An approach to recognizing the working cycle stage with small sample data for energy conservation

Abstract

Energy conservation is an important strategy for low-carbon development. With utilizing stage recognition and control strategies, engineering machinery can achieve energy savings, and reduce carbon emissions. The traditional machine learning approach requires a large amount of data to train models. In addition to many cases where the available data is limited, even if the data volume is sufficient, it often means that a large amount of testing is required, which is difficult for users who are not professionals and may cause further waste, it is necessary to develop methods that can achieve high accuracy even with limited data. This paper proposes an intelligent recognition method based on library for support vector machine (LIBSVM), which uses the relationship between working stages and pressure waveforms of working cycle. Initially, the pressure waveforms are carefully extracted and subjected to a robust filtering process to effectively eliminate noise artifacts. Subsequently, relevant features are extracted from the waveforms, which include important characteristics such as the amplitude and frequency of the pressure waves, then the extracted features are normalized using appropriate techniques. The LIBSVM model is carefully trained using the aforementioned extracted features and label data. Finally, to further enhance the accuracy of the proposed method, an intelligent calibration system is utilized. This system plays a crucial role in ensuring that the results obtained from the machine learning algorithm are effectively calibrated, thereby improving the overall accuracy of the method. A series of experiments have verified the accuracy of the proposed identification method, and the effectiveness of using this method for energy-saving design has been verified through real machine validation.