Process Health used for sensor deviations
Has anyone used process health for sensor deviations such as weighment. pressure, or thermal?The reason I ask is the opportunity to trigger centerline or CIL activities.
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Yes! Trapping process variables either as a direct input tagname or as a calculated variable. The beauty of using process health as opposed to machine annunciation (alarm) is that it doesn't have to be an absolute max/min. It can be based upon expected process variation and dependent upon product or recipe based. The analytics can also be captured across multiple startups, production runs, shifts, raw material sourcing changes, etc. to bring greater insights to process related quality conformance and fine tuning centerlines.
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Scott,
Were able to deploy quickly across other lines? This has been the challenge I Have seen in he past.
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Terry, there were two areas that we had to overcome in order to expand our deployment. The first was related to what many face when it comes to proving the value of new systems and technologies and embracing the change. We found the barriers to adoption has many faces and in our case, perspectives and expectations within our technical and reliability workforce had to be aligned. Secondly, there exists the existence, availability and readiness of the digital infrastructure - power supply, cell/wifi capabilities, etc.. Our manufacturing lines had maintenance outage planning schedules many weeks out in advance and we had to align these outages to complete necessary infrastructure related installations. Sometimes this schedule didn't align with the Augury sensor installations so in some cases, deploying quickly was as quickly as we could allow ourselves. I belive we deployed the last dozen or so in about the time it took us to do the first 3.
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100% agree with Scott here. Tracking key process variables from sensors including attributes from weights, pressures, temperatures, torques, speeds, levels, tensions, etc. can allow for some great centerline and analytics opportunities. Being able to analyze outputs based on specific conditions (product, machine state, raw material inputs, etc.) leads to even greater insight. That being said, this is where the human element for needing contextual information is critical to gaining valuable insights, as subject matter experts need to apply the necessary conditional assumptions to accurately analyze and draw conclusions.
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