The introduction of edition 2 IEC 61511 has brought with it a set of new challenges to the process industry. The standard now calls for mandatory stage 4 functional safety assessments (FSA) to be performed periodically in service and to monitor the actual behaviour of the safety system.
In this paper, we present a computational method for analysing a large number of maintenance records using a variety of data mining techniques. Currently many technical authorities (TAs) and asset operators are aware of problems on their plant but struggle to demonstrate the cause, for example “repeat offenders”, or even that a problem exists at all, due to the way data is recorded in plain text.
The methods presented here uses machine learning algorithms to analyse an entire set of maintenance records for a Safety Instrumented System (SIS). The algorithm can detect anomalies in the data and the way that maintenance records have been recorded, allowing for more targeted assessments and better understanding of the actual behaviour of the SIS related devices by plant owners. The subjectivity of the assessor is removed, and the assessment can focus more on records which have anomalies, as opposed to a random sampling method which makes identifying anomalies more difficult. For asset owners this allows more understanding of actual behaviours which can then contribute to a more accurate demand, failure and spurious trip rate associated with the Safety Integrity Level (SIL) level for each Safety Instrumented Function (SIF).
The algorithm automatically highlights areas for investigation in a matter of seconds which crucially reduces the amount of time spent reviewing non-erroneous records. This increases efficiency and supports prioritisation of budgets based on the findings and recommendations. The algorithm also supports automating high-cost, error-prone tasks in which the cumulative effects of inconsistencies and errors in the analysis can adversely impact safety.
The processes detailed in this paper can easily be applied other systems and assessment criteria’s such as IECEx, ATEX where classifying data into distinct categories for trending and analysis purposes can be deemed useful.
As well as the use of machine learning this paper will also discuss a case study done by DNV where a cross-industry process for data mining (CRISP-DM) sprint methodology was used to manage data science projects as an example of how best to unlock the value from maintenance data.Andrew DerbyshireAndrew is a Principal Safety Engineer at DNV specialising in Functional Safety consultancy and independent conformity assessment activities throughout the lifecycle. Andrew is an Incorporated Engineer and a Registered Functional Safety Engineer (RFSE) with the InstMC and a member of several institutions such as the IET, InstMC and SaRS where he provides voluntary services in professional review interviews for prospective IEng/CEng/RFSE candidates and review of candidates CPD record for maintaining their RFSE. Andrew is a registered Chairperson and Assessor for several clients such as Shell, ConocoPhillips and Bluewater providing full lifecycle support from Identification of Hazard and Determination of Risks through to Independent Safety Assessments. He is also a member of the IEC 61508 Association management committee and the current chair and a director of The CASS Scheme which is a non for profit organisation aimed at promoting the correct use of the IEC 61508 group of standards.Chris BellChris is a Chartered Mechanical Engineer with experience in both technical safety and industrial research and development. He holds a PhD in Physics. Currently Chris works with DNV, where he works on the development of new technologies and digital tools for use in the oil & gas sector with a focus on technical safety and asset integrity. More recently Chris has been involved with data mining and the use of machine learning to improve current engineering practices with regards to FSA’s, EX management and Asset Integrity.