Electricity + Control March 2018

PLANT MAINTENANCE, TEST + MEASUREMENT

nal interrogation on a frequent basis to interpret the Systems prediction. The last stage of the software capability is the Prescriptive … How do we fold the thousands of tags that describe our assets into a one-dimen- sional health index of actionable data? Remote collection and analysis Remote collection of plant equipment condition data is now a reality with Setpoint Monitoring Sys- tem. The open architecture developed by OSIsoft is inherently secure. Setpoint as a condition mon- itoring system interfaces directly into the OSI PI environment. For plant wide installation the condi- tion monitoring data can be collected in a local PI server correlated with process data and sent via PI Cloud Connected services. Industry 4 Turboma- chinery then retrieves this data to our office serv- ers for remote support and reporting using tools as described through the data flow process. A simi- lar architecture exists for isolated turbomachinery trains, whereby the data is sent directly from the SETPOINT hardware to OSI’s Cloud services. These remote servers bring expertise to your door without the lead times and expenses of trav- el, thereby increasing support availability in situa- tions of unexpected downtime. Conclusion Analysing and assessing rotating machinery such as compressor trains and blowers involves expe- rience and the collection of the correct in-depth data. The ability to correlate this data with exist- ing information coming from the machines control and process information is crucial to the effective- ness and efficiency of such analysis. Distinguish- ing between cause and event relies on comparing these data sets for the same event. Furthermore, historical data indicating signs and progression to the occurrence of such an event should have the resolution in time and deviation to give early warning and possibly predictive action to mitigate catastrophic failure.

These remote servers bring

See diagram on advert on page 41.

expertise to your door without the lead times and expenses of travel, thereby increasing support availability in situations of unexpected downtime.

After measuring and sampling information comes the collection and storage. The high-resolution data and high sampling frequencies required for in depth analysis will saturate your data storage in short time if not managed correctly. There is al- ways a level of filtering and compression involved in industrial historians. It is an old question that has plagued anyone with data and limited storage, what can I ignore without losing sight of the pic- ture? Legacy systems relied on the crossing of finite thresholds and alarm points to trigger data collection with a small buffer prior to the event. The issue was the lack of information between events. Minor process hiccups and their increase in occurrence are often a key indicator to equip- ment degradation in health. The next issue plagu- ing the Industry 4.0 development is the Concept of data cycles. Data pertaining to a specific field of analytics or control is contained at the instru- mentation and automation control layer with little or no transfer of rich data for statistical correlation across the process. The modern architecture offered by real time data servers such as OSI PI allow us to collect data with a sub-millisecond time stamp from multiple data streams if necessary. Furthermore, we can implement custom compression algorithms based on the type of data. For example, vibration analy- sis waveforms can now be collected on parameter deviation outside a tuneable threshold. Next in the journey of the data flow is the layers of analytics. Descriptive, vs diagnostic vs predictive vs prescriptive. Often systems that display data in a format specific to a type of data analysis, such as compressor map or vibration spectrums are de- scribed as diagnostic systems. However, these dis- plays still require human intervention in the form of an expert to analyse and sift through the data. The diagnostics is not done by the system itself. This is the descriptive layer of analytics. The access to raw data and the ability to model that data into various display formats to highlight the information of inter- est in different states of failure and operation. The next step is for the system to have algo- rithms which allow it to predict future degradation in the equipment based on the historical trend of the equipment condition. This still requires exter-

Kegan Smith qualified at University of Johannes- burg (UJ) with Master’s degree in Electrical Engi- neering. He spent two years lecturing at UJ. He has been working for PREI Instrumentation for the past four years as a Rotating Machinery Engineer. Enquiries: Email: Kegan.smith@prei.co.za

Electricity + Control

MARCH 2018

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