预测维护是对工厂中关键机器的状况和性能的分析，以减少机器故障的实例。过去，经验丰富的植物运营商可能通过经验，操作声或其他资产行为来预测结果。今天，这是通过多种软件工具和分析类型完成的。
如果您拥有接近生命尽头的主要资产或机器，或者计划外的停机时间对您的生产计划产生很大影响，那么了解预测性维护至关重要。选择它作为植物的解决方案可以通过更多的运行正常运行时间和更快的问题诊断来节省。
为了使工厂的预测维护获得最大的价值，您应该了解以下过程：
1.数据采集。For predictive maintenance, the data collected are usually time series process data such as historian data. Common tags analyzed include current, temperature, pressure, flow, vibration, etc.
2. Data cleansing.为了避免使用无效的数据，您必须找到异常值和缺失值，并使用纠正技术来保存数据。这包括删除异常值，过滤毫无意义的数据以及纠正偏移时间参数。清理数据可减少道路上的问题，并为分析团队可以提供的见解增加价值。
3. Identifying conditional indicators.This involves distinguishing between normal asset operation and various fault types. Examples include healthy motor operational parameters, seal leakages, worn bearings, blocked inlets, or a combination of faults. Methods to identify fault features include time-based and frequency analyses.
4. Training the model.确定健康操作和断层状态后，对模型进行了训练。这是理解故障指标准确性的重要步骤。通过运行多个测试并确保一致性，您可以准确选择数据模型类型。机器学习算法有许多品种。用于构建高级模型的算法的五个家族是：分类，回归，聚类，密度估计和降低尺寸。回归家族最常用于连续数据。
5. Deployment and integration.These steps can occur in three places: On-premises, where your local networks and data systems are stored; at the edge, where data is so high speed you need it as close to the data source as possible to reduce latency; or in the cloud, where your company already has cloud-based systems to serve a network of remote engineers who need to see the data.
6.重新训练模型。重新培训基于实时过程数据，因为新功能或故障会随着时间的推移出现。随着新故障的发生，分析师可以识别问题并寻求与客户和客户团队的分辨率。
These steps encompass the process of predictive maintenance. You will achieve more operational uptime when you can observe the trend of your assets and can see when a machine is going down. As a result, unplanned downtime is turned into planned, effective maintenance. Additionally, when you avoid suddenly shutting down machines, you extend their life by reducing wear and tear.
Fault type identification provides value by getting your machines up and working faster. When you can see the behavior, you can fix the break rather than relying on guesswork. Predictive maintenance also helps you estimate the time of failure, taking away the guessing game and giving you a solid idea of when a machine will fail so you can prepare for maintenance and shutdowns on your own time.
Ultimately, investing in predictive maintenance can pay off for many types of industrial plants with various machine types. To learn more about how predictive maintenance can benefit your plant and prevent downtime, choose an analytics team with proven experience in industrial analytics.
