Anomaly detection of gas turbines based on normal pattern extraction

2020 ◽  
Vol 166 ◽  
pp. 114664 ◽  
Author(s):  
Mingliang Bai ◽  
Jinfu Liu ◽  
Jinhua Chai ◽  
Xinyu Zhao ◽  
Daren Yu
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Pattern Extraction ◽  
Normal Pattern

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

Trajectory pattern extraction and anomaly detection for maritime vessels

2021 ◽  
pp. 100436
Author(s):  
Gözde Boztepe Karataş ◽  
Pinar Karagoz ◽  
Orhan Ayran
Keyword(s):  
Anomaly Detection ◽  
Pattern Extraction ◽  
Trajectory Pattern

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.

scholarly journals Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Shahram Payandeh ◽  
Eddie Chiu
Keyword(s):  
Older Adults ◽  
Anomaly Detection ◽  
Life Style ◽  
Daily Living ◽  
Movement Patterns ◽  
Monitoring Systems ◽  
Place Of Residence ◽  
Normal Pattern ◽  
Seamless Integration ◽  
Pagerank Algorithm

It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated, and analyzed, they can be used to assist in determining some onsets of anomalies in the health of elderly at various levels of their Movements and Activities of Daily Living (MADL). This paper explores how the framework of the PageRank algorithm can be extended for monitoring the global movement patterns of older adults at their place of residence. Through utilization of an existing dataset, the paper shows how the movement patterns between various rooms can be represented as a directed graph with weighted edges. To demonstrate how PageRank can be utilized, a base graph representing a normal pattern can be defined as what can be used for further anomaly detection (e.g., at some instances of observation the measured movement pattern deviates from what is previously defined as a normal pattern). It is shown how the PageRank algorithm can detect simulated change in the pattern of motion when compared with the base-line normal pattern. This feature can offer a practical approach for detecting anomalies in movement patterns associated with older adults in their own place of residence and in support of aging in place paradigm.

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.

scholarly journals Explainable Artificial Intelligence for Anomaly Detection and Prognostic of Gas Turbines using Uncertainty Quantification with Sensor-Related Data

2021 ◽  
Author(s):  
Ahmad Kamal Mohd Nor ◽  
Srinivasa Rao Pedapati ◽  
Masdi Muhammad ◽  
Víctor Leiva
Keyword(s):  
Artificial Intelligence ◽  
Anomaly Detection ◽  
Uncertainty Quantification ◽  
Gas Turbines ◽  
Health Management ◽  
Mean Square ◽  
Simulation Data ◽  
Related Data ◽  
Local Accuracy ◽  
Explainable Artificial Intelligence

Explainable artificial intelligence (XAI) is in its assimilation phase in the prognostic and health management (PHM). The literature on PHM-XAI is deficient with respect to metrics of uncertainty quantification and explanation evaluation. This paper proposes a new method of anomaly detection and prognostic for gas turbines using Bayesian deep learning and Shapley additive explanations (SHAP). The method explains the anomaly detection and prognostic and improves the performance of the prognostic, aspects that have not been considered in the literature of PHM-XAI. The uncertainty measures considered serve to broaden explanation scope and can also be exploited as anomaly indicators. Real-world gas turbine sensor-related data are tested for the anomaly detection, while NASA commercial modular aero-propulsion system simulation data, related to turbofan sensors, were used for prognostic. The generated explanation is evaluated using two metrics: consistency and local accuracy. All anomalies were successfully detected using the uncertainty indicators. Meanwhile, the turbofan prognostic results showed up to 9% improvement in root mean square error and 43% enhancement in early prognostic due to the SHAP, making it comparable to the best existing methods. The XAI and uncertainty quantification offer a comprehensive explanation for assisting decision-making. Additionally, the SHAP ability to increase PHM performance confirms its value in AI-based reliability research.

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.

Use of Vector Autoregressive Model for Anomaly Detection in Utility Gas Turbines

2019 ◽  
Author(s):  
Vipul Goyal ◽  
Mengyu Xu ◽  
Jayanta Kapat
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Autoregressive Model ◽  
Gas Turbines ◽  
Time Series Data ◽  
Combined Cycle ◽  
Series Data ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Multi Stage

Abstract This study is based on time-series data from the combined cycle utility gas turbines consisting of three-gas turbine units and one steam turbine unit. We construct a multi-stage vector autoregressive model for the nominal operation of powerplant assuming sparsity in the association among variables and use this as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the plant-operation containing anomalies. Granger causality networks, which are based on the associations between the time series streams, are learned as an important implication from the vector autoregressive modelling. Anomaly is detected by comparing the observed measurements against their predicted value.

Plant Performance Monitoring and Diagnostics: Remote, Real-Time and Automation

2014 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Power Plant ◽  
Anomaly Detection ◽  
Real Time ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Performance Modeling ◽  
Combined Cycle ◽  
Remote Monitoring System ◽  
And Performance

Combined cycle gas turbine plants are built and operated with higher availability, reliability, and performance than simple cycle in order to help provide the customer with capabilities to generate operating revenues and reduce fuel costs while enhancing dispatch competitiveness. The availability of a power plant can be improved by increasing the reliability of individual assets through maintenance enhancement and performance degradation recovery through remote efficiency monitoring to provide timely corrective recommendations. This paper presents a comprehensive system and methodology to pursue this purpose by using instrumented data to automate performance modeling for real-time monitoring and anomaly detection of combined cycle gas turbine power plants. Through thermodynamic performance modeling of main assets in a power plant such as gas turbines, steam turbines, heat recovery steam generators, condensers and other auxiliaries, the system provides an intelligent platform and methodology to drive customer-specific, asset-driven performance improvements, mitigate outage risks, rationalize operational patterns, and enhance maintenance schedules and service offerings at total plant level via taking appropriate proactive actions. In addition, the paper presents the components in the automated remote monitoring system, including data instrumentation, performance modeling methodology, operational anomaly detection, and component-based degradation assessment. As demonstrated in two examples, this remote performance monitoring of a combined cycle power plant aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive values for customers including shortening outage downtime, lowering operating fuel cost and increasing customer power sales and life cycle value of the power plant.

scholarly journals Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

2021 ◽  
Author(s):  
Ahmad Kamal Mohd Nor ◽  
Srinivasa Rao Pedapati ◽  
Masdi Muhammad
Keyword(s):  
Anomaly Detection ◽  
Uncertainty Quantification ◽  
Gas Turbines ◽  
Health Management ◽  
Detection Task ◽  
Uncertainty Measure ◽  
Real Gas ◽  
Prognostic Performance ◽  
Local Accuracy ◽  
Evaluation Metric

XAI is presently in its early assimilation phase in Prognostic and Health Management (PHM) domain. However, the handful of PHM-XAI articles suffer from various deficiencies, amongst others, lack of uncertainty quantification and explanation evaluation metric. This paper proposes an anomaly detection and prognostic of gas turbines using Bayesian deep learning (DL) model with SHapley Additive exPlanations (SHAP). SHAP was not only applied to explain both tasks, but also to improve the prognostic performance, the latter trait being left undocumented in the previous PHM-XAI works. Uncertainty measure serves to broaden explanation scope and was also exploited as anomaly indicator. Real gas turbine data was tested for the anomaly detection task while NASA CMAPSS turbofan datasets were used for prognostic. The generated explanation was evaluated using two metrics: Local Accuracy and Consistency. All anomalies were successfully detected thanks to the uncertainty indicator. Meanwhile, the turbofan prognostic results show up to 9% improvement in RMSE and 43% enhancement in early prognostic due to SHAP, making it comparable to the best published methods in the problem. XAI and uncertainty quantification offer a comprehensive explanation package, assisting decision making. Additionally, SHAP ability in boosting PHM performance solidifies its worth in AI-based reliability research.

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