OPAX Method Based on Pre-denoising MMV Compressed Sensing

To reduce the impact of interference noise on the analysis of Operational-X Transfer Path and the data collection volume and as also to enhance OPAX analytical precision, this paper presents a method based on pre-donosing Multiple Measurement Vectors (MMV) compressive sensing for OPAX. Firstly, the sample data adapted the learning dictionary undergoes pre-denoising treatment. Secondly, a MMV compressed sensing framework is developed to the collection of data from excavators, in terms of using a wheeled hydraulic excavator, the vibration acceleration signals are collected from both ends of the active and passive under stationary steady-state conditions (1200 r/min). In addition, vibration acceleration signals were recorded from the response points within the cabin, as well as the frequency response function of the entire vehicle system. Nevertheless, a transfer path analysis model was established, by tracing the pathway from the powertrain to the seat rails and steering wheel. Finally, to compare and verify the computed values before and after the enhancement of OPAX, the vibration acceleration values along the Z-axis of the steering wheel served as a reference. The results indicate that the data collection volume which are required for the improvement of OPAX method was only the half of sampling data of the traditional OPAX method. Furthermore, the computed values from the enhanced OPAX model are significantly closer to the measured values, and the peak frequency relative error was maintained within 10%. While reducing the overall amount of data collected, the application of pre-denoising MMV compressed sensing effectively improved the analytical precision of the OPAX method.