The maturity of sensor network technologies has facilitated the emergence of an industrial Internet of Things (IIoT), which has collected an increasing volume of data. Converting these data into actionable intelligence for fault diagnosis is key to reducing unscheduled downtime and performance degradation, among other examples. This article formalizes a problem called semantic fault diagnosis- to construct the formal specifications of faults directly from data collected from IIoT-enabled systems. The specifications are written as signal temporal logic formulas, which can be easily interpreted by humans. To tackle the issue of the combinatorial explosion that arises, we propose an algorithm that combines ideas from agenda-based searching and imitation learning to train a policy that searches formulas in a strategic order. Specifically, we formulate the problem as a Markov decision process, which is further solved with a reinforcement learning algorithm. Our algorithm is applied to time-series data collected from an IIoT-enabled iron-making factory. The results show empirically that our proposed algorithm is both scalable to the size of the data set and interpretable, therefore allowing human users to take actions, for example, predictive maintenance.

中文翻译：

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利用工业物联网时间序列数据进行基于时间逻辑的语义故障诊断


传感器网络技术的成熟促进了工业物联网（IIoT）的出现，收集的数据量不断增加。将这些数据转换为可操作的故障诊断情报是减少计划外停机和性能下降等的关​​键。本文形式化了一个称为语义故障诊断的问题 - 直接根据从支持 IIoT 的系统收集的数据构建故障的形式规范。这些规范被写成信号时序逻辑公式，人类可以轻松解释。为了解决出现的组合爆炸问题，我们提出了一种算法，该算法结合了基于议程的搜索和模仿学习的思想，来训练按策略顺序搜索公式的策略。具体来说，我们将问题表述为马尔可夫决策过程，并通过强化学习算法进一步解决。我们的算法应用于从支持工业物联网的炼铁工厂收集的时间序列数据。结果根据经验表明，我们提出的算法既可扩展至数据集的大小，又可解释，因此允许人类用户采取行动，例如预测性维护。