Incipient faults are characterized by low-amplitude, unclear fault features, which are susceptible to unknown disturbances, leading to unsatisfactory detection performance. In this paper, an incipient fault detection enhancement method based on siamese spatial-temporal multi-mode feature contrast learning method is proposed. Firstly, we design a novel siamese spatial-temporal multi-mode convolutional neural network model consisting of two weight-shared spatial-temporal multi-mode convolutional neural networks and one feature discrimination measure operator, which are then used to extract the spatial-temporal multi-mode features of two datasets and to measure the distance between them. Then, an incipient fault feature discrimination intensification training strategy is developed to enhance the incipient fault detection performance. Specifically, this strategy intends to maximize the feature distance between the normal data and the incipient fault data, as well as that between different incipient faults, while minimizing the feature distance between the normal data and between the same incipient faults. Moreover, due to the long-term slow change characteristic of the incipient fault, the multi-head self-attention Long Short-Term Memory is presented as a dynamic feature learning model to further lopsidedly learn the incipient fault temporal long-term dependency according to attention weights utilizing the scaled dot-product multi-head self-attention mechanism. Finally, the performance of the proposed method is demonstrated on two industrial cases.

早期故障具有振幅低、故障特征不明确等特点，容易受到未知干扰的影响，导致检测效果不理想。本文提出了一种基于孪生时空多模式特征对比学习方法的早期故障检测增强方法。首先，我们设计了一种新颖的连体时空多模式卷积神经网络模型，该模型由两个权重共享的时空多模式卷积神经网络和一个特征判别测度算子组成，然后用于提取时空多模式卷积神经网络模型。 -两个数据集的模式特征并测量它们之间的距离。然后，开发了一种早期故障特征辨别强化训练策略来增强早期故障检测性能。具体来说，该策略旨在最大化正常数据与初期故障数据之间以及不同初期故障之间的特征距离，同时最小化正常数据之间以及相同初期故障之间的特征距离。此外，由于早期故障的长期缓慢变化特性，提出了多头自注意力长短期记忆作为动态特征学习模型，以根据以下公式进一步不平衡地学习早期故障时间长期依赖性利用缩放点积多头自注意力机制的注意力权重。最后，在两个工业案例上证明了所提出方法的性能。