Process Independent Condition Monitoring of Hydraulic Systems via Data-Centric Transfer Learning

Abstract

Hydraulic systems are widely used in various applications, including industrial production, off-road mobile machinery, and aerospace. The failure of a component within a hydraulic system can lead to significant damage and maintenance downtime. To prevent such issues, automated condition monitoring techniques are emerging, relying on data-driven methods such as machine learning algorithms. However, most existing solutions depend on system and application-specific data, which can be costly to obtain in real-world scenarios. Despite this, different hydraulic systems often share structural similarities and common physical mechanisms that lead to faults and failures. This raises the question of whether these similarities can be leveraged to develop more efficient data-driven condition monitoring solutions for hydraulic applications. Therefore, this paper focuses on developing an automated condition monitoring solution that can be independently applied across similar hydraulic systems using transfer learning. The systems considered in this study are a hydraulic press and an electro-hydraulic reach truck. For both systems, simulation data is generated, including scenarios of healthy operation and six different fault conditions. A data-centric transfer approach is pursued by applying the domain adaptation algorithm called Transfer Adaptive Boosting (TrAdaBoost). Additionally, cases with varying degrees of fault complexity and different transfer directions are evaluated. The results demonstrate that the proposed framework is suitable for process independent condition monitoring with a reasonable average transfer accuracy of 93.6% for the presence of non-concurrent faults and 85.7% with the presence of concurrent faults, representing a substantial improvement compared to the baseline transfer results, which remain near 50% when domain adaptation is not applied.