深部煤层采煤机关键传动部件混叠故障解耦诊断理论研究

项目名称： 深部煤层采煤机关键传动部件混叠故障解耦诊断理论研究

项目编号： No.51505475

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 机械、仪表工业

项目作者： 李志雄

作者单位： 中国矿业大学

项目金额： 20万元

中文摘要： 群故障研究是机械故障诊断领域的难点。本项目提出深部煤层采煤机关键传动部件混叠故障解耦诊断的学术构想。研究将以准确提取故障源信号为手段，以实现将混叠故障振动信号分解成单一故障子信号集簇为目的，结合故障程度评估模型，构建混叠故障解耦诊断理论。.研究思路为：1.考虑到截割冲击等环境影响，利用非线性度量技术，辨识伪故障；2.考虑到实际信号振动源存在相关性，且以卷积形式混合，提出以Bounded Component Analysis(BCA)为理论基础的相关源分离方法，并在频域中解卷积；3.研究噪声辅助多元经验模式分解的故障瞬时频率变化规律分析方法，以构造故障源参考信号，提出参考约束卷积BCA(Convolutive BCA-R)的混叠故障解耦方法；4.利用流形学习新方法提取解耦信号的固有特征，建立故障程度评估模型。.通过本项目的研究，可望创新出混叠故障解耦诊断方法，为采煤机群故障研究提供理论基础。

中文关键词： 深部煤层；；采煤机；混叠故障；解耦诊断

英文摘要： Harsh working environment in deep coal seam accelerates the wear process of coal cutters. A simple failure in the coal cutter would knock off the whole mining production line for a couple of days. Hence, it is imperative to monitor the machine condition to prevent break-downs. One of the challenging tasks is to diagnose faults that occur simultaneously in the same/different components of a machine. To address this issue, this project aims to propose a novel approach to decouple compound faults and develop new technologies for fault type detection and fault severity identification. .To achieve the research goal, this project adopts the following four phases: (1) In order to identify the fake fault impulse components aroused by cutting coals in deep mining seam, a new approach using nonlinearity measurement will be developed in the first phase. The identification of the occurrence of hybrid faults would be investigated using nonlinearity measurement approach..(2) Then, in the second phase, a novel approach for correlated vibration source separation will be presented. The vibration signals recorded in real word essentially are convolved from different vibration sources and in fact these sources somehow correlate with each other. To solve this problem, a new generation technology will be developed to deal with the correlated source separation issue in this project. The new technology, convolutive bounded component analysis (Convolutive BCA), is capable of separating not only the desired independent sources but also the sources that are dependent/correlated in both component (space) and sample (time) dimensions from real word vibration signals. .(3) Whereafter, a novel convolutive bounded component analysis with reference (Convolutive BCA-R) will be proposed for hybrid faults decoupling, where each vibration source corresponding to a fault type/location in the hybrid faults could be extracted from the sensor observations. This is achieved based on the BCA framework and an intrinsic frequency tracking reference construction algorithm. Herein the challenge is how to construct the reference signal for BCA to decouple the hybrid faults signal. Given typically nonlinear and nonstationary of a machine vibration signal, it is always difficult to capture accurate instantaneous frequency (IF) which depict the fault characteristics. The IF is essential for a meaningful interpretation of reference construction. To that end, a modified multivariate empirical mode decomposition (MEMD) will be employed to exploit the IFs associated with machine faults. Based on the IFs, the characteristics of typical single faults will be fully investigated and an intrinsic frequency tracking reference algorithm will be established. .(4) In the last phase, a manifold learning approach for fault severity assessment will be developed. The intrinsic fault features will be extracted to develop a fault severity indicator for the coal cutters.

英文关键词： Deep Mining Seam;Coal Cutter;Hybrid Faults;Decoupling Diagnosis