Automated Risk Priority Number Calculation for Process Plant Failures
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Solution Overview
Problem
Existing methods for estimating operational risk in process plants are time-consuming and subjective, relying on cross-functional teams and past experience, making it difficult to frequently assess and prioritize potential failures due to equipment degradation over time.
Innovation Solution
A processor-implemented method and system that receive and preprocess operational data to calculate range limits, identify failure modes, determine severity and detectability, and estimate Risk Priority Numbers (RPN) based on predefined thresholds and correlations, automating the risk assessment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing RPN calculation method using cross-functional teams and subjective judgement is used, then risk assessment can be performed, but it consumes a lot of time and is difficult to perform frequently
Solution Approach 1:
The patent replaces the manual mechanical process of cross-functional team assessments with an automated computational system. The system uses processors to automatically calculate RPN values by receiving operational data, determining occurrence, severity, and detectability ratings, and computing the final RPN score without requiring human teams to manually assess each failure mode, thereby eliminating time consumption while maintaining assessment accuracy.
Solution Approach 2:
The system enables self-service risk assessment by automatically performing the entire RPN calculation process. The automated system independently receives operational data, pre-processes it to exclude anomalies, calculates range limits, identifies failure modes, determines ratings for occurrence/severity/detectability, and generates RPN values without requiring external human intervention, making frequent assessments practical and efficient.
2Reliability
If existing RPN calculation method based on past experience and engineering judgement is used, then risk assessment can be performed, but it relies on subjective judgement which reduces objectivity
Solution Approach 1:
The patent transforms the subjective assessment process into an objective parameter-based calculation system. Instead of relying on human judgement, the system receives operational parameters, pre-processes them to exclude anomalies like sensor inputs and shutdowns, calculates statistical parameters such as range limits and standard deviations, and uses these objective parameters to determine occurrence, severity, and detectability ratings, thereby eliminating subjectivity while maintaining reliability.
3Measurement precision
If manual cross-functional team assessment is used, then comprehensive risk evaluation can be achieved, but it is difficult to update frequently as equipment degrades over time
Solution Approach 1:
The patent enables continuous risk assessment by automatically receiving and processing operational data in real-time or near-real-time. The system continuously pre-processes data to exclude anomalies, calculates updated range limits and statistical parameters, identifies current failure modes, and generates updated RPN values without interruption, allowing frequent assessments that track equipment degradation over time without the constraints of manual team availability.
Solution Approach 2:
The system replaces the manual mechanical assessment process with an automated computational system that can operate continuously. The processor-based system automatically performs data reception, pre-processing, range limit calculation, failure mode identification, and RPN generation without human intervention, enabling high-frequency assessments that capture equipment degradation trends while maintaining precise risk evaluation.
Data Source
AI summary
This disclosure relates generally to a system and method to estimate an operational risk associated with one or more failures in at least one unit of a process plant. There is a continuous stream of operational data of several variables such as temperature, pressure, etc. Detections are defined in terms of acceptable/unacceptable ranges of parameters over a finite period and operating load of the unit. Often, these predefined parameters must be within a specified range based on operating condition of the process plant and when the measured parameters go beyond, a failure is detected. A risk priority number is estimated from number of occurrences of failure mode, average percentage change from dynamic limits with severity and degree of correlation with detectability from operational data and dynamic limits. Herein, operational risk associated with failure modes can be calculated and updated from time to time automatically from the stream of operational data.