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

近年来，随着柔性制造系统和智能制造系统的发展，世界范围内的工业正在经历着深刻的变化。这种无人工厂的趋势将在21世纪继续发展。随着这些发展，设备维护的角色也将继续演变为“保证者”或高生产率和高质量的角色。在工厂机械的状态监测领域，振动或声音信号用于测量故障，以检测故障和区分故障种类。当在机器故障的早期阶段或在远离故障部件的位置测量用于诊断的信号时，故障信号的提取和故障的早期检测是困难的，因为故障信号被强烈的噪声污染。为了提高故障检测的灵敏度，尽可能干净地消除声音信号中的噪声是很重要的。为了消除噪音，许多Me…已经提出了更多的方法。如带通滤波、自适应滤波、维纳滤波、卡尔曼滤波等。但在机械故障诊断领域，这些方法并不总是适用于故障信号的提取。当使用计算机对工厂机械进行状态监测时，需要良好的特征参数，通过这些参数可以精确地识别模式。目前还没有一种可以接受的提取优秀特征参数的方法。为了克服这些困难，提出了故障信号提取的新方法：(1)故障信号的提取方法；(2)基于遗传算法和统计信息的故障信号提取方法；(2)利用序贯统计检验从故障频谱中提取故障频域的方法；(2)特征参数的自动生成方法；(1)基于遗传算法的特征参数在时间域中的自重组；(2)基于遗传算法的频域特征参数的自重组；(3)基于小波分析和遗传算法的特征参数自动生成方法机器非稳定运行(3)智能诊断方法“部分线性化神经网络(P.N.N.)”提出了基于粗糙集的知识获取方法，用于齿轮设备的故障诊断和神经网络模糊诊断，并将本文提出的方法应用于滚动轴承、齿轮设备等实际故障诊断中，验证了方法的有效性。较少

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。

陳 鵬: "可変運動条件における機械設備の異常診断,(第1報,遺伝的アルゴリズムとウェーブレット解析による回転機械の異常診断法)"日本機械学会論文集(C編). 65(640). 202-207 (1999)

陈鹏：“变运动条件下机械设备的异常诊断，（第1次报告，利用遗传算法和小波分析的旋转机械的异常诊断方法）”日本机械工程学会会刊（ed.C）65（640）。 202-207（1999）