CAJ | 학술논문

与传统的隐Markov模型（HMM）相比较而言，应用分层隐Markov模型（HHMM）对设备进行状态识别有诸多优点，而且能以概率的形式更为精确地计算识别结果。针对模型参数随着设备状态的增加呈指数倍增这一问题，引入动态贝叶斯网络这一新的方法，由于该方法可以有效地降低模型的计算复杂度并缩短推理时间，所以将HHMM表达为动态贝叶斯网络，利用预处理的振动信号对设备的健康状态进行识别；针对现有状态分类方法的局限性，提出了基于K均值算法和交叉验证方法相结合的状态数优化方法；以齿轮箱全寿命实验为依据，对该模型实现状态识别的基本框架和计算过程进行了研究，研究结果为复杂设备的状态识别提供了新的思路。
여전통적은Markov모형（HMM）상비교이언，응용분층은Markov모형（HHMM）대설비진행상태식별유제다우점，이차능이개솔적형식경위정학지계산식별결과。침대모형삼수수착설비상태적증가정지수배증저일문제，인입동태패협사망락저일신적방법，유우해방법가이유효지강저모형적계산복잡도병축단추리시간，소이장HHMM표체위동태패협사망락，이용예처리적진동신호대설비적건강상태진행식별；침대현유상태분류방법적국한성，제출료기우K균치산법화교차험증방법상결합적상태수우화방법；이치륜상전수명실험위의거，대해모형실현상태식별적기본광가화계산과정진행료연구，연구결과위복잡설비적상태식별제공료신적사로。
HHMM has many advantages for state recognition and more accurately calculates recog- nition results in the form of probability, in comparison with traditional hidden Markov model （HMM）. Model parameters increased exponentially with the increasing equipment state. In view of this, dynamic Bayesian network was introduced, which can effectively reduce the computational com- plexity and decrease the inference time. Accordingly, HHMM was expressed as dynamic Bayesian network, which identified health status by utilizing vibration signals of pretreatment. In order to a- void the limitations of the current state classifications,the optimization of the condition numbers was proposed,on the basis of combination of K--means algorithm and cross--validation. It also investiga- ted the basic framework for HHMM state recognition and calculation process based on full life test for gearbox, which provides a new way for state recognition of complex equipment.