In the field of electrical machines maintenance, accurate and timely diagnosis plays a crucial role in ensuring reliability and efficiency. Variational autoencoder (VAE) techniques have emerged as a promising tool for fault classification due to their robustness in handling complex data. However, the inherent nondeterministic aspect of the VAE creates a significant challenge as it leads to varying cluster locations for identical health states across different machines. This variability complicates the creation of a standardized applicable diagnostic tool and challenges for the implementation of effective real-time health monitoring and prognostics. Addressing this issue, a novel approach is proposed wherein a desirability function-based term is integrated into the cost function of the VAE. The enhancement achieved by this approach arises from the standardization of classification, guaranteeing that analogous faults are assigned to identical geolocations within a 2-D user-friendly space. This method's efficacy is validated through two separate case studies: one analyzing vibration data from two diverse designs of large existing hydrogenerators, and the other utilizing vibration data sourced from an open-access dataset focused on bearing fault. The findings of both studies show that the model can cluster 97% of similar faults into preset zones, compared with 40% when the desirability term is excluded.

中文翻译：

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基于振动测量和变分自编码器期望函数的故障检测

在电机维护领域，准确、及时的诊断对于确保可靠性和效率起着至关重要的作用。变分自动编码器（VAE）技术因其在处理复杂数据方面的鲁棒性而成为一种有前途的故障分类工具。然而，VAE 固有的不确定性方面带来了重大挑战，因为它导致不同机器上相同健康状态的集群位置不同。这种可变性使标准化适用诊断工具的创建变得复杂，并为实施有效的实时健康监测和预测带来了挑战。为了解决这个问题，提出了一种新方法，其中基于意愿函数的项被集成到 VAE 的成本函数中。这种方法所实现的增强源于分类的标准化，确保将类似的故障分配给二维用户友好空间内的相同地理位置。该方法的有效性通过两个单独的案例研究得到验证：一个分析来自现有大型水轮发电机的两种不同设计的振动数据，另一个利用来自专注于轴承故障的开放访问数据集的振动数据。两项研究的结果表明，该模型可以将 97% 的相似断层聚集到预设区域中，而排除可取性项时，这一比例为 40%。