By Kock Chee Kiong, ARM, ARiMI-CERM.
Operational and maintenance practices are aligned through policies and standard operating procedures developed by individual organization. Adhering to standard operating procedure helps facilities to operate normally and functioning optimally. Out of the normal issues such as breakdown and troubleshooting may not be explicitly identified and addressed in operating procedure. Problems and issues arise from equipment and process will then have to be tackled by experienced personnel or third-party vendor. Since there is a mixture of experiences existed among employees in the organization and manufacturing facilities, the more experienced and senior personnel will be relied upon to solve the problems when facing difficult issues in equipment and process operation.
Intuitive-based approach towards problem solving
Conventional method such as breakdown and failure mode analysis allow engineering team to identify root cause, but the resulting solution is usually retrospective. Generic engineering solutions maybe applied at times after issues and problems are surfaced out. The effectiveness to solve these problems will very much depend on experience of the personnel making such decision and the solution taken.
Engineering methodology has to be improved by moving towards an intuitive -based approach, rather than the conventional way of preventive, predictive maintenance and conditioning monitoring, troubleshooting. Algorithm derived from Big Data Analysis should be used to assess issues and problem with respective to the exiting situation/environment. The results from the analysis enable users to make better informed decision on formulating an effective solution to resolve issues faster.
The key advantage for such an apporach is the ability to resolve underlying issues and tackle the problem real time and in a timely and appropriate sequence. These are critical for engineering and maintenance personnel whose responsibility is to upkeep production and operation activities.
Intuition-based engineering for AI
The maturity of Artificial Intelligence (AI) and robot to have human intuition is still at an early stage, if human still have real concerns on safety when taking a driver-less plane, high speed rail or electric car.
Despite the advancement of neuro science and programming, intuition baes decision will require time to be developed and learnt by machine. AI would be analyzing bulks of data, making decision though computing and processing capabilities and doing the tasks with minimal or without human intervention. However, this process still lacks human intuition-based approach to solve issue.
What do I mean about human based intuition that AI and machine are lacking. A machine can extract and computes more data at a faster speed and would perform a similar task as the most experienced human operator. There would be no difference in executing this task for both and in fact, many would prefer technology and machineries over human. AI and machine can also pick up issues and alarms before linking these issues to incipient problems. If a problem without prior information and past encounters arises, could we trust AI to resolve the problem or you are betting on the human operator with vast experience but without knowledge on the new problem.
In solving technical issues and problems that are not predefined, human would make relation with experience he/she has, think about all possible root cause (qualifying the issues) and outcome (quantifying the approach/methodology in assessment) which is similar to a matrix-based analysis. For AI, it would be the same using the data and information that could be relevant for analysis.
Machines can learn the skillsets in human approach to solve problem in deriving with alternatives to solve the problems while also predicting list of issues to improvise or work on 1) should solutions failed or 2) the problems got out of control.
Working with vast information and skewed information with the input of intuitive assessment. This enables ignorant data to be analyzed for possibility.
Improve decision making by applying rules and factor application (weighted factor) – using fact-based application for decision making.
Developing the Intuitive-based approach
The formulation towards intuitive-based approach to solve engineering related problems would comprise AI and human user, working together in deriving cost-efficient action to be taken together with various stakeholder and third-party vendors. The workload for human operator work would also be reduced by using mobile devices which are programmed with routine maintenance procedure and equipment troubleshooting methodology.