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Multi-Auto Associative Neural Network based sensor validation and estimation for aero-engine

机译：基于多自动关联神经网络的航空发动机传感器验证和估计

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Aircraft gas turbine engine, being a complex system, uses a wide sensor network to monitor its performance for control and Engine Health Management (EHM) purposes. Both applications necessitate accurate functioning of all sensors, however due to harsh operating conditions, life and accuracy of sensors is affected. Early detection of drift in measurement or fault in sensors is important as it can help in avoiding false alarms in the EHM system. It is equally important to predict the measurement, that the sensor failed to measure, till the time sensor is replaced. An Auto Associative Neural network (AANN) based sensor validation module is an analytically-redundant sensor network, which provides continuous sensor status information and estimates the measurement value in place of faulty measurements during both online and offline data validation. The number of sensors used to monitor engine are large and it is not viable to monitor all the sensors using a single AANN. Hence in this work a novel approach is adopted for sensor validation and Estimation (SVE) where sensors are grouped into smaller sets based on their location and physical relationships between them. By breaking network into smaller groups dual benefit is achieved; first it reduces complexity arising from higher dimensionality, secondly it ensures multiple-validation of each sensor through various networks. The network is trained using data generated from a validated twin spool turbojet engine simulation model. Presented approach is validated through a simplified experiment and results show prompt fault identification and prediction of sensor value with satisfactory accuracy.
机译：飞机燃气涡轮发动机是一个复杂的系统，它使用广泛的传感器网络来监视其性能，以进行控制和发动机健康管理（EHM）。两种应用都要求所有传感器都具有准确的功能，但是由于恶劣的工作条件，会影响传感器的寿命和精度。尽早发现测量中的漂移或传感器故障很重要，因为它可以帮助避免EHM系统中的误报。同样重要的是，在更换传感器之前，要预测测量结果，即传感器无法测量。基于自动关联神经网络（AANN）的传感器验证模块是一种分析冗余传感器网络，它提供连续的传感器状态信息，并在在线和脱机数据验证期间代替错误的测量值来估计测量值。用于监视引擎的传感器数量很多，并且无法使用单个AANN监视所有传感器。因此，在这项工作中，采用了一种新颖的方法来进行传感器验证和估计（SVE），其中根据传感器的位置和它们之间的物理关系将传感器分组为较小的集合。通过将网络分成较小的组，可以获得双重利益;首先，它降低了因更高尺寸而产生的复杂性，其次，它通过各种网络确保对每个传感器进行多次验证。使用从经过验证的双阀芯涡轮喷气发动机仿真模型生成的数据来训练网络。通过简化的实验验证了所提出的方法，结果表明可以以令人满意的精度对故障进行及时的识别和预测。

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