This paper proposes a novel fault detection and diagnosis (FDD) method using multiscale convolutional neural networks (MCNNs) for Air Handling Unit (AHU) in Heating, Ventilation, and Air conditioning (HVAC) system. In existing works, it is challenging to achieve high diagnosis performance on multiscale monitoring signals from AHU system since the feature extraction methods in these works are not powerful enough. Although the single-scale convolutional neural networks (CNNs) have been adopted to improve the ability of feature extraction in FDD, it remains difficult to extract strong discriminative feature from multiscale monitoring signals only using single-scale kernels. In this paper, a novel MCNNs-based FDD method is proposed with three different scale kernels to improve the ability of feature extraction and the end-to-end learning strategy is adopted to optimize the model of MCNNs. With strong representation ability, the proposed method can capture highly discriminative features, which can help to improve the diagnostic performance of AHU. The proposed method is compared with other five commonly used methods using the measured data from our AHU experiment platform. The comparison results demonstrate that the proposed MCNNs-based FDD method outperforms other FDD methods.

本文针对供暖，通风和空调（HVAC）系统中的空气处理单元（AHU），提出了一种使用多尺度卷积神经网络（MCNN）的新型故障检测和诊断（FDD）方法。在现有作品中，要对来自AHU系统的多尺度监视信号实现高诊断性能具有挑战性，因为这些作品中的特征提取方法不够强大。尽管已经采用单尺度卷积神经网络（CNN）来提高FDD中特征提取的能力，但是仍然难以仅使用单尺度内核从多尺度监视信号中提取强大的判别特征。在本文中，提出了一种新的基于MCNNs的FDD方法，该方法具有三个不同尺度的内核，以提高特征提取的能力，并采用了端到端学习策略来优化MCNNs的模型。该方法具有较强的表示能力，可以捕捉到具有较高判别力的特征，有助于提高AHU的诊断性能。使用我们的AHU实验平台上的测量数据，将提出的方法与其他五种常用方法进行了比较。比较结果表明，所提出的基于MCNNs的FDD方法优于其他FDD方法。使用我们的AHU实验平台上的测量数据，将提出的方法与其他五种常用方法进行了比较。比较结果表明，所提出的基于MCNNs的FDD方法优于其他FDD方法。使用我们的AHU实验平台上的测量数据，将提出的方法与其他五种常用方法进行了比较。比较结果表明，所提出的基于MCNNs的FDD方法优于其他FDD方法。