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![Splunk [IT Service Intelligence] 2019 .conf Videos w/ Slides](https://podcast-api-images.s3.amazonaws.com/corona/show/926958/logo_300x300.png){kind=logo}
Deep Dive: How to use Splunk to protect the environment [Splunk Enterprise, Splunk IT Service Intelligence, Splunk Machine Learning Toolkit, AI/ML]
Can Splunk help eliminate environmental risks and save the company money at the same time? The Alabama Department of Environmental Management (ADEM) sets standards defining the amount of Volatile Organic Compounds (VOCs) that can be safely released. Honda Manufacturing of Alabama (HMA) must adhere to those standards and track our compliance within the parameters set forth by the ADEM. Failure to comply with these standards will result in potentially damaging the environment and large fines. HMA uses a Regenerative Thermal Oven (RTO) to burn off pollutants from our paint department, which is controlled by an Allen Bradley Programmable Logic Controller (PLC). Using this PLC and the sensors built into the RTO, we collect individual chamber temperatures, fan speeds, exhaust temperatures, motor winding temperatures, motor vibrations, and fan vibrations. Using these readings and the Splunk Machine Learning Toolkit we try to predict if the temperature will be within the set range.
Speaker(s)
Charlie Studdard, Machine Data Analytics, Honda Manufacturing of Alabama
Charlie Studdard, Machine Data Analytics, Honda Manufacturing of Alabama
Slides PDF link - https://conf.splunk.com/files/2019/slides/IOT1629.pdf?podcast=1576909575
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By SplunkCan Splunk help eliminate environmental risks and save the company money at the same time? The Alabama Department of Environmental Management (ADEM) sets standards defining the amount of Volatile Organic Compounds (VOCs) that can be safely released. Honda Manufacturing of Alabama (HMA) must adhere to those standards and track our compliance within the parameters set forth by the ADEM. Failure to comply with these standards will result in potentially damaging the environment and large fines. HMA uses a Regenerative Thermal Oven (RTO) to burn off pollutants from our paint department, which is controlled by an Allen Bradley Programmable Logic Controller (PLC). Using this PLC and the sensors built into the RTO, we collect individual chamber temperatures, fan speeds, exhaust temperatures, motor winding temperatures, motor vibrations, and fan vibrations. Using these readings and the Splunk Machine Learning Toolkit we try to predict if the temperature will be within the set range.
Speaker(s)
Charlie Studdard, Machine Data Analytics, Honda Manufacturing of Alabama
Charlie Studdard, Machine Data Analytics, Honda Manufacturing of Alabama
Slides PDF link - https://conf.splunk.com/files/2019/slides/IOT1629.pdf?podcast=1576909575