殘差膠囊網(wǎng)絡(luò)在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用研究

打開文本圖片集

摘要：針對旋轉(zhuǎn)機(jī)械中的故障診斷需求，在傳統(tǒng)的膠囊網(wǎng)絡(luò)中引入殘差塊和模糊C均值聚類算法，構(gòu)建殘差膠囊網(wǎng)絡(luò)故障診斷模型。在殘差膠囊網(wǎng)絡(luò)的基礎(chǔ)上，引入注意力機(jī)制和G-K動態(tài)路由算法，構(gòu)建注意力膠囊網(wǎng)絡(luò)故障診斷模型。仿真分析表明：兩種模型都能對故障進(jìn)行精準(zhǔn)測試，具有較強(qiáng)的表達(dá)能力和泛化能力。

關(guān)鍵詞：旋轉(zhuǎn)機(jī)械；故障診斷；膠囊網(wǎng)絡(luò)；殘差塊；注意力機(jī)制

中圖分類號：TP277文獻(xiàn)標(biāo)志碼：B文章編號：1671-5276（2024）06-0244-03

Abstract：To meet the fault diagnosis requirements in rotating machinery， a residual capsule network fault diagnosis model is constructed by introducing residual blocks and fuzzy C-means clustering algorithm in traditional capsule networks. On the basis of residual capsule network， attention mechanism and G-K Dynamic routing algorithm are introduced to build a fault diagnosis model of attention capsule network. Simulation analysis shows that both models can accurately test faults and have strong expressive and generalization abilities.

Keywords：rotating machinery; fault diagnosis; capsule network; residual block; attention mechanism

0引言

在工業(yè)4.0時代的背景下，機(jī)械故障診斷的核心目標(biāo)是利用先進(jìn)的技術(shù)手段提高機(jī)械設(shè)備使用壽命，減少由于機(jī)械故障帶來的經(jīng)濟(jì)損失［1］。（剩余3762字）