Hydraulic piston pump is a vital component of hydraulic transmission system and plays a critical role in some modern industrials. On account of the deficiencies of traditional fault diagnosis in preprocessing of original data and feature extraction, the intelligent methods based on deep learning accomplish the automatic learning of fault information by integrating feature extraction and classification. As a popular deep learning model, convolutional neural network (CNN) has been demonstrated to be potent and effective in image classification. In this research, an improved intelligent method based on CNN with adapting learning rate is constructed for fault diagnosis of a hydraulic piston pump. Firstly, three raw signals are converted into two dimensional time–frequency images by continuous wavelet transform, including vibration signal, pressure signal and sound signal. Secondly, an improved deep CNN model is built with an adaptive learning rate strategy for identifying the different fault types. Moreover, t-distributed stochastic neighbor embedding is employed to visualize the distribution of features learned by the main layers of CNN model. Confusion matrix is used to analyze the classification accuracy of each fault type. Compared with the CNN model without adapting learning rate, the improved model achieves a higher accuracy based on the selected three kinds of signals. Experiments indicate that the improved CNN model can effectively and accurately identify various faults for a hydraulic piston pump.

液压柱塞泵是液压传动系统的重要组成部分，在一些现代工业中起着至关重要的作用。针对传统故障诊断在原始数据预处理和特征提取方面的不足，基于深度学习的智能方法将特征提取和分类相结合，实现故障信息的自动学习。作为一种流行的深度学习模型，卷积神经网络 (CNN) 已被证明在图像分类中非常有效。本研究构建了一种基于CNN的自适应学习率的改进智能方法，用于液压柱塞泵的故障诊断。首先，通过连续小波变换将三个原始信号转换为二维时频图像，包括振动信号、压力信号和声音信号。其次，使用自适应学习率策略构建改进的深度 CNN 模型，用于识别不同的故障类型。而且，t-分布式随机邻居嵌入用于可视化CNN模型主要层学习的特征分布。混淆矩阵用于分析每种故障类型的分类精度。与未调整学习率的CNN模型相比，改进后的模型基于所选择的三种信号实现了更高的准确率。实验表明，改进后的CNN模型能够有效准确地识别液压柱塞泵的各种故障。