滚动轴承早期故障粒子群算法优化随机共振诊断法的研究与实验验证,滚动轴承早期故障粒子群算法优化的随机共振诊断新思路,滚动轴承早期故障优化粒子群算法优化的随机共振诊断法针对滚动轴承不同零件早期故障诊断难
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滚动轴承早期故障粒子群算法优化随机共振诊断法的研究与实验验证,滚动轴承早期故障粒子群算法优化的随机共振诊断新思路,滚动轴承早期故障优化粒子群算法优化的随机共振诊断法针对滚动轴承不同零件早期故障诊断难的问题，课题组提出了粒子群算法优化的随机共振的诊断方法。;提出了以信噪比为目标的随机共振诊断法;采用正弦加噪信号的仿真实验验证了粒子群算法优化的随机共振的诊断可行性。实测信号实验结果表明:粒子群算法优化的随机共振对轴承内圈、外圈故障具备直接诊断能力;该研究为滚动轴承不同零件早期故障诊断提供了一种新思路。,核心关键词：滚动轴承早期故障；粒子群算法优化；随机共振诊断法；信噪比；直接诊断能力。关键词用分号分隔：滚动轴承早期故障;粒子群算法优化;随机共振诊断法;信噪比;直接诊断能力。,基于粒子群算法优化的随机共振法：滚动轴承早期故障智能诊断新策略 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401321/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401321/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">滚动轴承早期故障优化粒子群算法优化的随机共振诊断法</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff2">：</span>滚动轴承作为工业设备中常见的关键部件之一<span class="ff2">，</span>其早期故障的准确诊断对于预防设备故障和提</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">高设备可靠性至关重要<span class="ff3">。</span>然而<span class="ff2">，</span>由于滚动轴承结构复杂<span class="ff3">、</span>故障信号微弱等原因<span class="ff2">，</span>导致其早期故障诊断</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">难度较大<span class="ff3">。</span>本文针对滚动轴承不同零件早期故障诊断难的问题<span class="ff2">，</span>提出了一种粒子群算法优化的随机共</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">振诊断方法<span class="ff3">。</span>该方法以信噪比为目标<span class="ff2">，</span>采用正弦加噪信号进行仿真实验验证了粒子群算法优化的随机</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">共振诊断的可行性<span class="ff3">。</span>实测信号实验结果表明<span class="ff2">，</span>粒子群算法优化的随机共振诊断方法对于轴承内圈<span class="ff3">、</span>外</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">圈故障具备直接诊断能力<span class="ff2">，</span>为滚动轴承不同零件早期故障诊断提供了一种新思路<span class="ff3">。</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">关键词<span class="ff2">：</span>滚动轴承<span class="ff2">；</span>早期故障<span class="ff2">；</span>粒子群算法<span class="ff2">；</span>随机共振<span class="ff2">；</span>诊断</div><div class="t m0 x1 h2 y9 ff4 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _0"> </span><span class="ff1">引言</span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">滚动轴承作为工业设备中常见的关键部件之一<span class="ff2">，</span>其在机械运转过程中承受着较大的载荷和转速<span class="ff2">，</span>容易</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">受到磨损<span class="ff3">、</span>损伤等故障的影响<span class="ff3">。</span>因此<span class="ff2">，</span>早期故障的准确诊断对于预防设备故障和提高设备可靠性具有</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">重要意义<span class="ff3">。</span>然而<span class="ff2">，</span>由于滚动轴承结构复杂<span class="ff3">、</span>故障信号微弱等原因<span class="ff2">，</span>导致其早期故障诊断难度较大<span class="ff2">，</span>一</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">直以来都是研究领域的热点之一<span class="ff3">。</span></div><div class="t m0 x1 h2 ye ff4 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _0"> </span><span class="ff1">相关工作</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">在早期故障诊断领域<span class="ff2">，</span>已经有许多方法被提出和应用<span class="ff2">，</span>包括傅里叶变换<span class="ff3">、</span>小波变换<span class="ff3">、</span>时频分析等<span class="ff3">。</span>然</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">而<span class="ff2">，</span>这些方法在处理滚动轴承早期故障时存在一些问题<span class="ff2">，</span>如对故障频率的确定性要求高<span class="ff3">、</span>对信噪比要</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">求高等<span class="ff3">。</span>因此<span class="ff2">，</span>需要寻找一种新的方法来解决这些问题<span class="ff3">。</span></div><div class="t m0 x1 h2 y12 ff4 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _0"> </span><span class="ff1">粒子群算法优化的随机共振诊断方法</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">粒子群算法是一种基于群体智能的优化算法<span class="ff2">，</span>通过模拟鸟群觅食行为<span class="ff2">，</span>寻找最优解<span class="ff3">。</span>本研究将粒子群</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">算法应用于滚动轴承早期故障诊断中<span class="ff2">，</span>通过优化随机共振的诊断方法<span class="ff2">，</span>提高了故障诊断的准确性<span class="ff3">。</span></div><div class="t m0 x1 h2 y15 ff4 fs0 fc0 sc0 ls0 ws0">3.1.<span class="_"> </span><span class="ff1">信噪比为目标的随机共振诊断法</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">在滚动轴承早期故障诊断中<span class="ff2">，</span>信噪比是一个重要的评价指标<span class="ff3">。</span>本方法通过优化随机共振的诊断方法<span class="ff2">，</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">以信噪比为目标<span class="ff2">，</span>提高了故障诊断的准确性<span class="ff3">。</span>具体步骤如下<span class="ff2">：</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">步骤一<span class="ff2">：</span>采集滚动轴承的振动信号<span class="ff3">。</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">步骤二<span class="ff2">：</span>对振动信号进行预处理<span class="ff2">，</span>包括滤波<span class="ff3">、</span>降噪等<span class="ff3">。</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">步骤三<span class="ff2">：</span>利用粒子群算法优化参数<span class="ff2">，</span>寻找最佳的随机共振诊断方法<span class="ff3">。</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">步骤四<span class="ff2">：</span>对优化后的随机共振诊断方法进行仿真实验<span class="ff2">，</span>验证其可行性<span class="ff3">。</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">步骤五<span class="ff2">：</span>通过实测信号实验<span class="ff2">，</span>验证优化后的随机共振诊断方法对于轴承内圈<span class="ff3">、</span>外圈故障的直接诊断能</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">力<span class="ff3">。</span></div><div class="t m0 x1 h2 y1e ff4 fs0 fc0 sc0 ls0 ws0">3.2.<span class="_"> </span><span class="ff1">仿真实验结果分析</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>