A novel fault diagnosis method that is based on neural networks and statistical decision making is proposed for Permanent Magnet Linear Synchronous Machines (PMLSM). Such a type of electric machines is widely used in traction of Maglev electric trains, in several mechatronic systems, as well as in electric power generation through wave energy conversion. First a neural network with Gauss–Hermite activation function is used for modelling the dynamics of the PMSLM. The neural network is used as the fault-free model of the PMLSM. Next, to perform fault diagnosis, the output of the neural network is compared against the output that is measured in real-time from the PMLSM, when both the NN and the electric machine receive the same input. Thus, the residuals' sequence is generated. It is proven that the sum of the squares of the residuals' vectors, being weighted by the inverse of the associated covariance matrix, is a stochastic variable (statistical test) that follows the χ2 distribution. The or the confidence intervals of the distribution with degrees of freedom to be equal to the dimension of the residuals' vector provide a statistical test for inferring with a high confidence level if the PMLSM has undergone a failure or not.

中文翻译：

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用于永磁同步电机故障诊断的神经网络和统计决策

针对永磁直线同步电机(PMLSM)，提出了一种基于神经网络和统计决策的新型故障诊断方法。这种类型的电机广泛用​​于磁悬浮电动列车的牵引、多种机电一体化系统以及通过波能转换发电。首先，使用具有 Gauss-Hermite 激活函数的神经网络对 PMSLM 的动力学进行建模。神经网络用作 PMLSM 的无故障模型。接下来，为了执行故障诊断，当神经网络和电机都接收到相同的输入时，将神经网络的输出与来自 PMLSM 的实时测量的输出进行比较。因此，生成残差序列。证明残差的平方和' 由相关协方差矩阵的倒数加权的向量是遵循 χ2 分布的随机变量（统计检验）。具有等于​​残差向量维数的自由度分布的置信区间或置信区间为推断 PMLSM 是否发生故障提供了高置信水平的统计检验。