Processing, April 2018

big data analytics projects was the challenge of lowquality data Example quality issues were incomplete data nonstandard data obsolete data and incorrect data Information from one system that completely disagreed with another system was reviewed to see which system contained the most reliable as built or design data Where the integrity of all conflicting systems was suspect field verification or subject matter experts determined which of the conflicting data was correct The project also faced the challenge of acquiring data from numerous systems owners and making the data comparable to that from other systems The project team accepted data from each system owner in a format they could easily provide since the project had no authority over the system owners Due to accepting native format data the project needed to create data loading and standardization applications and queries to standardize data types and formats To allow analysis the project performed semiautomated and manual work to map instrument tags between systems Instrument tag numbers were the most common key field in the data enabling crossreferencing of related data For example the safety requirements specification SRS has a final element compressor motor that shuts down on a vibration initiator The SIS references the output relay tag name for the shutdown not the compressor motor equipment number The motor identifier and relay tag need to be associated to allow comparison between the SRS document and the SIS In addition if there is a vibration monitoring system in place the multiple vibration tags need to be associated to the corresponding trip tag wired to the SIS The system retains the tag mapping work making it a one time process for each plant Resisting the Siren Song of Analytics was the final system design and build challenge This is a human psyche problem rather than a data or technology problem Once the team had loaded almost 10 million records of data from 14 data sources the data comparison and analytics possibilities seemed infinite The temptation to get lost in analysis was high but GET ME A CHECK ALL 58 Processing APRIL 2018 Utilizing computerized data analytics MicroStockHub iStock the project team managed to retain focus on highpriority misalignments meeting the project requirements within budget The project proposal and planning phases identified only the systems and data sources for comparison and the high level issues the plant was experiencing Due to the lack of detailed specifications the project team utilized agile development methods for the system design and implementation phases Agile development allowed for flexible responses to frequent stakeholder interaction in order to keep the end system and its deliverables in line with stakeholder requirements that both changed and matured as the project progressed The team started by building SQL views to identify and report the high risk issues defined during project planning True to the principles of agile development newly identified high value targets of analysis were substituted for lower value ones as the specific tests for each issue were designed and implemented Benefits that were unplanned and of little to no additional cost also resulted as many of the continuity checks built to maximize the quality and completeness of the analysis results turned out to be valuable on their own For example identifying system instrument tags that were in one source but not in another identified tag naming issues between the field instruments and the as built documentation systems In the end more than 100 SQL views were developed by the project for reporting Each provided either identification of specific risk or reliability issues i e primary reports or a continuity check within or between the data sources secondary reports Since the system is dynamic all primary and secondary reports refresh as soon as any base data is reloaded the analytics are fully automatic Safety issues were the main driver for the projects acceptance and funding The following are examples of how the Auditor analyzes data to identify the diverse issues that can create safety risks Smart instrumentation performs self diagnosis and sends a specific mA output signal when the selfdiagnosis identifies a bad state The SIS configuration must recognize this mA signal as indicating a bad Utilizing computerized data analytics is absolutely necessary to effectively mine the big data found in a typical process facility In analyzing the almost 10 million records of raw data this project produced 184803 records of analysis results concentrating on more than 100 lines of investigation the costs to achieve this manually would be prohibitive The project identified specific potential contributors to both dangerous and nuisance failures enabling the process facility to act to reduce the occurrence of both spurious trips and incidents harming people environment and facility by Proudly manufactured in the USA by www checkall com 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