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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor
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Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor

机译：基于单加速度传感器的基于混沌扩展神经网络理论的XXY级碰撞故障检测

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摘要

The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced.
机译：本文首次提出了XXY平台的碰撞故障检测。通过从平台的振动幅度进行信号滤波，提取平台特征信号并将其导入到主，从混沌误差系统中。轨迹图是由混沌同步动态误差信号E1和E2制成的。通过观察各种信号轨迹图的变化，将特征正负重心之间的距离以及轨迹图的最大和最小距离作为故障识别的特征。通过扩展神经网络建立了正常状态和碰撞状态的物元模型。计算XXY阶段各种故障状态的相关等级以进行诊断。 dSPACE用于通过加速度传感器对载物台故障状态进行实时分析。在这项研究中检测到三个阶段的故障状态，包括正常状态，Y碰撞故障和X碰撞故障。结果表明，该方案对XXY阶段的碰撞故障的诊断率至少为75％。结果，可以仅使用一个传感器来实现故障诊断系统，因此，大大降低了硬件成本。

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