Abstract Fault prognosis plays a key role in the framework of Condition-Based Maintenance (CBM). Limited by the inherent disadvantages, most traditional intelligent algorithms perform not very well in fault prognosis of hydraulic pumps. In order to improve the prediction accuracy, a novel methodology for fault prognosis of hydraulic pump based on the bispectrum entropy and the deep belief network is proposed in this paper. Firstly, the bispectrum features of vibration signals are analyzed, and a bispectrum entropy method based on energy distribution is proposed to extract the effective feature for prognostics. Then, the Deep Belief Network (DBN) model based on the Restrict Boltzmann Machine (RBM) is proposed as the prognostics model. For the purpose of accurately predicting the trends and the random fluctuations during the performance degradation of the hydraulic pump, the Quantum Particle Swarm Optimization (QPSO) is introduced to search for the optimal value of initial parameters of the network. Finally, analysis of the hydraulic pump degradation experiment demonstrates that the proposed algorithm has a satisfactory prognostics performance and is feasible to meet the requirements of CBM.

中文翻译：

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基于双谱熵和深度置信网络的液压泵故障预测

摘要 故障预测在基于状态的维护 (CBM) 框架中起着关键作用。受限于固有的缺点，大多数传统的智能算法在液压泵故障预测方面表现不佳。为了提高预测精度，本文提出了一种基于双谱熵和深度置信网络的液压泵故障预测新方法。首先分析了振动信号的双谱特征，提出了一种基于能量分布的双谱熵方法来提取预测的有效特征。然后，提出了基于限制玻尔兹曼机（RBM）的深度信念网络（DBN）模型作为预测模型。为了准确预测液压泵性能退化过程中的趋势和随机波动，引入量子粒子群优化（QPSO）来寻找网络初始参数的最优值。最后，对液压泵退化实验的分析表明，该算法具有令人满意的预测性能，满足煤层气的要求是可行的。