Abstract Estimating remaining useful life (RUL) of industrial machinery based on their degradation data is very critical for various industries. Machine learning models are powerful and very popular tools for predicting time to failure of such industrial machinery. However, RUL is ill-defined during healthy operation. This paper proposes to use anomaly monitoring during both RUL estimator training and deployment to tackle with this problem. In this approach, raw sensor data is monitored and when a statistically significant change is detected, it is taken as the degradation onset point and a data-driven RUL estimation model is triggered. Initial results with a simple anomaly detector, suited for non-varying operating conditions, and multiple RUL estimation models showed that the anomaly triggered RUL estimation scheme enhances the estimation accuracy, on in-house simulation and benchmark C-MAPSS turbofan engine degradation data. The scheme can be employed to varying operating conditions with a suitable anomaly detector.

中文翻译：

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异常监测改进了工业机械的剩余使用寿命估计

摘要 基于退化数据估算工业机械的剩余使用寿命（RUL）对于各行各业都非常重要。机器学习模型是用于预测此类工业机械故障时间的强大且非常流行的工具。然而，RUL 在正常运行期间是不明确的。本文建议在 RUL 估计器训练和部署期间使用异常监控来解决这个问题。在这种方法中，原始传感器数据受到监控，当检测到统计上的显着变化时，将其作为退化起始点，并触发数据驱动的 RUL 估计模型。使用简单异常检测器的初始结果，适用于非变化的操作条件，多个 RUL 估计模型表明，在内部模拟和基准 C-MAPSS 涡扇发动机退化数据上，异常触发的 RUL 估计方案提高了估计精度。该方案可用于通过合适的异常检测器改变操作条件。