论文标题
使用机器学习预测性维护
Predictive Maintenance using Machine Learning
论文作者
论文摘要
预测维护(PDM)是一个概念,可以通过通过数据驱动技术预测其失败来有效地管理资产的维护计划。在这些情况下,在一定时间段内收集数据以监视设备状态。目的是找到一些可以帮助预测并最终防止失败的相关性和模式。在没有计划的维护方法的情况下,通常使用制造业的设备。由于某些意外的失败,这种练习经常导致意外停机。在预定的维护中,固定时间间隔后检查制造设备的状况,如果发生任何故障,则更换组件以避免出乎意料的设备停工。另一方面,这导致了机器无功能和进行维护成本的时间增加。行业4.0和智能系统的出现导致人们对预测维护(PDM)策略的重视越来越重,这些策略可以降低停机时间的成本并提高制造设备的可用性(利用率)。 PDM还有可能通过充分利用组件的使用寿命来实现制造业方面的新可持续实践。
Predictive maintenance (PdM) is a concept, which is implemented to effectively manage maintenance plans of the assets by predicting their failures with data driven techniques. In these scenarios, data is collected over a certain period of time to monitor the state of equipment. The objective is to find some correlations and patterns that can help predict and ultimately prevent failures. Equipment in manufacturing industry are often utilized without a planned maintenance approach. Such practise frequently results in unexpected downtime, owing to certain unexpected failures. In scheduled maintenance, the condition of the manufacturing equipment is checked after fixed time interval and if any fault occurs, the component is replaced to avoid unexpected equipment stoppages. On the flip side, this leads to increase in time for which machine is non-functioning and cost of carrying out the maintenance. The emergence of Industry 4.0 and smart systems have led to increasing emphasis on predictive maintenance (PdM) strategies that can reduce the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to bring about new sustainable practices in manufacturing by fully utilizing the useful lives of components.