Asset Operation Recommendations Through Hidden Failure Pattern Discovery
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Solution Overview
Problem
Current systems fail to detect hidden failure patterns and associate events effectively, leading to operational challenges and high costs due to labor shortages and operational knowledge gaps in remote and harsh environments, particularly in the oil and gas industry.
Innovation Solution
A system and method for dynamic failure pattern discovery and event association using time series data analysis, including data capturing, feature extraction, and a learning system for automatic operation recommendation, utilizing a translation module to label and predict future events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual task assignment is used for remote asset failures, then human decision-making can be applied, but operational redundancy increases and time is lost due to manual intervention requirements
Solution Approach 1:
The system performs preliminary analysis by detecting failure patterns, associating events, and generating operation recommendations before human intervention is needed. The pattern discovery module continuously monitors asset data and pre-identifies potential failures, so when a failure occurs, the recommendation is already prepared and can be immediately implemented.
Solution Approach 2:
The system enables self-service by automatically detecting failure patterns, classifying events, and generating operational recommendations without requiring human analysis. The machine learning models and pattern recognition algorithms perform the diagnostic and recommendation functions that would otherwise require human expertise, allowing the system to serve itself in real-time.
2Measurement precision
If hidden failure patterns are not detected, then current systems can operate with simpler detection mechanisms, but failure identification accuracy remains insufficient leading to operational challenges
Solution Approach 1:
The system segments the complex failure detection problem into distinct functional modules: pattern discovery module for detecting failure patterns, event association module for linking patterns to operational events, and operation recommendation module for generating actions. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system transitions from traditional single-dimensional failure detection to multi-dimensional analysis by examining multiple parameters simultaneously (temperature, pressure, flow rate, time) and their relationships. The pattern discovery algorithms analyze failures across different dimensions and time scales, enabling detection of hidden patterns that would be invisible in single-parameter monitoring.
3Reliability
If routine maintenance is performed in remote environments, then asset reliability can be maintained, but labor costs and operational overhead increase significantly
Solution Approach 1:
The system enables preliminary maintenance actions by detecting failure patterns and generating recommendations before actual failures occur. This allows maintenance to be scheduled proactively rather than reactively, reducing the need for emergency repairs in remote locations and enabling planned maintenance during convenient time windows, thereby reducing overall maintenance costs and labor requirements.
Solution Approach 2:
The system implements continuous feedback by monitoring asset performance, detecting deviations from normal operation, and generating maintenance recommendations. This closed-loop feedback mechanism ensures that maintenance actions are based on actual asset condition data rather than fixed schedules, optimizing maintenance timing and reducing unnecessary labor costs in remote environments.
Data Source
AI summary
Example implementations described herein involve, for receipt of time series data from sensors of one or more assets in a system, executing feature extraction on the received time series data; identifying event types from the feature extraction; identifying pairs of event type and a window of the time series data; clustering motifs of the identified pairs to update a motif dictionary; estimating a likelihood of an event from the event types based on one or more discovered motifs from the time series data and an associated time series motif of the event; using a language model to generate associated event descriptions; providing, through a user interface, the estimated likelihood of the event, the associated time series motif, sequencing of the associated time series motif, and a recommendation; and for an acceptance of the recommendation through the user interface, executing the recommendation for the one or more assets in the system.