基于混合高斯隐马尔可夫模型的带式输送机堆煤时刻预测方法

提出了一种基于混合高斯隐马尔可夫模型的带式输送机堆煤时刻预测方法。该方法根据传感器采集的带式输送机功率时序数据建立带式输送机运行状态的混合高斯隐马尔可夫模型,基于该模型采用基于图的状态序列遍历算法和基于切普曼-柯尔莫哥罗夫方程的概率转移算法对带式输送机堆煤时刻进行预测:基于图的状态序列遍历算法通过寻找当前状态到堆煤状态的通路确定剩余时间;基于切普曼-柯尔莫哥罗夫方程的概率转移算法通过粒子群优化算法及切普曼-柯尔莫哥罗夫方程交叉验证来获取训练样本上失败状态的概率阈值,并计算当前的状态迁移到超过失败状态概率阈值的转移次数来确定剩余时间。基于煤矿生产实际数据集的实验验证了该方法可有效预测带式输送机的堆煤发生时刻。

A prediction method of coal piling time for belt conveyor based on mixture of Gaussian and hidden Markov model

A prediction method of coal piling time based on mixture of gaussian and hidden Markov model(MG-HMM)was proposed.In the method,MG-HMM models of running state of belt conveyor are built according to power time series collected by sensors.Based on the models,two algorithms are raised up to predict coal piling time of belt conveyor:graph based path traversal algorithm is used to estimate remaining useful life by finding a connection path from current state to pile coal state,and probability transition algorithm based on Chapman-Kolmogrov equation is used to predict remaining useful life by counting number of shifting times from current state to the state whose probability is larger thanathreshold.The threshold is determined by particle swarm optimization and Chapman-Kolmogrov equation.Several experiments are carried on benchmark data sets and mine production data.The experimental results demonstrate that the method can effectively predict occurrence time of coal piling.