Application of robust statistical models to measure data quality for improved use of sensors and diagnostics in an active mine setting

我们建议研究新的统计分析方法，以改善工业传感器中正常和异常行为的预测。能够在广泛的操作 ** 条件下检测细微变化，并衡量其对测量的潜在个体和复合影响，对于在具有挑战性的条件下部署传感设备的公司具有重要的潜在价值。在许多工业环境中进行反应式传感器系统维护可能非常困难，因为它可能需要关闭或暂停操作。因此，仍然使用受损传感器数据的机会是重要的，因为如果在常规操作例程中出现机会，则 ** 是预测何时应该进行预防性维护的机会。然而，自信地处理受损数据需要对整个系统有深入的理解，包括通常认为与 ** 感知模式无关的变量。MineSense Technologies Ltd.是一家B.C.-基于采矿技术的公司，专门从事矿石在采矿的各个阶段的多元 ** 遥测。他们的客户是希望通过预浓缩和分选开采的矿石以及剔除废石来优化其运营的矿业公司。实时感知开采矿石的算法 ** 开发是一项复杂的任务，需要与矿山工作人员进行密切讨论并共享 ** 数据和知识。以 ** 统计模式和评估为基础部署机器学习方法将是该公司可以利用的另一种方法。利用统计学术专家的 ** 经验将加快以下进程：（1）整合正在收集的各类 ** 数据;（2）探索高级统计研究可能带来的机会。该项目的主要目标是：（1）确定系统数据质量的特征并对其进行监测;（2）为数据质量模型奠定 ** 科学和技术基础;（3）制定方法，将这些模型 ** 纳入更广泛的系统健康监测。研究结果有可能提供重要的

We propose to investigate novel approaches of statistical analysis to improve upon predictions of normal and**abnormal behavior in industrial sensors. The ability to detect subtle changes within a wide range of operational**conditions and gauge their potential individual and compounding influence on measurements has significant**potential value for companies deploying sensing devices in challenging conditions. It can be very difficult to**perform reactive sensor system maintenance in many industrial settings because it may require operational shut**down or suspension. Accordingly, the opportunity to still work with compromised sensor data is significant, as**is the opportunity to predict when preventative maintenance should be undertaken if an opportunity presents**itself within regular operational routines. Working confidently with compromised data, however, requires deep**understanding of an entire system, including variables perhaps not normally considered as relevant for the**sensing mode(s).**MineSense Technologies Ltd. is a B.C.-based mining technology company specializing in multivariate**telemetry of ore at all stages of mining. Their clients are mining companies that want to optimize their**operations through pre-concentration and sorting of mined ore as well as the rejection of waste rock. Algorithm**development for real-time sensing of mined ore is a complex task that requires intimate discussion and sharing**of data and knowledge with mine staff. Deployment of machine learning approaches with foundations in**statistical patterns and assessment would be another approach that the company could leverage. Harnessing the**experience of statistical academic experts would accelerate the process of (1) integrating the various types of**data being collected and (2) exploring opportunities that advanced statistical research could bring.**The main objectives of this project are to (1) characterize and monitor system data quality, (2) develop the**scientific and technical basis for data quality models, and (3) develop approaches to integrate these models**within broader system health monitoring. Results of the research have the potential to provide significant