Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

2021 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Market Force ◽  
Data Mining Technique ◽  
Heterogeneous Datasets ◽  
Clustering Approach ◽  
Almost All

Abstract At present, the challenges related to energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive Oil & Gas sector. Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips allows the prediction of their occurrence and avoids further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required. Clustering is a data mining technique that addresses the scope of partitioning multivariate time series into a given number of homogeneous and separated groups. Thus, the multivariate time series belonging to the same cluster are expected to be very similar to each other. This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study that includes transients acquired from a fleet of Siemens gas turbines in operation during three years. The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90 % in almost all cases.

Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series

2020 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Ceschini ◽  
Giovanni Bechini ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Remaining Useful Life ◽  
Market Force ◽  
Data Mining Technique ◽  
Heterogeneous Datasets ◽  
Key Factor ◽  
Clustering Approach ◽  
Almost All

Abstract The challenges related to current energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive market of the Oil & Gas sector. Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips is a key factor to predict their occurrence and avoid further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required. Clustering is a data mining technique that addresses the scope of partitioning multi-variate time series into a given number of homogeneous and separated groups. In such a manner, the multi-variate time series belonging to the same cluster are very similar to each other and dissimilar to those of the other clusters. This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study, by investigating transients acquired from a fleet of Siemens gas turbines in operation during three years. The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90 % in almost all cases.

Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

2021 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Remaining Useful Life ◽  
Training Data ◽  
The Novel ◽  
Novel Approach ◽  
Forest Models ◽  
Random Forest Models

Abstract A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions. The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.

Remaining Useful Life Estimation and Prognostication of SAC305 PCB's for Dynamic Conditions of Temperature and Vibration Loads

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Series Data ◽  
Sac305 Solder ◽  
Feature Vectors ◽  
Useful Life

Abstract This study focuses on the feature vector identification and Remaining Useful Life (RUL) estimation of SAC305 solder alloy PCB's of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels are investigated. The remaining Useful Life of the packages was estimated using a deep learning approach based on Long Short Term Memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The autocorrelation function's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.

scholarly journals Whisper and Roar

2014 ◽  
Vol 136 (07) ◽  
pp. 38-43
Author(s):  
Lee S. Langston
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Power Plants ◽  
High Efficiency ◽  
Thermal Power ◽  
Electrical Power ◽  
Rankine Cycle ◽  
Combined Cycle ◽  
Marine Applications ◽  
Almost All

This article focuses on the use of gas turbines for electrical power, mechanical drive, and marine applications. Marine gas turbines are used to generate electrical power for propulsion and shipboard use. Combined-cycle electric power plants, made possible by the gas turbine, continue to grow in size and unmatched thermal efficiency. These plants combine the use of the gas turbine Brayton cycle with that of the steam turbine Rankine cycle. As future combined cycle plants are introduced, we can expect higher efficiencies to be reached. Since almost all recent and new U.S. electrical power plants are powered by natural gas-burning, high-efficiency gas turbines, one has solid evidence of their contribution to the greenhouse gas reduction. If coal-fired thermal power plants, with a fuel-to-electricity efficiency of around 33%, are swapped out for combined-cycle power plants with efficiencies on the order of 60%, it will lead to a 70% reduction in carbon emissions per unit of electricity produced.

Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

2017 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Thomas Hubauer ◽  
Alin Murarasu
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Simulated Data ◽  
Health State ◽  
Statistical Efficiency ◽  
Absolute Deviation ◽  
Detection Techniques ◽  
Dynamic Time

The reliability of gas turbine health state monitoring and forecasting depends on the quality of sensor measurements directly taken from the unit. Outlier detection techniques have acquired a major importance, as they are capable of removing anomalous measurements and improve data quality. To this purpose, statistical parametric methodologies are widely employed thanks to the limited knowledge of the specific unit required to perform the analysis. The backward and forward moving window (BFMW) k-σ methodology proved its effectiveness in a previous study performed by the authors, to also manage dynamic time series, i.e. during a transient. However, the estimators used by the k-σ methodology are usually characterized by low statistical robustness and resistance. This paper aims at evaluating the benefits of implementing robust statistical estimators for the BFMW framework. Three different approaches are considered in this paper. The first methodology, k-MAD, replaces mean and standard deviation of the k-σ methodology with median and mean absolute deviation (MAD), respectively. The second methodology, σ-MAD, is a novel hybrid scheme combining the k-σ and the k-MAD methodologies for the backward and the forward windows, respectively. Finally, the bi-weight methodology implements bi-weight mean and bi-weight standard deviation as location and dispersion estimators. First, the parameters of these methodologies are tuned and the respective performance is compared by means of simulated data. Different scenarios are considered to evaluate statistical efficiency, robustness and resistance. Subsequently, the performance of these methodologies is further investigated by injecting outliers in field data sets taken on selected Siemens gas turbines. Results prove that all the investigated methodologies are suitable for outlier identification. Advantages and drawbacks of each methodology allow the identification of different scenarios in which their application can be most effective.

scholarly journals Recurrent Neural Networks for Online Remaining Useful Life Estimation in Ion Mill Etching System

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Vishnu TV ◽  
Priyanka Gupta ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Gautam Shroff
Keyword(s):  
Time Series ◽  
Failure Modes ◽  
Multivariate Time Series ◽  
Regression Function ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Modes Of Failure ◽  
Deep Recurrent Neural Network ◽  
Useful Life ◽  
Etching System

We describe the approach – submitted as part of the 2018 PHM Data Challenge – for estimating time-to-failure or Remaining Useful Life (RUL) of Ion Mill Etching Systems in an online fashion using data from multiple sensors. RUL estimation from multi-sensor data can be considered as learning a regression function that maps a multivariate time series to a real-valued number, i.e. the RUL. We use a deep Recurrent Neural Network (RNN) to learn the metric regression function from multivariate time series. We highlight practical aspects of the RUL estimation problem in this data challenge such as i) multiple operating conditions, ii) lack of knowledge of exact onset of failure or degradation, iii) different operational behavior across tools in terms of range of values of parameters, etc. We describe our solution in the context of these challenges. Importantly, multiple modes of failure are possible in an ion mill etching system; therefore, it is desirable to estimate the RUL with respect to each of the failure modes. The data challenge considers three such modes of failures and requires estimating RULs with respect to each one, implying learning three metric regression functions - one corresponding to each failure mode. We propose a simple yet effective extension to existing methods of RUL estimation using RNN based regression to learn a single deep RNN model that can simultaneously estimate RULs corresponding to all three failure modes. Our best model is an ensemble of two such RNN models and achieves a score of 1:91 X 10^7 on the final validation set..

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