技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 航天
适用功能
- 维护
用例
- 预测性维护
- 根因分析与诊断
客户
未公开
关于客户
飞机制造商。
挑战
首先,飞机制造商难以通过健康预测来监测飞机系统的健康状况并提供预测性维护见解。其次,飞机制造商需要一种能够提供上下文咨询并调整工作分配以匹配技术人员经验和专业知识的解决方案。
解决方案
SparkPredict 利用尖端机器学习技术构建机器规模的模式识别模型,以监控飞机内的机械系统并预测故障。这些算法的认知特性意味着 SparkPredict 可以部署到任何位置的飞机系统,并且洞察力将适应该特定飞机的独特特征。此外,SparkPredict 可以与诊断数据库、维护记录和人员记录等其他系统集成,以帮助对故障代码进行分类,推荐合适的人员,并以最佳方式安排维护。这将减少飞机必须在地面上花费的时间。
收集的数据
Asset Performance, Asset Status Tracking, Device Diagnostic Status, Fault Detection, Maintenance Records
运营影响
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