Nowadays, the importance of gas turbine monitoring and diagnostics pushes OEM operators to exploit historical data to search for early indicators of incipient failures. One of the most disrupting events that affect GT operation is gas turbine trip, 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 gas turbine trip is crucial to ensure efficient operation and lower operation and maintenance costs. This paper presents a data-driven methodology aimed at investigating and disclosing the onset of trip symptoms. The goal of the methodology is the identification of the time point at which trip symptoms are triggered, by exploring multiple scenarios characterized by different trigger positions. For each scenario, a time window of the same length is considered before and after the trigger time point. For classification purposes, the former is supposed to be representative of normal operation and thus is labeled as “No trip”, whereas the latter is labeled as “Trip”. A Long Short-Term Memory (LSTM) neural network is employed as the classification model. A predictive model is first trained for each scenario and subsequently tested on new observations (i.e., trip events) by considering the whole available timeframe. Finally, trips are clustered into homogeneous groups according to their most likely trigger position, which identifies the time point of onset of trip symptoms. 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 Siemens gas turbines. Data collected from multiple sensors during three days of operation before trip occurrence are employed and analyzed. The methodology provides the most likely trigger position for four clusters of trips within the two days before trip occurrence with a confidence in the range 66%–97%.
This paper presents the application of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations for estimating head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which simultaneously optimizes both the pump and PAT operation. The simulation model is calibrated in this paper on data taken from the literature, reporting both pump and PAT performance curves for head and efficiency over the entire range of operation. The performance data refer to twelve different centrifugal pumps, running in both pump and PAT mode. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT performance curves and best efficiency point. Prediction consistency from a physical point of view is also evaluated.
The prediction of time evolution of gas turbine (GT) performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian hierarchical model (BHM) is employed to perform a probabilistic prediction of GT future behavior, thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM-predicted outputs. Then, the BHM approach is applied to both simulated and field data representative of GT degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors are found to be lower than 1.0% or 1.7% for single- or multi-step prediction, respectively.
Nowadays, gas turbines (GTs) are equipped with an increasing number of sensors, of which the acquired data are used for monitoring and diagnostic purposes. Therefore, anomaly detection in sensor time series is a crucial aspect for raw data cleaning, in order to identify accurate and reliable data. To this purpose, a novel methodology based on Bayesian hierarchical models (BHMs) is proposed in this paper. The final aim is the exploitation of information held by a pool of observations from redundant sensors as knowledge base to generate statistically consistent measurements according to input data. In this manner, it is possible to simulate a “virtual” healthy sensor, also known as digital twin, to be used for sensor fault identification. The capability of the novel methodology based on BHM is assessed by using field data with two types of implanted faults, i.e., spikes and bias faults. The analyses consider different numbers of faulty sensors within the pool and different fault magnitudes. In this manner, different levels of fault severity are investigated. The results demonstrate that the new approach is successful in most fault scenarios for both spike and bias faults and provide guidelines to tune the detection criterion based on the morphology of the available data.
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