Fault detection and diagnosis (FDD) plays an essential role in identifying and isolating various faults in a system. In general, fault detection is attained by monitoring the extent of matching between the actual operating condition and an analytical model prediction. This process aids in achieving enhanced performance, and for operating the system within the acceptable bounds. In this article, a neural network‐based classification method and fuzzy‐based control strategy are adapted to perform FDD on a two degree of freedom (2DoF) helicopter system. The operating voltage, pitch, and yaw outputs of the 2DoF helicopter system were considered for developing the algorithm. The signal processing properties of the discrete wavelet transform and pattern recognition properties of a multilayer perceptron neural network are adapted to design the classification algorithm. The developed algorithm improves training and testing efficiency. In order to reinstate the normal operation of the system, the classifier output is integrated with a hybrid fuzzy‐proportional integral derivative controller. This control technique enhances the 2DoF helicopter response as the time taken by the pitch and yaw angle to settle trajectory is reduced. The results depicted validate the efficiency of the projected approach.

中文翻译：

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二自由度直升机系统基于故障分类的新型容错控制

故障检测与诊断（FDD）在识别和隔离系统中的各种故障中起着至关重要的作用。通常，通过监视实际操作条件与分析模型预测之间的匹配程度来实现故障检测。此过程有助于提高性能，并在可接受的范围内操作系统。在本文中，基于神经网络的分类方法和基于模糊的控制策略适用于在两自由度（2DoF）直升机系统上执行FDD。在开发算法时，考虑了2DoF直升机系统的工作电压，俯仰和偏航输出。离散小波变换的信号处理特性和多层感知器神经网络的模式识别特性适用于设计分类算法。所开发的算法提高了训练和测试效率。为了恢复系统的正常运行，分类器输出与混合的模糊比例积分微分控制器集成在一起。随着俯仰和偏航角沉降轨迹所花费的时间减少，这种控制技术增强了2DoF直升机的响应能力。所描述的结果验证了该方法的有效性。分类器的输出与混合的模糊比例积分微分控制器集成在一起。随着俯仰和偏航角沉降轨迹所花费的时间减少，这种控制技术增强了2DoF直升机的响应能力。所描述的结果验证了该方法的有效性。分类器的输出与混合的模糊比例积分微分控制器集成在一起。随着俯仰和偏航角沉降轨迹所花费的时间减少，这种控制技术增强了2DoF直升机的响应能力。所描述的结果验证了该方法的有效性。