e., acceptability thresholds and windows of observation. Second, a sensitivity analysis is carried out on I-DCIDS parameters to derive some rules of thumb about their optimal setting. The sensitivity analysis is performed on four heterogeneous and challenging datasets with redundant sensors acquired from Siemens gas turbines (GTs). The results demonstrate the diagnostic capability of the I-DCIDS approach in a real-world scenario. Moreover, the methodology proves to be suitable for all types of datasets and physical quantities and, thanks to its optimal tuning, can also identify the exact time point of fault onset.

Computer Models for Education on Performance Monitoring and Diagnostics of Gas Turbines

2002 ◽  
Vol 30 (3) ◽  
pp. 204-218 ◽  
Author(s):  
K. Mathioudakis ◽  
A. Stamatis ◽  
A. Tsalavoutas ◽  
N. Aretakis
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Measurement Data ◽  
Performance Model ◽  
Operating Conditions ◽  
Engine Operation ◽  
Monitoring And Diagnostics ◽  
Engine Components ◽  
The Way

The paper discusses how performance models can be incorporated in education on the subject of gas turbine performance monitoring and diagnostics. A particular performance model, built for educational purposes, is employed to demonstrate the different aspects of this process. The way of building a model is discussed, in order to ensure the connection between the physical principles used for diagnostics and the structure of the software. The first aspect discussed is model usage for understanding gas turbine behaviour under different operating conditions. Understanding this behaviour is essential, in order to have the possibility to distinguish between operation in ‘healthy’ and ‘faulty’ engine condition. A graphics interface is used to present information in different ways such as operating line, operating points on component maps, interrelation between performance variables and parameters. The way of studying faulty engine operation is then presented, featuring a novel comparison to existing simulation programs. Faults can be implanted into different engine components and their impact on engine performance studied. The notion of fault signatures on measured quantities is explained. The model has also a diagnostic capability, allowing the introduction of measurement data from faulty engines and providing a diagnosis, namely a picture of how the performance of engine components has deviated from a ‘healthy’ condition

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