Remaining Useful Lifetime for New and Used Technical Systems under Non-Stationary Conditions

非静止条件下新旧技术系统的剩余使用寿命

• 批准号: 451737409
• 负责人: Professor Dr. Eyke Hüllermeier
• 金额: --
• 依托单位: Lehrstuhl für Dynamik und Mechatronik (LDM)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/451737409?language=en
• 关键词: Remaining; Useful; Lifetime; New; Used

Condition-based maintenance and predictive maintenance are increasingly applied in the industry due to their ability of ensuring an optimum utilization of the monitored system. These maintenance strategies allow for diagnosing and predicting the health states of the system under stationary operating conditions. However, technical systems mostly operate under non-stationary conditions, e.g. a wind turbine affected by different loads and speeds due to stochastic wind excitation. Non-stationary conditions lead to changed sensor data and thereby mask alterations caused by either faults or degradation of the system. Therefore, condition monitoring methods need to be extended and adapted for systems operating under non-stationary conditions.The proposed project aims to develop methods for remaining useful lifetime prediction for systems operated under non-stationary conditions. Therefore, classical data-driven and model-based approaches from engineering are combined with approaches from the field of artificial intelligence. By a hybrid combination of clustering and classification with knowledge-based approaches, operating conditions are categorized and failure modes are identified. Based on uncertainty quantification and analyzed relationships between the operating conditions, the sensor data und the degradation evolution, suitable features for enabling the prediction of the remaining useful lifetime are developed and evaluated. Embedding non-stationary future operating conditions is realized by the use of different machine learning methods such as learning on data streams. These methods enable incremental learning and adaption to changes like variation of operating conditions. Moreover, hybrid methods are developed to allow a prediction of the remaining useful lifetime for used systems that are retrofitted with suitable sensors but lack sensor data of their past operation.For validation of the methods for remaining useful lifetime predictions, three application examples are chosen which have been selected from various thematic fields. To generate data for the first example, a suitable ball bearing test rig needs to be developed and constructed. The test rig should allow varying operating conditions regarding speed and bearing load. Run-to-failure data is acquired by different sensors such as acceleration sensors, temperature sensors, and strain gauges. For the second example, a laboratory experiment based on piezoelectric transducers is also implemented, whose failure is characterized by cracks and should be monitored. The third example is based on simulated data of a turbofan engine whose degradation under six conditions has been detected by various sensors.

基于状态的维护和预测性维护在工业中的应用越来越多，因为它们能够确保被监测系统的最佳利用。这些维护策略允许诊断和预测系统在固定运行条件下的健康状态。然而，技术系统大多在非平稳条件下运行，例如，由于随机风激励，风力涡轮机受到不同负载和速度的影响。非平稳条件会导致传感器数据发生变化，从而导致因系统故障或降级而导致的掩模改变。因此，状态监测方法需要扩展和适应于非平稳条件下运行的系统。本项目旨在开发非平稳条件下运行的系统剩余有效寿命预测方法。因此，工程学中的经典数据驱动和基于模型的方法与人工智能领域的方法相结合。通过将聚类和分类与基于知识的方法相结合，对运行状态进行分类，并识别故障模式。基于不确定性量化和分析运行条件、传感器数据和退化演化之间的关系，开发和评估了能够预测剩余使用寿命的合适特征。嵌入非平稳的未来运行条件是通过使用不同的机器学习方法来实现的，例如对数据流进行学习。这些方法使增量学习和适应变化，如运行条件的变化。此外，还开发了混合方法，用于预测已改装了合适传感器但缺乏过去运行的传感器数据的旧系统的剩余使用寿命。为了验证剩余使用寿命预测方法的有效性，从不同的主题领域选择了三个应用实例。为了产生第一个例子的数据，需要开发和建造一个合适的滚珠轴承试验台。试验台应允许有关速度和轴承负荷的不同运行条件。从运行到故障的数据由不同的传感器获取，如加速度传感器、温度传感器和应变计。对于第二个例子，还进行了基于压电式换能器的实验室实验，其故障以裂纹为特征，应进行监测。第三个例子是基于涡扇发动机的模拟数据，该发动机在六种条件下的退化已经被各种传感器检测到。