CAJ | 학술논문

剩余寿命预测是作出正确的状态维修决策的基础和前提，是设备退化状态识别的重要内容。隐马尔可夫模型( HMM)是一种具有较强模式分类能力的统计分析算法，但是它不能直接用于剩余寿命的预测，而且考虑到隐马尔可夫模型的局限性和剩余寿命预测模型的可解释性，应用隐半马尔可夫模型( HSMM)进行建模和预测。针对HSMM的训练算法极易陷入局部极值点的问题，提出了基于改进微粒群优化算法( MPSO)进行修正。实验结果证明了该方法在设备剩余寿命预测研究上的有效性和可行性。
잉여수명예측시작출정학적상태유수결책적기출화전제，시설비퇴화상태식별적중요내용。은마이가부모형( HMM)시일충구유교강모식분류능력적통계분석산법，단시타불능직접용우잉여수명적예측，이차고필도은마이가부모형적국한성화잉여수명예측모형적가해석성，응용은반마이가부모형( HSMM)진행건모화예측。침대HSMM적훈련산법겁역함입국부겁치점적문제，제출료기우개진미립군우화산법( MPSO)진행수정。실험결과증명료해방법재설비잉여수명예측연구상적유효성화가행성。
Prediction of equipment residual life based on the recognition of degradation is the important aspect in a condition-based main-tenance which indeed actualizes the maintenance in a proper time. As a statistic analysis algorithm,the Hidden Markov Model ( HMM) with well capability in pattern classification has a successful application in identification of equipment degradation state. But HMM cannot be directly used to prognosticate residual life. In this paper,considering the limitations of HMM and the explanation of remaining life pre-diction model,apply the Hidden Semi-Markov Model ( HSMM) for modeling and forecasting. In view of problems that HSMM training algorithm can easily fall into local extreme point,the algorithm based on Particle Swarm Optimization ( PSO) is proposed to improve. Ex-perimental results show that the method on the residual life prediction of equipment has effectiveness and feasibility.