Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of introducing and extending monitoring solutions to improve product quality. Often, no expertise of how much data is required for a certain task (e.g. monitoring) exists. Especially in vital machinery, a trend to exaggerated sensors may be noticed, both in quality and in quantity. This often results in an excessive generation of data, which should be transferred, processed and stored nonetheless. In a previous case study, several sensors have been mounted on a healthy radial fan, which was later artificially damaged. The gathered data was used for modeling (and therefore monitoring) a healthy state. The models were evaluated on a dataset created by using a faulty impeller. This paper focuses on the reduction of this data through downsampling and binning. Different models are created with linear regression and random forest regression and the resulting difference in quality is discussed.

翻译：系统的监测关键组成部分是迈向故障安全的关键步骤。有负担得起的传感器,而且该行业正在采用和扩大监测办法,以提高产品质量。通常没有专门知识说明某项任务(例如监测)需要多少数据(特别是在重要机器中,在质量和数量上都可注意到超大传感器的趋势。这往往造成数据过度生成,数据应当转移、处理和储存。在以前的案例研究中,一些传感器安装在一个健康的放射扇上,后来被人为损坏。所收集的数据被用于制作一个健康状态的模型(因此也用于监测)。模型是在使用错误的插头生成的数据集上评价的。本文侧重于通过下层取样和宾机减少这些数据。不同的模型是线性回归和随机森林回归形成的,并讨论了由此产生的质量差异。