Gas Turbine Engine Fault Research Articles

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.

Fault feature signal extraction method for rolling bearings in gas turbine engines based on threshold parameter decision screening

Vibrations during the operation of gas turbine engines (GTEs) include shocks from the combustion process as well as vibrations from high-speed rotating components, which may mask or distort the characteristic signals of rolling bearing failure. Compared to those of ordinary rolling bearings, the fault characteristics of rolling bearings in GTEs are more difficult to extract. In response to the difficulty of extracting characteristic signals of rolling bearing faults in gas turbine engines due to the effect of environmental noise, a method for extracting characteristic features of GTE rolling bearings is proposed. First, the particle swarm optimization (PSO) algorithm is used to optimize the parameters in the variational mode decomposition (VMD) algorithm, resulting in K0 modal components. VMD can not only suppress the modal aliasing phenomenon, but also decompose signals at different scales, thereby providing multi-resolution information. Second, a new parameter harmonization equation is introduced, which balances the relationship between the kurtosis and correlation coefficients, and is fused into a single parameter P. Subsequently, high signal-to-noise ratio (SNR) signals are selected ted based on threshold parameter criteria, and these high-SNR signals are used to generate new vibration signals. Finally, the envelope spectrum is used to extract fault characteristics of bearings. According to the results, the fault feature signal extraction (FFSE) method can extract fault characteristics in rolling bearings under both simple and complex transmission paths.

Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine

Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. The gas turbine performance model was developed by considering variable inlet guide vane and bleed air. Fouling and erosion were injected into all six main components of the gas turbine engine. Faulty and non-faulty data were generated from the developed performance model. Based on sensitivity analysis, 12 measurement parameters and 11,824 data points were selected for the development of a fault detection and isolation model. The faulty and non-faulty data were balanced, smoothed, corrected and normalized. Finally, the classification accuracy of the machine learning techniques was analyzed. The result shows that K-Nearest Neighbours, Neural Network and Decision Tree classifiers exhibited high classification accuracy, about 99% with all three data smoothing techniques. It is also observed that the computation time of Support Vector Machine is higher whereas K-Nearest Neighbours shows the lowest. Finally, the research proves that K-Nearest Neighbours is the best classification technique for gas turbine engine fault detection and isolation application.

Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.

Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis

Gas-path analysis method has been widespread applied to gas turbine engine health control and has become one of the key techniques in favor of condition-oriented maintenance strategy. Theoretically, gas-path analysis method (especially nonlinear gas-path analysis) can easily quantify gas-path component degradations. However, when the number of components within engine is large which highly expands the dimension of fault coefficient matrix, maybe leading to strong smearing effect (i.e. predicted degradations are located almost in all component health parameters, although some of them are not really degraded), the degraded components may not be accurately identified. In order to improve the robustness of gas turbine gas-path fault diagnosis, a hybrid gas-path analysis approach integrating gray relation algorithm into gas-path analysis method has been proposed in this study. The gray relation algorithm and gas-path analysis method approach includes two steps. First, the faulty components are recognized and isolated by gray relation algorithm, which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the gas-path analysis. The fault classification analyses and case studies have shown that the confidence level of the fault classification can reach more than 95%, when single and multiple components are degraded, and the predicted degradations are almost same as that of implanted fault patterns.

An Approach for Optimal Measurements Selection on Gas Turbine Engine Fault Diagnosis

Gas path fault diagnosis plays an important role in guaranteeing safe, reliable and cost-effective operation for gas turbine engines. Measurements selection is among the most critical issues for diagnostic method implementation. In this paper, an integration approach for optimal measurements selection, which combines finger print diagrams analysis, health parameters correlation analysis, performance estimation uncertainty index analysis and fault cases validation based on genetic algorithm, has been proposed and applied to assess the health condition of a two-spool split flow turbofan in test bed. First, mathematical description of an engine gas path fault diagnosis process was given and the influence coefficient matrix was also calculated based on a well calibrated nonlinear engine performance simulation model. Second, the number of combination candidates was reduced from 782 to 256 and three measurements were picked out using the finger print diagrams analysis and the health parameters correlation analysis. Then, the number of the combination candidates was further narrowed down to 13 using the performance estimation uncertainty index analysis. A nonlinear genetic algorithm fault diagnosis method was applied to test the diagnostic ability of the remaining measurement candidates. Finally, an optimal measurement combination was worked out which demonstrated the effectiveness of the integration approach. This integration approach for optimal measurements selection is also applicable to other type of gas turbine engines.

Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.

퍼지 로직 시스템을 이용한 항공기 가스터빈 엔진 오류 검출에 대한 연구

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

An agent-based system for distributed fault diagnosis

A multi-agent system is demonstrated to be a suitable architecture for solving distributed problems. An agent-based system for distributed fault diagnosis is introduced in this paper. Within the multi-agent framework, the dynamic system is monitored by a group of agents and each agent can make its own detection decisions. To coordinate the low-level monitoring results, a high-level inference is then deployed to assess the state of system and isolate the potential fault. The structure of the agent framework is described in this paper and the role of each agent in the framework is summarised. Some intelligent model-based fault diagnosis methods used by the local diagnostic agents are discussed. A hidden Markov model (HMM) approach is proposed to achieve the result sharing for distributed fault diagnosis. Given the monitoring report from different agents, HMM based algorithms can be applied to coordinate the partial diagnostic results from different agents and find the most likely state evolution for fault isolation. Some simulation results are demonstrated and an agent-based software demonstrator on gas turbine engine fault diagnosis is presented for investigating agent implementation issues.

Service‐oriented architecture on the Grid for integrated fault diagnostics

AbstractFor industrial fault diagnostics, many model‐based fault diagnosis approaches have been proposed so far and some of them have been put into practice. However, for modern complex processes, owing to the variable nature of faults and model uncertainty, no single method can diagnose all faults and meet different contradictory criteria. In this paper, the importance of integration of different fault detection and isolation schemes in a generic problem‐solving environment is emphasized. A service‐oriented architecture for the integration is proposed, based on Grid technologies. As an engineering implementation, a decision support system for the gas turbine engine fault diagnosis is presented and some deployed services are discussed. Copyright © 2006 John Wiley & Sons, Ltd.

Multi-Agent Systems for Model-based Fault Diagnosis

Model-based methods are commonly used for fault diagnosis. But for modern complex processes, the analytical model of the overall process is not usually available. In this paper, a multi-agent framework for distributed fault diagnosis and isolation is proposed. The distributed fault diagnosis and isolation is achieved by a group of cooperative intelligent agents. The structure of the multi-agent framework is described. The learning algorithms employed by agents for building the fuzzy analytical model of the local process is detailed. The implementation of this agent-based approach on aero gas turbine engine fault diagnosis is also discussed.