Study on Extraction Method of Failure Signal and Automatic Generation Method of Feature Parameters

故障信号提取方法及特征参数自动生成方法研究

• 批准号: 10650148
• 负责人: CHEN Peng
• 金额: $ 1.34万
• 依托单位: Kyushu Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 1999
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650148/
• 关键词: Failure diagnosis; Condition monitoring; Signal processing; Feature parameter; Genetic algorithms; Genetic programming; Neural network; Rough sets; 異常診断; 異常信号; 時間領域; 周波数領域; 自己再組織化

Recently, industry world wide has been experiencing profound changes as the result of the development of flexible and intelligent manufacturing system. This tendency towards unmanned plants will to continue to develop in the 21st century. In line with these developments, the role of plant maintenance will also continue to evolve to one of a "guarantor" or high productivity and quality.In the field of condition monitoring for plant machinery, vibration or sound signal for measured for detection of failures and discrimination of kinds of failure. When the signals for the diagnosis are measured at an early stage of a machine failure or at a distant location from the failure parts, the extraction of failure signal and the early detection of failure are difficult, because the failure signal is strongly contaminated by noise. It is important to cancel the noise from the sound signal as cleanly as possible in order to increase the sensitivity of failure detection. For noise canceling, many me … More thods have been proposed. For example, band pass filter, adaptive filter, Wiener filter, and Kalman filter etc.. But in the field of machinery diagnosis, these methods can not always be applied to failure signal extraction.Furthermore. When using a computer for condition monitoring for plant machinery, excellent feature parameters are necessary, by which patterns can be precisely distinguished. Currently there is not an acceptable method for extracting the excellent feature parameter.For overcoming these difficulties, this study proposes new method as follows.(1) extraction methods of failure signal1) Extraction method of the failure signal from thc signal measured in the abnormal state of a machine using genetic algorithms (GA) and statistical information.2) Extraction method of failure frequency areas from spectrum measured in the abnormal state of a machine by sequential statistical tests.(2) Automatic Generation Method of Feature Parameters1) Self-reorganization of feature parameters in time domain by genetic algorithms2) Self-reorganization of feature parameters in frequency domain by genetic algorithms.3) Automatic generation method of feature parameters by Wavelet analysis and genetic algorithms for diagnosis of machine in unsteady operating conditions(3) Intelligent diagnosis methodThe "Partially-linearized Neural Network (P.N.N.)" and the knowledge acquisition method by rough sets have been proposed, in order to diagnosing failures of a gear equipment and processing ambiguous diagnosis by neural network.The efficiencies of all the methods proposed in this study have been verified by applying them to practical failure diagnosis, such as, rolling bearing, gear equipment etc.. Less

最近，随着灵活和智能制造系统的发展，全球范围内的行业一直在经历深刻的变化。这种无人植物的趋势将在21世纪继续发展。与这些发展相一致，植物维护的作用还将继续发展为“保证人”或高生产率和质量之一。当在机器故障的早期或远离故障零件的遥远位置测量诊断信号时，难以提取故障信号和失败的早期检测，因为故障信号被噪声强烈污染。重要的是要尽可能清晰地从声音信号中取消噪声，以提高故障检测的敏感性。为了取消噪音，许多我……已经提出了更多的th。例如，带通滤波器，自适应过滤器，维纳尔过滤器和卡尔曼滤波器等。但是在机械诊断领域，这些方法不能总是应用于故障信号提取。当使用计算机进行植物机械监控时，需要精确区分模式，因此，实际上没有一种可接受的方法来提取出色的特征参数。对于克服这些困难，本研究提出了以下的新方法。 2）2）通过顺序统计测试从机器的异常状态中测得的频谱的提取频率区域提取方法。（2）特征参数的自动生成方法1）通过遗传算法的特征域中特征参数自组织特征参数的自组织，遗传算法2）特征域中的特征域中的特征域中的特征参数自动化遗传学方法3）在不稳定的操作条件下机器诊断的算法（3）智能诊断方法“部分内部线性化的神经网络（P.N.N.）和通过粗糙集的知识获取方法已提出，以诊断齿轮设备的诊断故障，并通过对所有方法进行诊断。滚动轴承，齿轮设备等。

项目成果

Jinwei SONG: "Failure Diagnosis for Gear Equipment by Rough Sets and Partially-linearized Neural Network"International Conference on Advenced Mechatronics (ICAM '98). 808-813 (1998)

宋金伟：“通过粗糙集和部分线性化神经网络对齿轮设备进行故障诊断”先进机电一体化国际会议（ICAM 98）。

Peng CHEN: "Automatic Generation Method of Optimum Symptom Parameters for Condition Diagnosis of Plant Machinery by Genetic Algorithms"Proc.of First International Symposium on Environmentally Conscious Design and Inverse Manufacturing. 880-885 (1998)

陈鹏：“遗传算法用于植物机械状态诊断的最佳症状参数自动生成方法”第一届国际环保设计与逆向制造研讨会论文集。

千場隆之: "ウェーブレット解析と遺伝的アルゴリズム(GA)による異常診断法(1)"北九州医工学術者協会誌. 9(2). 1-4 (1999)

Takayuki Chiba：“使用小波分析和遗传算法（GA）的异常诊断方法（1）”北九州医学工程师协会杂志9（2）（1999）。

Fang FENG: "SEQUENTIAL EXTRACTION METHOD OF SYMPTOM PARAMETERS IN FREQUENCY DOMAIN FOR FUZZY DIAGNOSIS OF MACHINERY"Proc.of International Conference on Advanced Manufacturing Technology. 929. 934 (1999)

冯芳：“机械模糊诊断频域症状参数顺序提取方法”先进制造技术国际会议论文集。

宋京偉,陳 鵬: "逐次ファジィ・ニューラルネットワークを用いた歯車装置の異常診断法" 日本設備管理学会誌. Vol.10 No.1. 15-20 (1998)

宋经伟，陈鹏：“利用顺序模糊神经网络的齿轮设备异常诊断方法”日本设备管理学会杂志第10卷第15-20期（1998年）。