预测性维护测验中的人工智能

当您参加测验时，就会出现答案和解释。

1. 与预防性维护相比，预测性维护主要解决什么问题？
   • 仅在需要时进行修复，从而避免按固定时间表更换健康部件
   • 它消除了对任何传感器的需要
   • 它使机器运行得更快
   • 它完全消除了对技术人员的需求
2. “剩余使用寿命”(RUL) 的估计值是多少？
   • 一台机器的总成本
   • 组件失效前还剩下多长时间
   • 电机每小时消耗的能量
   • 设备上的传感器数量
3. 在凯斯西储大学轴承故障诊断数据集中收集了哪种信号？
   • Patient electrocardiograms
   • Satellite color channels
   • Vibration acceleration
   • Text sentiment scores
4. 自动编码器通常如何支持 PdM 中的异常检测？
   • 它自动按名称标记每种故障类型
   • 它产生新机器
   • 它取代了振动传感器
   • 它仅针对健康数据进行训练，并将高重建错误标记为异常
5. 所引用的状态监测论文研究了哪种边缘计算方法？
   • Federated autoencoder training using vibration data at devices near the monitored machines
   • Centralizing all raw data in a public image database
   • Training a language model on maintenance manuals only
   • Replacing sensors with a periodic questionnaire
6. How does NIST define a digital twin?
   • A maintenance calendar with no model of equipment
   • A virtual representation of a physical or perceived real-world entity
   • A physical duplicate machine kept in storage
   • A guarantee that a machine will not fail
7. What is the NASA C-MAPSS dataset used to evaluate?
   • Cancer grade classification from biopsy slides
   • Speech recognition in noisy rooms
   • Turbofan engine degradation and remaining-useful-life prediction
   • Global climate averages without time series
8. How should an anomaly alert from a predictive-maintenance model be interpreted?
   • As a guaranteed failure date
   • As an automatic repair prescription
   • As evidence that no human review is needed
   • As a signal for inspection, not proof of a specific fault or failure time