This work presents a study on fault diagnosis in vibration signal processing. Rather than building a fault model through frequently used approaches to handling the series data such as LSTM or hidden Markov field, this work processes the vibration signal by moving the time window to generate multiple samples and then transfers fault diagnosis into a traditional supervised learning problem. Stochastic configuration neural network (SCN) which gives a clear condition of guaranteeing high performance of randomly weighted neural networks is selected as the model for training and testing. Different classifiers are used to conduct a performance comparison, and their comparative advantages including why SCN particularly suitable for this type of learning and more discussions about the experimental results are shown. The paper provides a new scheme to processing vibration signal for fault diagnosis and some useful guidelines of building an appropriate model with high performance.

这项工作提出了振动信号处理中的故障诊断研究。这项工作不是通过常用的方法来处理序列数据（例如LSTM或隐马尔可夫场）来建立故障模型，而是通过移动时间窗口以生成多个样本来处理振动信号，然后将故障诊断转移到传统的监督学习问题中。随机配置神经网络（SCN）提供了明确的条件以保证随机加权神经网络的高性能，被选作训练和测试的模型。使用不同的分类器进行性能比较，并显示了它们的比较优势，包括为何SCN特别适合此类学习以及对实验结果的更多讨论。