Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted.
Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and different sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less affected.
Prompt detection of incipient faults and accurate monitoring of engine deterioration are key aspects for ensuring safe operations and planning a timely maintenance. Modern computing capabilities allow for more and more complex tools for engine monitoring and diagnostics. Nevertheless, an underlying physics-based approach is often preferable, because not only the “what” but also the “why” can be identified, providing an effective decision support tool to the service engineer. In this work, a physics-based adaptive model is used to evaluate performance deltas and correct the data to reference conditions (gas turbine load and ambient conditions), while a data-driven correlation algorithm identifies the most likely matches within a fault signatures database. Possible faults are ordered from the highest correlation in the decision support system and the most likely fault can be selected based on the number of occurrences and the associated correlation. Gradual engine degradation can also be monitored by displaying performance deltas trends during time. The diagnostics tool was tested on a validated performance model of a single-shaft industrial gas turbine and subsequently on experimental data. This paper presents the diagnostics system structure, the model adaptation scheme, and the results obtained from simulated and real fault data. Accurate fault isolation and severity identification were achieved in all cases, demonstrating the tool capability for decision support system.
When performing gas turbine diagnostics using Gas Path Analysis (GPA), a convenient way of extracting the degradations is by feeding the measured data from a gas turbine to a well-tuned gas turbine performance code, which in turn calculates the deltas on the chosen health parameters matching the measured inputs. For this, a set of measured parameters must be matched with suitable health parameters, such as deltas on compressor and turbine efficiency and flow capacity. In aero engines, the number of sensors are in general limited due to cost and weight constraints and only the necessary sensors for safe engine operation are available. Some important sensors may have redundancy in case of a sensor loss but it is far from certain that this applies to all sensors available. If a sensor malfunctions by giving false or no values, the functions using the sensor will be negatively affected in some way causing them to either synthesize a fictive measurement, changing operating scheme, going into a degraded operating mode or shutting down parts or the whole process. If an onboard diagnostic algorithm fails due to sensor faults it will lead to a decrease in flight safety, thus there is a need for a robust system. This paper presents a strategy for automatic modifications of the gas turbine diagnostic matching scheme when sensors malfunction to ensure a robust function. When a sensor fault is detected and classified as malfunctioning, the gas turbine matching scheme is modified according to predefined rules. If possible, a redundant measurement replaces the faulty measurement. If not, the matching scheme will be modified by determining if any health parameters cannot be derived by the functional set of measurements and remove the least valuable health parameter while maintaining a working matching scheme for the remaining health parameters.
Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.
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