1,720,969 research outputs found
Prediction of Gas Turbine Trip by Combining Gas Path Measurements and Vibration Signals
No full textAs well-known, gas turbine (GT) trip causes a reduction of GT lifespan and makes costs increase, because of unscheduled stops. Thus, predicting GT trip in advance would allow saving costs and maintaining high operational efficiency and availability.
For these reasons, this paper presents the development of a model based on deep learning, i.e., a temporal convolutional neural network, aimed at GT trip prediction. The model exploits both vibration data collected from multiple sensors installed at different locations of the GT asset and gas path measurements. Its prediction capability is compared to that of the models fed only with vibration signals or gas path measurements. The simultaneous exploitation of vibration signals and gas path measurements, which is the main novel contribution of this paper, is aimed at enhancing the reliability of GT trip prediction, since trip causes are heterogeneous.
The paper also investigates different time frames for predicting GT trip, by considering training and testing time windows taken at different time points from trip occurrence.
The case study is represented by a collection of field measurements referring to real-world GT trips taken from multiple GTs during several years of operation. The field data used to validate the results cover multiple ambient and operating conditions.
The results demonstrate that the simultaneous exploitation of both gas path measurements and vibration signals actually enhances the reliability of GT trip prediction in the majority of the considered cases, with accuracy values up to 73%
Ensemble Learning Approach to the Prediction of Gas Turbine Trip
No full textIn the field of gas turbine (GT) monitoring and diagnostics, GT trip is of great concern for manufactures and users. In fact, due to the number of issues that may cause a trip, its occurrence is not infrequent, and its prediction is a quite unexplored field of research. This is demonstrated by the fact that, despite its relevance, a comprehensive study on the reliability of predicting GT trip has not been proposed yet. To fill this gap, this paper investigates the fusion of five data-driven base models by means of voting and stacking, in order to improve prediction accuracy and robustness. The five benchmark supervised machine learning and deep learning classifiers are k-nearest neighbors, support vector machine (SVM), Na ive Bayes (NB), decision trees (DTs), and long short-term memory (LSTM) neural networks. While voting just averages the predictions of base models, without providing additional pieces of information, stacking is a technique used to aggregate heterogeneous models by training an additional machine learning model (namely, stacked ensemble model) on the predictions of the base models. The analyses carried out in this paper employ filed observations of both safe operation and trip events, derived from a large fleet of industrial Siemens GTs in operation. The results demonstrate that the stacked model provides higher accuracy than base models and also outperforms voting by proving more effective, especially when the reliability of the prediction of base models is poor
Methodology to Monitor Early Warnings Before Gas Turbine Trip
No full textThe current energy scenario requires that gas turbines (GTs) operate at their maximum efficiency and highest reliability. Trip is one of the most disrupting events that reduces GT availability and increases maintenance costs. To tackle the challenge of GT trip prediction, this paper presents a methodology that has the goal of monitoring the early warnings raised during GT operation and trigger an alert to avoid trip occurrence. The methodology makes use of an auto-encoder (prediction model) and a three-stage criterion (detection procedure).
The auto-encoder is first trained to reconstruct safe operation data and subsequently tested on new data collected before trip occurrence. The trip detection criterion checks whether the individually tested data points should be classified as normal or anomalous (first stage), provides a warning if the anomaly score over a given time frame exceeds a threshold (second stage), and, finally, combines consecutive warnings to trigger a trip alert in advance (third stage). The methodology is applied to a real-world case study composed of a collection of trips, of which the causes may be different, gathered from various GTs in operation during several years. Historical observations of gas path measurements taken during three days of GT operation before trip occurrence are employed for the analysis. Once optimally tuned, the methodology provides a trip alert with a reliability equal to 75%at least 10 h in advance
before trip occurrence
Methodology to Monitor Early Warnings Before Gas Turbine Trip
No full textThe current energy scenario requires that gas turbines (GTs) operate at their maximum efficiency and highest reliability. Trip is one of the most disrupting events that reduces GT availability and increases maintenance costs.
To tackle the challenge of GT trip prediction, this paper presents a methodology that has the goal of monitoring the early warnings raised during GT operation and trigger an alert to avoid trip occurrence. The methodology makes use of an autoencoder (prediction model) and a three-stage criterion (detection procedure). The autoencoder is first trained to reconstruct safe operation data and subsequently tested on new data collected before trip occurrence. The trip detection criterion checks whether the individually tested data points should be classified as normal or anomalous (first stage), provides a warning if the anomaly score over a given time frame exceeds a threshold (second stage), and, finally, combines consecutive warnings to trigger a trip alert in advance (third stage).
The methodology is applied to a real-world case study composed of a collection of trips, of which the causes may be different, gathered from various GTs in operation during several years. Historical observations of gas path measurements taken during three days of GT operation before trip occurrence are employed for the analysis.
Once optimally tuned, the methodology provides a trip alert with a reliability equal to 75% at least ten hours in advance before trip occurrence
Influence of the trigger time window on the detection of gas turbine trip
No full textGas turbine (GT) trip is one of the most disrupting events that affect GT operation,
since its occurrence causes a reduction of equipment remaining useful life as well as revenue loss because of business interruption. Thus, early detection of incipient symptoms of GT trip is crucial to ensure efficient operation and lower operation and maintenance costs. This paper applies a data-driven methodology that employs a Long Short-Term Memory (LSTM) neural network and a clustering technique to identify the time point at which trip symptoms are triggered. The same methodology also partitions trips into homogeneous clusters according to their most likely trigger position. The methodology is applied to two real-world case studies composed of a collection of trips, of which the causes are different, taken from various fleets of
industrial GTs. Data collected from twenty sensors during three days of operation before trip occurrence are analyzed. For each trigger scenario, this paper investigates different lengths of the training and testing time window (namely “trigger time window”), by considering up to 24, 18, 12 or 6 hours before and after the considered trigger position. The results demonstrate that longer time windows allow an improvement of the predictive capability
Application of Transfer Learning for the Prediction of Gas Turbine Trip
No full textOne of the most disrupting events that reduces gas turbine (GT) availability and also significantly increases maintenance costs is trip. Given its nature, data-driven methodologies, which are becoming more and more widespread for diagnostic and prognostic purposes, are best suited for its prediction. However, one of the major limitations of
data-driven models is their partial extrapolability.
To overcome such limitation, this paper exploits a transfer learning approach for sharing knowledge about GT trip among different GT fleets. The transfer learning approach applied in this paper employs a Long Short-Term Memory (LSTM) neural network that is pre-trained by using the data of a given fleet and then finetuned on the data of another fleet running in a different site. The case study is represented by a collection of field measurements referring to real-world trips taken from two GT fleets during several years of operation. The field data cover multiple ambient and operating conditions, thus providing a challenging test for the transfer learning approach.
The analyses performed in this paper demonstrate that the loss of accuracy between the source domain model (trained and tested on trips that belong to the same domain) and the predictions made on the target domain can be less than 7%. Thus, in general, the results prove that transfer learning is possible and allows comparable (though necessarily lower) accuracy to that of the source domain model
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults
No full textThe current energy market requires that gas turbines (GTs) run efficiently and reliably, thus improving their sustainability. To this aim, condition monitoring is fundamental for GT manufacturers to track performance degradation and ensure fault detection in advance.
Thanks to the huge amount of data acquired during GT operation, data-driven methodologies can be successfully employed for fault detection and prediction.
Therefore, this paper develops an unsupervised data-driven methodology for the prognostics of GT abrupt faults. The methodology comprises i) an autoencoder (AE) neural network aimed at modeling GT operation in the absence of fault symptoms and ii) a threshold-based criterion for the detection of anomalous deviations between actual data and AE predictions. A sensitivity analysis on the anomaly threshold is carried out to identify the optimal set-up of the methodology.
The paper considers GT trip as archetypal of an abrupt fault. Since no labeled data are available in the case of trip occurrence, the time point at which trip symptoms arise has to be identified. Furthermore, the paper also investigates which measurable variables are most effective for predicting GT trip, in order to provide useful insights on trip prediction.
The methodology is validated by considering a real-world case study comprising data collected from six GTs before trip occurrence. The results prove that the optimally tuned methodology correctly classifies the 80% of data. Another relevant result is that fuel mass flow rate, exhaust gas temperature, and compressor discharge temperature are the most useful measurable variables for the detection of trip symptoms
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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