Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
Sensors (Basel). 2023 Jun 2;23(11):5299. doi: 10.3390/s23115299.
Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a "soft fault state", which is a transient state that eventually becomes a permanent fault -open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estimating fault severity to diagnose early-stage faults. The proposed diagnosis method comprised a novelty detection and severity estimation network. The novelty detection part is specially designed to deal with varying operating conditions of industrial applications. First, an autoencoder calculates anomaly scores to detect faults using three-phase currents. If a fault is detected, a fault severity estimation network, wherein long short-term memory and attention mechanisms are integrated, estimates the fault severity based on the time-dependent information of the input. Accordingly, no additional equipment, such as voltage sensors and signal generators, is required. The conducted experiments demonstrated that the proposed method successfully distinguishes seven different soft fault degrees.
电缆对于机器和设备的控制和仪表至关重要。因此，早期诊断电缆故障是防止系统停机和最大程度提高生产力的最有效方法。我们关注的是“软故障状态”，这是一种最终会变成永久性故障（开路和短路）的暂态。然而，在之前的研究中，软故障诊断问题并没有得到足够的重视，无法提供关键信息，如故障严重程度，以支持维护。在本研究中，我们专注于通过估计故障严重程度来解决软故障问题，从而进行早期故障诊断。所提出的诊断方法包括新颖性检测和严重程度估计网络。新颖性检测部分专门设计用于处理工业应用的各种运行条件。首先，自编码器计算异常分数，使用三相电流来检测故障。如果检测到故障，故障严重程度估计网络（其中集成了长短时记忆和注意力机制）会根据输入的时变信息来估计故障严重程度。因此，不需要额外的设备，如电压传感器和信号发生器。进行的实验表明，所提出的方法能够成功区分七种不同的软故障程度。
