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  本文選題：齒輪箱　切入點：DSP　出處：《中北大學》2011年碩士論文

【摘要】：齒輪箱是機械設(shè)備中廣泛使用的關(guān)鍵部件,對齒輪箱進行實時監(jiān)測和故障診斷對于工業(yè)生產(chǎn)具有重大的經(jīng)濟和安全意義。本文以齒輪箱為研究對象,使用DSP處理器開發(fā)了一種在線監(jiān)測診斷系統(tǒng),并將分層時序記憶算法應(yīng)用于齒輪箱常見故障的診斷識別中,旨在檢測該方法在齒輪箱故障診斷上的使用效果。 嵌入式故障診斷系統(tǒng)基于嵌入式硬件平臺,將信號采集、數(shù)據(jù)處理與特征提取、故障分類識別集成于一體在嵌入式硬件平臺上執(zhí)行,可以實現(xiàn)自動故障診斷和多種通信方式。本系統(tǒng)以TI公司的DSP—TMS320F2812為核心處理器,并集成了采集和通信模塊電路。該系統(tǒng)可以實現(xiàn)模擬量采集、數(shù)字量輸入輸出和轉(zhuǎn)速測量功能,同時也具有以太網(wǎng)通信、GSM網(wǎng)絡(luò)通信和CAN總線通信功能。為了實現(xiàn)在線數(shù)據(jù)處理,系統(tǒng)嵌入了基于DSP的FIR數(shù)字濾波、FFT功率譜、細化譜分析、希爾伯特包絡(luò)和小波分析處理算法。最后,故障診斷的特征提取函數(shù)也被嵌入到了DSP上以實現(xiàn)齒輪箱故障特征量自動提取。 本文介紹了分層時序記憶(HTM)算法的原理和結(jié)構(gòu),并以齒輪箱故障診斷為實驗基礎(chǔ)進行了算法測試。實驗首先將齒輪箱多個測點所提取出來的特征量進行了融合,并轉(zhuǎn)化為位圖格式以滿足HTM的輸入要求,然后設(shè)計了一個HTM區(qū)域來進行各工況下的輸入位圖模式學習。當HTM區(qū)域能夠?qū)Ω鞴r的輸入位圖產(chǎn)生穩(wěn)定的稀疏分布表征后,就計算區(qū)域的條件概率矩陣來實現(xiàn)故障診斷工作。系統(tǒng)使用VC編寫了分層時序記憶的算法程序,結(jié)合前端DSP診斷模塊成為一個完整的故障診斷系統(tǒng)。實驗結(jié)果表明本系統(tǒng)能夠準確的診斷出故障,而且采用分層時序記憶算法使系統(tǒng)具備在線學習、多傳感器融合和實時預(yù)測的優(yōu)點。
[Abstract]:Gearbox is a key component widely used in mechanical equipment. It is of great economic and safety significance for industrial production to monitor and diagnose gearbox in real time.In this paper, a kind of on-line monitoring and diagnosis system is developed by using DSP processor, and the hierarchical sequential memory algorithm is applied to the diagnosis and identification of common faults of the gearbox.The purpose of this paper is to detect the application effect of this method in gearbox fault diagnosis.Embedded fault diagnosis system is based on embedded hardware platform, which integrates signal acquisition, data processing, feature extraction, fault classification and identification on embedded hardware platform. It can realize automatic fault diagnosis and various communication modes.This system takes TI company's DSP-TMS320F2812 as the core processor, and integrates the collection and communication module circuit.The system can realize the functions of analog data acquisition, digital input and output, speed measurement, Ethernet communication, GSM network communication and CAN bus communication.In order to realize on-line data processing, the system embed FIR digital filter power spectrum, thinning spectrum analysis, Hilbert envelope and wavelet analysis algorithm based on DSP.Finally, the feature extraction function of fault diagnosis is embedded into DSP to automatically extract the gearbox fault feature.This paper introduces the principle and structure of hierarchical temporal memory (HTM) algorithm, and tests the algorithm based on gearbox fault diagnosis.In the experiment, the features extracted from several measuring points of the gearbox are fused and converted into bitmap format to meet the input requirements of HTM, and then a HTM region is designed to study the input bitmap mode under various operating conditions.When the HTM region can generate a stable sparse distribution representation of the input bitmap of each condition, the conditional probability matrix of the region is calculated to realize the fault diagnosis.The system uses VC to write the algorithm program of hierarchical sequential memory, combined with front-end DSP diagnosis module to become a complete fault diagnosis system.Experimental results show that the system can accurately diagnose the fault, and the hierarchical sequential memory algorithm makes the system have the advantages of on-line learning, multi-sensor fusion and real-time prediction.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關(guān)碩士學位論文 前2條

1 宋棟;基于嵌入式的柴油機故障診斷系統(tǒng)[D];中北大學;2012年

2 劉敏娜;改進的Elman神經(jīng)網(wǎng)絡(luò)在齒輪箱故障診斷中的應(yīng)用[D];中北大學;2012年

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本文編號：1718366