The analysis of the behaviour of complex mechanical components in order to identify relevant patterns for health monitoring and diagnostics is a complex task. One example of this complexity is the data workflow that can be generated for analysis purposes when a new prototype of an electro-mechanical actuator (EMA) is being designed and experimented. The most accurate way of getting valuable insights during this analysis is by running tests in a controlled environment. In this regard, a test bench can be used to simulate the operating conditions experienced by the actuator during its operational lifetime in an accelerated manner.
Depending on the number and nature of the parameters to be obtained, the prototype’s functionality under study, and the test frequencies and experiment duration, big data challenges may appear (volume, variety, and velocity). The analysis of such data and the development of adequate data-driven algorithms usually involves computations on massive amounts of data, a flexible and automated data pipeline, repeatable and reliable operations, and a collaborative approach. The use of DataOps techniques on a cloud infrastructure can provide the ideal environment for creating an end-to-end integrated architecture which meets these requirements.
This work describes a data-driven information system developed for an electro mechanical actuator on a test bench. It uses a multivariate statistical process control (SPC) and a linear discriminant analysis (LDA) algorithm for detecting and evaluating the evolution of the actuator’s health. The information system runs an automated data pipeline on a cloud platform with signals obtained on the test bench, leveraging DataOps and machine learning techniques for a flexible and scalable data management.
How to Cite
Big Data, Test Benches, Data Platform, EMA, DataOps
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