Accelerated editing for the load spectrum of harvester operational units using optimal wavelet components

This study aims to improve the efficiency of durability testing, and then verify the harvesting machinery in laboratory scenarios. The accelerated editing was investigated on the load spectrum, according to the operating loads of combine harvester components as an example. The testing efficiency was further improved to reduce the costs for better consistency in the load spectrum loading between field and laboratory bench tests. Different wavelet components were selected to accelerate the editing using traditional wavelet decomposition. The threshold of pseudo-damage ratio was used to filter the wavelet components. The high contribution of damaged segments was identified in the wavelet components using the extreme difference method. Among them, the peak and valley values were extracted from the reconstructed wavelet components. Differences among adjacent extreme values were calculated after reconstruction. The threshold was then applied to discriminate the specific damage segments during extraction. An accelerated editing of the load spectrum was developed using optimal wavelet components. A case study was selected as the operating components of the feeding system in a combine harvester. The torque load was compared with the wavelet decomposition and time-domain damage retention. The results indicated that there was a greater variation in the signal frequency components and statistical features represented by different wavelet components, compared with the traditional acceleration editing of wavelet decomposition. Therefore, the negative impact of acceleration editing was also found in the indiscriminate identification of the damaged segment for each wavelet component. The acceleration editing was potentially improved to exclude the specific high-frequency wavelet components from the damage segment calculation. The wavelet components were then filtered, according to the pseudo-damage ratio between each wavelet component and the original load. There was a more precise and flexible identification of damage segments using extreme differences. Particularly for the boundaries of damage segments, compared with the time-domain window and the envelope damage segment identification. The periodicity of load cycles was achieved within the extracted load segments, in order to further enhance the accuracy of damage segment identification. The accelerated editing with optimized wavelet components was achieved in the more concise acceleration under the condition where the pseudo-damage retention ratios were nearly consistent. Taking the header auger load as an example under 95% and 98% pseudo-damage retention ratios, the acceleration values of the current wavelet decomposition were improved by 11.59 and 15.72 percental points, respectively, compared with the traditional. The acceleration editing with the optimized wavelet components also demonstrated better applicability to the load of drive shafts in the critical operating components of the feeding system. The signal compression ratios increased for the reel, header, and conveyor loads by 6.77, 12.44, and 20.19 percental points, respectively, under the relatively consistent levels of pseudo-damage retention, compared with the traditional. The better-accelerated editing of the load spectrum also exhibited better versatility for the different loads of operational components. The potential applications can also be found for the accelerated testing of structural durability scenarios in industrial field.