Validating the integrity of single source condition monitoring data

Abstract

Vast amounts of data are generated daily and play an important role in decision-making and performance evaluation. Ill-informed decisions can have costly, negative effects. For this reason, confidence in the data used is important. In the mining industry, bad decisions could lead to equipment failure and production losses, resulting in large financial implications. To minimise these risks, preventative measures are implemented. Condition monitoring can reduce production losses by monitoring the status of equipment and determining maintenance needs. This minimises down-time and keeps the equipment in good operating condition. To optimise the efficiency of this process, the data needs to have high integrity. Methods exist to validate the integrity of data. These methods differ depending on the context and use of the data. In literature, the need to validate the data integrity of single source condition monitoring data was identified. A system was designed to calculate the data integrity of data streams in context to one another. The system was designed generically so it can be applied to any company which follows a described data layout. The system makes use of contextual data to classify each data point as having high or low integrity. Generic temperature and vibration profile data sets were created using historical data from four major component types, namely fridge plants, compressors, fans, and pumps. The system was verified with a clean data set in order to calibrate it. Subsequently, a test data set with manually introduced errors was used to judge the accuracy of the system. The system was implemented on a deep-level mine case study to validate the integrity of the data for eight different components located across six different sites. The system was able to classify data integrity accurately, with some limitations being identified. It was seen that the case study had a recurring problem with data loss and faulty metering equipment. The additional benefits of identifying human error in component configuration and faulty measurement equipment identification were identified. These benefits added value to the system. Future work was recommended to address the limitations of the current system. It was recommended that calculating the data quality of individual data streams, verifying the power consumption measurements, taking transition states into consideration, and calibrating the profile data per component will increase the accuracy of the system. Incorporating the system results into existing condition monitoring systems and implementing a notification system to inform users of results will decrease the time to correct low integrity data. The developed system met all the study objectives identified and succeeded in classifying the data integrity of single source condition monitoring data. The accuracy of the system can be improved by including an existing system which calculates the data quality of the individual data streams. Implementing a notification system which uses the results of the developed system could reduce the number of human errors in component configuration and increase the rate at which faulty equipment is repaired.