Abstract
Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.
Highlights
With the rapid development of science and technology and the modern manufacturing industry, the structures and functions of mechanical equipment are gradually shifting toward achieving integration, intelligence, refinement, and comprehensiveness
Aiming at the problem that the gear pump cannot be properly monitored and maintained in time, a mechanical equipment remaining useful life (RUL) prediction method based on the kernel principal component analysis (KPCA) and this paper has higher prediction accuracy than the traditional RUL prediction model based on k-NN
Aiming at the problem that the gear pump cannot be properly monitored and maintained in time, a mechanical equipment RUL prediction method based on the KPCA and just in time learning (JITL) methods is proposed in this paper, which can timely warn the performance degradation process of the gear pump and timely forecast the RUL of the gear pump
Summary
With the rapid development of science and technology and the modern manufacturing industry, the structures and functions of mechanical equipment are gradually shifting toward achieving integration, intelligence, refinement, and comprehensiveness. The methods of signal analysis, monitoring, and fault diagnosis of mechanical equipment are becoming increasingly mature, practical, and specialized. Fault diagnosis methods based on vibration monitoring and analysis have the following main advantages: (1) Under the condition in which early failure of mechanical equipment is not evident, the collected vibration signals can still be analyzed to determine its position, degree, and cause. Sinao et al proposed a new method for vibration-based techniques to detect, monitor, and prevent pump cavitations [4]; (3) different characteristic parameters of mechanical equipment vibration signals have different sensitivities to different types of faults, and they have practical physical relevance. This study proposes a prediction method based on JITL to predict the RUL of mechanical equipment on time and accurately.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
