Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique

Peng Zhang,Xinyuan Chen,Li Qing

doi:10.1155/2021/8856835

Abstract

Internal leakage diagnosis in a hydraulic cylinder is a key technique for the maintenance of hydraulic systems. However, it is difficult to diagnose the internal leakage under different low loads. To solve this problem, a novel fault diagnosis method based on the optimization deep belief network (DBN) combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is proposed to treat the collected AE signals. The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. The reconstructed signals are regarded as the input of the optimization DBN, and the particle swarm optimization simulated annealing (PSOSA) algorithm is adopted to identify the four internal leakage levels. The experimental results show that the proposed method exhibits a higher classification accuracy than other methods under different low loads. This result validates the effectiveness and superiority of the proposed approach to realize internal leakage diagnoses under different low loads.

Highlights

Hydraulic systems are widely applied in industry as actuators in hydraulic systems [1,2,3]
This paper proposes a method involving the optimization deep belief network (DBN) in combination with the CEEMDAN technique
No prior knowledge is available to set the number of sensitive intrinsic mode functions (IMFs) for further reconstruction. erefore, an investigation is conducted to determine the number of sensitive IMFs

Summary

Introduction

Hydraulic systems are widely applied in industry as actuators in hydraulic systems [1,2,3]. E existing methods for the fault diagnoses of internal leakage in hydraulic cylinders can be categorized into two types, namely, model-based and data-driven methods. Under different working loads of the hydraulic cylinder, the difference in the raw leakage signals of identical patterns increases, which hinders the fault diagnosis process. In this case, Pearson correlation coefficients can be used to account for the main characteristics that differentiate the fault modes while eliminating the difference caused by the different working conditions [24, 25]. The CEEMDAN combined with the Pearson correlation coefficients is employed to eliminate noise from the raw AE signals for four different internal leakage levels. The denoised signals are directly fed to the optimization DBN by the PSOSA to identify the levels of internal leakage

Theoretical Framework

Experimental Setup

Methods