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Prevent Process Inefficiencies with Automated Root Cause Analysis
技术
- 分析与建模 - 机器学习
适用功能
- 维护
用例
- 质量预测分析
服务
- 数据科学服务
挑战
制造商主要依靠现场专家知识进行根本原因分析。当有缺陷的产品被送到实验室进行分析时，这很费力，而且总是在进行验尸。从 IT 和 OT 收集数据的制造商也需要对各种专业人士有全面的了解才能理解它。这不仅耗时，而且效率低下。
解决方案
鉴于工业 4.0 革命，根本原因分析正在得到改进。曾经的验尸分析已成为预测分析。机器学习算法和大数据分析的进步有助于使用自动化方法执行根本原因分析。这些方法基于生产车间的历史实时数据。对于万无一失的技术，算法也接受制造商提供的输入。这是至关重要的，因为传统或机构知识也被考虑在内，并且预测的准确性在下一次得到提高。
数据驱动的诊断模型经过训练以检测任何与正常情况的偏差，以将其声明为异常。机器学习算法捕捉 5Ms 制造对输出质量的影响。算法将临界值添加到每个已识别的原因并相应地生成信号。
- “提醒”用户最关键和对质量影响最大的参数
- “警告”用户对质量有中等影响的参数
- “注意”对质量影响较小的参数的用户
运营影响
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