Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data from faulty system conditions at training time. Since faults of unknown types can arise during deployment, fault diagnostics in this scenario is an open-set learning problem. Without labels and samples from the possible fault types, the open-set diagnostics problem is typically reformulated as fault detection and fault segmentation tasks. Traditional approaches to these tasks, such as one-class classification and unsupervised clustering, do not typically leverage all the available labeled and unlabeled data in the learning algorithm. As a result, their performance is sub-optimal. In this work, we propose an adapted version of the variational autoencoder (VAE), which leverages all available data at training time and has two new design features: 1) implicit supervision on the latent representation of the healthy conditions and 2) implicit bias in the sampling process. The proposed method induces a compact and informative latent representation, thus enabling good detection and segmentation of previously unseen fault types. In an extensive comparison using two turbofan engine datasets, we demonstrate that the proposed method outperforms other learning strategies and deep learning algorithms, yielding significant performance improvements in fault detection and fault segmentation.

安全关键系统的数据驱动故障诊断通常面临这样的挑战，即在培训时完全没有来自故障系统状况的标记数据。由于部署期间可能会出现未知类型的故障，因此这种情况下的故障诊断是一个开放式的过程。学习问题。如果没有来自可能的故障类型的标签和样本，则开放式诊断问题通常会重新构造为故障检测和故障分段任务。用于这些任务的传统方法（例如，一类分类和无监督的聚类）通常不会利用学习算法中的所有可用标记和未标记数据。结果，它们的性能不是最佳的。在这项工作中，我们提出了一种变体自动编码器（VAE）的改编版本，它利用了训练时的所有可用数据，并具有两个新的设计功能：1）对健康状况的潜在表示进行隐式监督，以及2）采样过程。所提出的方法产生了紧凑而内容丰富的潜在表示，因此，可以很好地检测和分割以前看不见的故障类型。在使用两个涡扇发动机数据集进行的广泛比较中，我们证明了所提出的方法优于其他学习策略和深度学习算法，从而在故障检测和故障分割方面产生了显着的性能改进。