The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.
翻译:在需要作出决定的高风险情况下,如在保健或工业监测设备方面,机器学习的使用迅速增加; 在关键情况下,一种能够对其决策提供有意义解释的模式至关重要; 在工业设施中,设备的及时维修对于确保持续操作以防止资金损失至关重要; 利用机器学习、预测和指令性维修的尝试来预测和防止最终系统故障; 本文引入一种可视化工具,其中包含解释,以显示从预测维护模型获得的信息,并经过时间序列数据培训。