低接触式机器学习助力实现资产绩效管理

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理,现在可能会从数十种程序、资产和维护数据中抽取相关数值,从而优化资产绩效。在本白皮书中,将学习这种插断性技术如何部署精确性故障模式识别,其具有较高的准确性,可以提前预测设备停机月数,并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践,以期增产提盈。

低接触式机器学习助力实现资产绩效管理

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Low-Touch Machine Learning is Fulfilling the Promise of APM

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