用于预测性维护和流程优化的新型深度学习方法
技术
- 分析与建模 - 机器学习
适用功能
- 维护
用例
- 预测性维护
服务
- 数据科学服务
挑战
大多数组织对其流程采用“反应性维护”方法，即在发生故障后对设备进行维修和更换。机器发生故障后的维修成本大约高出 10 倍，更不用说对收入和客户满意度的直接影响。通过“预防性维护”，设备按照预先设定的时间间隔进行维修或更换，以避免故障。虽然这种方法减少了计划外停机时间，但代价高昂，因为这些计划维修发生在设备没有任何问题的情况下。然而，预测性维护的好处是显着的，因此它正成为制造商的首选方法，使组织能够在需要时预见和安排维修和更换，实现设备 100% 的正常运行时间。制造业中传统机器学习的一个挑战是技术需要干净和完整的数据。然而，制造和过程数据可能是稀疏且嘈杂的。
目前，工程师很难访问和解释生产过程数据，他们依靠个人经验和意见来修改工艺参数。这会导致决策的不一致和潜在的次优决策，此外还增加了流程失败的风险，增加了相关的时间和成本。由于改变操作参数及其影响之间的固有时间滞后和惯性，生产线特别难以使用标准技术建模。通过应用相关和创新的深度学习技术来更有效地设计生产流程，也可以显着降低与废料和失败生产相关的成本。
解决方案
Intellegens 开发了一种机器学习工具 Alchemite，它可以在所有可用数据上训练模型，无论多么稀疏或嘈杂。它将所有可用数据汇集在一起，并使用潜在的相关性来准确预测缺失值并生成尽可能完整的模型。
该技术利用现有的流程数据来降低资产维护计划的成本，提高对整个系统的整体了解，优化制造中涉及的生产线和流程。
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