Bayesian Network is a frequently-used model for fault detection and diagnosis in industrial processes. In this article, some modifications are made to Population-Based Incremental Learning and the improved algorithm is applied to structure learning of Bayesian networks. A pre-training step with K2 algorithm is added to the Population-Based Incremental Learning process to obtain an initial probability vector. Then, an elitist strategy is introduced into this method, providing a better way to update the probability vector. Individuals generated in every iteration, and elites in history are utilized to update the vector. The nature of this method makes it possible to learn the bayesian network whose structure is partly known, for sometimes we can specify some parts of the structure with prior process knowledge. A benchmark network Alarm and an industrial process are provided for performance evaluation and comparisons. Furthermore, we parallelize the algorithm to make it more efficient to learn Bayesian Networks. The speed of Improved Population-Based Incremental Learning has been improved significantly after parallelization.

贝叶斯网络是工业过程中故障检测和诊断的常用模型。在本文中，对基于人口的增量学习进行了一些修改，并将改进的算法应用于贝叶斯网络的结构学习。将基于K2算法的预训练步骤添加到基于人口的增量学习过程中，以获得初始概率矢量。然后，将精英策略引入该方法，从而提供了一种更新概率矢量的更好方法。在每次迭代中生成的个体以及历史上的精英被用来更新向量。这种方法的性质使得有可能学习其结构部分已知的贝叶斯网络，因为有时我们可以使用先验的过程知识来指定结构的某些部分。提供了基准网络警报和工业过程，用于性能评估和比较。此外，我们将算法并行化，以使其更高效地学习贝叶斯网络。并行化之后，改进的基于人口的增量学习的速度得到了显着提高。