Gas Turbine Health Monitoring Research Articles

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.

Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey

Health monitoring is an essential part of condition-based maintenance and prognostics and health management for gas turbines. Various health monitoring systems have been developed based on the measurement and observation of the fault symptoms including turbine performance parameters such as heat rate, and nonperformance symptoms such as structural vibration. This paper focuses on surveying state-of-the-art condition monitoring, diagnostic and prognostic techniques using performance parameters acquired from gas-path data that are mostly available from the operating systems of gas turbines. Performance parameters and the corresponding effective factors are presented in the beginning. Structure of performance monitoring and diagnostic systems are systematically laid out next, and the recent developments in each section are surveyed and discussed. Observing the importance of the prognostics in the recent trend of health monitoring research, an emphasis is given on the prognostic frameworks and their implementation for the remaining useful life prediction. A conclusion along with a brief discussion on the current state and potential future directions is provided at the end.

A Hybrid Kalman Filtering Approach Based on Federated Framework for Gas Turbine Engine Health Monitoring

The Kalman filter (KF) is the most common state estimation method for gas turbine health monitoring, and it runs in the centralized architecture. However, health estimation cannot be achieved by the KF-based method as sensor fault occurs, and malfunction of the central monitoring unit will unavoidably result to the termination of the diagnosis task. For these purposes, this paper develops a novel hybrid federated KF approach from the previous achievements. The hybrid KF consists of a bank of local filters and one master filter, and the federated filtering structure and asynchronous fusion mechanism are designed. Both the linearized KF and extended KF are employed as the local filters based on the linear correlation of thermodynamic parameters. The local state estimates and covariance are yielded in parallel, and then integrated in a master filter to produce global state estimate. The proposed methodology is evaluated and compared with the general federated KFs in terms of estimation accuracy, computational efforts, and robustness to sensor fault in the application of gas turbine health monitoring. The result shows that the hybrid KF is the best balance off the involved performance, and confirms our viewpoints in this paper.

Fusing unscented Kalman filter for performance monitoring and fault accommodation in gas turbine

The Kalman filter is widely utilized for gas turbine health monitoring due to its simplicity, robustness, and suitability for real-time implementations. The most common Kalman filter for linear systems is linearized Kalman filter, and for nonlinear systems are extended Kalman filter and unscented Kalman filter. These algorithms have proven their capabilities to estimate gas turbine performance variations with a good accuracy, and the studies are done provided that all sensor measurements are available. In this paper, a nonlinear fusion approach with consistent diagnostic mechanism based on unscented Kalman filter is proposed, especially for gas turbine performance monitoring in the case of sensor failure. The architecture of fusion method comprises a set of local unscented Kalman filters and an information mixer. The local unscented Kalman filters are utilized to estimate health parameters of various component combinations, and the results are then transferred to the mixer for the integrated estimation of global health state in fusion structure. The consistent fault diagnosis and isolation logic is designed based on the fusion architecture and combined with the fusing unscented Kalman filter, called an improved fusing unscented Kalman filter. A systematic comparison of the generic linearized Kalman filter, extended Kalman filter, and unscented Kalman filter to their fusion filter kinds is presented for engine health estimation of gradual deterioration and abrupt fault. The studies show that the fusing unscented Kalman filter evidently outperforms the fusing linearized Kalman filter and fusing extended Kalman filter, while the fusing Kalman filters have slightly better estimation accuracy than the basic Kalman filters. In addition, the proposed methodology can reach the reliable performance monitoring with measurement uncertainty while the conventional Kalman filters collapse.

Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

On Optimizing Anomaly Detection Rules for Gas Turbine Health Monitoring

Gas turbine health monitoring is a critical process in preventing costly unplanned maintenance and secondary damage. To monitor gas turbine health, control signals are typically collected and analyzed using anomaly detection rules and models to assess failure likelihood based on observed data patterns. An analytic designer will often deal with rule optimization tasks in order to maximize failure detection and reduce false alarms. Manual tradeoff analysis is typically time consuming and suboptimal. In this paper, we attempt to address this issue by introducing a strategy for automatic and efficient rule optimization. By focusing on optimizing rule parameters while keeping rule structure intact, we maximize the rule performance by integrating domain knowledge with data driven optimization techniques. Realizing that automated rule tuning can be computationally expensive and infeasible to complete in reasonable time, we will leverage our recently-developed scalable learning framework - iScale that allows for automatically distributing rule tuning tasks to a large number of cloud computers, which not only dramatically speeds up tuning process, but also enables us to handle big size of historical data for tuning. We also explore different search methods to make rule tuning more efficient and effective and finally demonstrate our rule optimization strategy by a real-world application.

Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using Labview

Recently, an aero gas turbine health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which occurs in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the online condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measured engine performance data and base performance results calculated by base engine performance model.

Polynomials and Neural Networks for Gas Turbine Monitoring: a Comparative Study

Gas turbine health monitoring includes the common stages of problem detection, fault identification, and prognostics. To extract useful diagnostic information from raw recorded data, these stages require a preliminary operation of computing differences between measurements and an engine baseline, which is a function of engine operating conditions. These deviations of measured values from the baseline data can be good indicators of engine health. However, their quality and success of all diagnostic stages strongly depend on an adequacy of the baseline model employed and, in particular, on mathematical techniques applied to create it. To create the baseline model we have applied polynomials and the least square method for computing their coefficients over a long period of time. Some methods were proposed to enhance such a polynomial-based model. The resulting accuracy was sufficient for reliable monitoring gas turbine deterioration effects. The polynomials previously investigated enough are used in the present study as a standard for evaluating artificial neural networks, a very popular technique in gas turbine diagnostics. The focus of this comparative study is to verify whether the use of networks results in a better description of the engine baseline. Extensive field data of two different industrial gas turbines were used to compare these two techniques in various conditions. The deviations were computed for all available data and quality of the resulting deviations plots was compared visually. A mean error of the baseline model was an additional criterion for the comparing the techniques. To find the best network configurations many network variations were realized and compared with the polynomials. Although the neural networks were found to be close to the polynomials in accuracy, they could not exceed the polynomials in any variation. In this way, it seems that polynomials can be successfully used for engine monitoring, at least for the analyzed gas turbines.Copyright © 2010 by ASME

Denoising Jet Engine Gas Path Measurements Using Nonlinear Filters

Traditionally, linear filters have been used to smooth time series of gas path measurements before performing fault detection and isolation. However, linear filters can smooth out sharp trend shifts in the signal and are also not good at removing outliers. Since most fault detection and isolation algorithms are optimized for Gaussian noise, they can show performance degradation when outliers are present. In this study, numerical results with simulated data for engine deterioration and abrupt fault show that the nonlinear rational filter with median preprocessor are useful for gas turbine health monitoring applications resulting in noise reduction of 73%-96% while preserving signal features and removing outliers.