Predictive maintenance (PdM) has become an important industrial feature. Existing methods mainly focus on remaining useful life (RUL) regression or anomaly detection to achieve PdM in a given application. Those approaches assume monotonic degradation processes leading to a single catastrophic failure at the system's end of lifetime. In contrast, much more complex degradation processes can be found in real-world applications, which are characterized by effects like self-healing or noncatastrophic anomalies. A important example of devices with complex degradation are electromechanical relays. As established PdM solutions failed when applied to a real-world relays degradation data set, the maintenance algorithm for unlabeled data (MAUD) is presented to detect signs of wear and enable a service in time. In detail, MAUD is based on an artificial neural network (ANN), which is trained semisupervised. Experiments with measurement data from 546 relays show that MAUD is superior to various existing methods: The static B10 threshold, which represents the state of the art in relay maintenance, is surpassed by a 17.07 p.p. increase in utilization while reducing failures by 6.42 p.p. Methods based on machine learning, such as RUL estimation and anomaly detection, achieved much lower utilization (up to 31.83 p.p.) compared with MAUD while maintaining the same failure rate.

中文翻译：

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用于继电器预测维护的伪标签机器学习算法

预测性维护（PdM）已成为一个重要的工业特征。现有方法主要侧重于剩余使用寿命 (RUL) 回归或异常检测，以在给定应用中实现 PdM。这些方法假设单调退化过程会导致系统寿命结束时发生单一灾难性故障。相比之下，在现实应用中可以发现更复杂的退化过程，其特征是自我修复或非灾难性异常等效应。具有复杂退化的设备的一个重要例子是机电继电器。由于现有的 PdM 解决方案在应用于现实世界的继电器退化数据集时失败，因此提出了未标记数据 (MAUD) 的维护算法来检测磨损迹象并及时启用服务。具体来说，MAUD 基于人工神经网络 (ANN)，该网络经过半监督训练。对 546 个继电器的测量数据进行的实验表明，MAUD 优于各种现有方法：代表继电器维护最先进水平的静态 B10 阈值的利用率提高了 17.07 pp，同时故障减少了 6.42 pp。在机器学习方面，例如 RUL 估计和异常检测，与 MAUD 相比，在保持相同故障率的情况下实现了更低的利用率（高达 31.83 pp）。