Impact of Sensor Faults on Condition Monitoring of a Hydraulic Actuator

While supervised machine learning is prevalent in prognostics and health management applications, the success of these models is dependent upon being trained on accurate data. Training data collected with faulty sensors can degrade the performance of these models when deployed in an operational environment. This study investigates the impact of faulty data and the robustness of feature extraction methods and tree-based classifiers. This study also provides an open-source software package for injecting faults into time series data. The numerical experiments are performed on an open-source hydraulic actuator data set and demonstrate that certain features are robust to certain types of faults and that more complex models, such as ensemble techniques, are more robust to sensor faults than simple models. This work suggests that more complex models and larger (and possibly redundant) feature sets may be preferred in situations where sensor faults are likely. Furthermore, certain feature extraction techniques may be selected if certain faults are more likely than others.