Large data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, failure patterns, machinery health metrics and other useful information that can help maintenance organizations make more-informed business decisions.
Vibration data collection methods and database strategies to maintain large complete data sets will be discussed. The process that allows the post analysis of the data to occur “as if you are at the machine”. The ability to let the data lead you through the analysis process. Apply different data filters, and see the results during the analysis process. Do this same analysis on historical data, while seeking answers to the question of what has changed.
Explore the possibilities of Data analytics through the three types of vibration data collection processes. Periodic data collection, semi continuous data collection and continuously acquired data.
The goal of Large data analytics is the ability to perform analysis in the current data and previous data sets to identify the point at which machine failure began. The Large Data model necessitates the ability to analyze previous data with the same focus as the current data. The focus of the analysis will shift from simply answering the question of when the machine will fail, to the question of what caused or why or how did the machine begin to fail. This effort and process will naturally lead to identifying the real root cause of failure. From this vantage point repairs and corrections can be implemented that will increase the long term reliability of the machine.
“R.A.I.” the Reliability.aiTMChatbot
You can ask "R.A.I." anything about maintenance, reliability, and asset management.