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real time silver vibration data collectors for farming


There are many generic sources from which information can be harvested for identifying your next reliability project. These sources include sensor data, machine data, maintenance reports and more. To provide some insight for your own projects, the following are three examples of how we have leveraged the power of data sources and the insight they can deliver as part of our Maintenance 4.0 efforts.

  • Vibration-based failure data collected from more than 800 industrial sites: By looking at the complete failure history of the entire I-care vibration analysis customer base, we can highlight what are the “generic” improvement areas. We’re able to tailor the findings based on different requests. Are you interested in an analysis for your industry vertical — or for certain equipment types? Our dataset can provide the answers!

  • Customer-specific failure data harvested from a CMMS: Extract information from your computerized maintenance management system (CMMS) database to refine and identify the projects that will provide the most immediate and substantial ROI. Such an effort requires extensive data mining most easily achieved with an automated data mining tool.

Such a tool allows you to identify the bad actors in your technical installation and indicate which are the core problems impacting your performance KPI’s.

Author’s note: CMMS data mining for Industry 4.0 projects is one of I-care’s core areas of expertise. If you would like further input on this specialized effort, give us a call.

  • Asset-Specific Failure Analysis: Identify specific assets that have higher than expected failure or unplanned downtime rates, then take a deep dive into those assets. At I-care we have developed DOFA (Data Oriented Failure Analysis) which will identify which data sources should be used for efficient prediction of different failure modes of the selected equipment. This is an essential step in launching a successful 4.0 project. This approach will be covered soon in the next blog post.

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