本文選題：隱馬爾可夫模型（HMM） + EM算法��； 參考：《華中科技大學》2012年碩士論文

【摘要】：二十世紀以來，隨著工業(yè)生產的不斷發(fā)展和進步，機械系統(tǒng)的可靠性和安全性問題日益突出。及時了解和掌握機械設備在運行過程中的狀態(tài)，整體或局部是正常還是異常，如果機器發(fā)生故障，如何識別故障類型，這些對于生產的監(jiān)控系統(tǒng)來說是至關重要的。隱馬爾可夫模型（HiddenMarkovModel，HMM）是一種基于統(tǒng)計學理論的模式識別方法，已經廣泛應用于語音識別領域�；谡駝有盘柡驼Z音信號的的相似性，將隱馬爾可夫模型（HMM）應用于機械故障診斷中。本文系統(tǒng)地介紹了隱馬爾可夫模型的基本理論并利用旋轉機械故障診斷的例子來說明隱馬爾可夫模型在故障診斷中的應用。HMM的基本理論主要包括HMM的基本元素、基本假設，以及HMM的三個基本問題及其解決方法。并詳細地推導了解決HMM估計問題的前向-后向算法，解決HMM譯碼問題的Viterbi算法。介紹了EM(ExpectationMaximization)算法，在此基礎上，采用組合的方法—多觀測序列概率是各單觀測序列概率的組合，更方便對多觀測序列給出不同的相關性假設，并引入了相關定理，具體地推導出基于多觀測序列的HMM參數重估公式。引入了時間可逆的隱馬爾可夫鏈和可逆的判定定理。高階HMM考慮了狀態(tài)轉移概率及觀測信號的輸出概率這兩個概率和系統(tǒng)歷史狀態(tài)的關聯性，對觀測信號具有有更強的識別能力，本文介紹了高階HMM的定義，以及高階HMM如何等價地轉化為一階HMM，使得一階HMM的理論方法能夠應用于任一高階HMM。最后本文講述了隱馬爾可夫模型在機械故障診斷中的應用技術，，以及在MATLAB環(huán)境下的計算機實現。
[Abstract]:Since the 20th century, with the continuous development and progress of industrial production, the problems of reliability and safety of mechanical system have become increasingly prominent.It is very important to know and master the state of mechanical equipment in the process of operation, whether the whole or part of the machine is normal or abnormal, and how to identify the type of failure if the machine breaks down, which is very important for the monitoring system of production.Hidden Markov Model (HMMM) is a pattern recognition method based on statistical theory and has been widely used in the field of speech recognition.Based on the similarity between vibration signal and speech signal, hidden Markov model (HMMM) is applied to mechanical fault diagnosis.And three basic problems of HMM and their solutions.A forward backward algorithm for HMM estimation and a Viterbi algorithm for HMM decoding are derived in detail.In this paper, EMN expectation maximization algorithm is introduced. On the basis of this, the method of combination is adopted-the probability of multiple observation sequences is the combination of the probabilities of each single observation sequence, it is more convenient to give different correlation hypotheses for multiple observation sequences, and the correlation theorem is introduced.The reestimation formula of HMM parameters based on multiple observation sequences is derived in detail.In this paper, we introduce a time-reversible hidden Markov chain and a decision theorem of invertibility.High order HMM considers the correlation between the state transition probability and the output probability of the observed signal and the historical state of the system. It has a stronger ability to recognize the observed signal. The definition of high order HMM is introduced in this paper.And how the high-order HMM can be equivalent to first-order HMMs, so that the theoretical method of first-order HMM can be applied to any high-order HMMs.Finally, this paper describes the application technology of hidden Markov model in mechanical fault diagnosis, and the computer implementation in MATLAB environment.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：O211.62;TH165.3

【參考文獻】

相關博士學位論文 前1條

1 葉大鵬;基于2D-HMM的旋轉機械故障診斷方法及其應用研究[D];浙江大學;2004年

本文編號：1730965