Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system, leading to enhanced accuracy of maintenance policies and, consequently, increasing the effectiveness of the equipment. In this work, we propose a method for one-step probabilistic multivariate forecasting of time variables based on a Hidden Markov Model with covariates (IO-HMM). These covariates account for the correlation of the predicted variables with their past values and additional process measurements by means of a discrete model and a continuous model. The probabilities of the former are updated using Bayesian principles, while the parameter estimates for the latter are recursively computed through an adaptive algorithm that also admits a Bayesian interpretation. This approach permits the integration of new samples into the estimation of unknown parameters, computationally improving the efficiency of the process. We evaluate the performance of the method using a real data set obtained from a company of a particular sector; however, it is a versatile technique applicable to any other data set. The results show a consistent improvement over a persistence model, which assumes that future values are the same as current values, and more importantly, over univariate versions of our model.

翻译：工业过程会产生大量监测数据，这些数据可用于挖掘系统中隐藏的时间损失，从而提升维护策略的准确性，进而提高设备效能。本文提出了一种基于带协变量的隐马尔可夫模型（IO-HMM）的时间变量单步概率多变量预测方法。这些协变量通过离散模型和连续模型，将预测变量与其历史值及其他过程测量值的相关性纳入考量。前者的概率采用贝叶斯原理进行更新，后者的参数估计则通过一种同样具有贝叶斯解释的自适应算法递归计算。该方法可将新样本整合到未知参数估计中，从计算层面提升处理效率。我们利用某特定行业企业的真实数据集评估了该方法性能——不过该技术具有通用性，可适用于任何其他数据集。结果表明，该方法较之假设未来值与当前值相同的持久性模型有显著改进，更重要的是，其性能优于我们模型的单变量版本。