Plant Messaging Suppression for Repetitive Asset Alerts
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Solution Overview
Problem
In large electronic monitoring systems, users are overwhelmed by numerous repetitive and spurious messages from asset monitors, which can compromise their ability to focus on more critical events, as existing filtering techniques are ineffective against repetitive signals.
Innovation Solution
Implementing suppression rules within a messaging application that filters out repetitive messages based on criteria such as time intervals, asset instances, event types, and sensor values, allowing users to configure and manage which messages are suppressed, thereby reducing the noise and prioritizing important alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional filtering techniques are used to limit messages, then users can receive fewer irrelevant messages, but users are still overwhelmed by spurious repetitive messages from failed sensors
Solution Approach 1:
The system performs preliminary analysis of message patterns by tracking the frequency and timing of messages from each asset instance. Before presenting messages to users, the system pre-identifies spurious repetitive patterns and applies suppression rules to eliminate these messages in advance, preventing message overload before it occurs.
Solution Approach 2:
The system continuously monitors message patterns from asset instances and uses this feedback to dynamically adjust message suppression. By tracking the frequency of messages over time intervals, the system automatically identifies when an asset instance is generating spurious repetitive messages and adjusts filtering accordingly, creating a self-regulating message control system.
2Loss of information
If all messages from asset monitors are transmitted to users, then users receive complete information, but users become overwhelmed and unable to focus on critical events
Solution Approach 1:
The system extracts and removes spurious repetitive messages from the overall message stream while preserving legitimate critical messages. By identifying patterns characteristic of failed sensors (repetitive messages within short time intervals from the same asset instance), the system selectively removes only the harmful subset of messages, maintaining information quality while reducing quantity.
Solution Approach 2:
The system changes the parameter of message frequency by applying time-based suppression rules. When an asset instance generates messages at a frequency exceeding a threshold within a defined time interval, the system suppresses subsequent messages from that instance, effectively changing the message output parameter from high-frequency to low-frequency while preserving the ability to detect critical events.
3Ease of operation
If suppression rules are applied to filter repetitive messages, then message overload is reduced, but system complexity increases due to multiple filtering criteria
Solution Approach 1:
The system segments the message filtering process into distinct, manageable components: asset instance identification, time interval tracking, message frequency counting, and suppression rule application. Each component handles a specific aspect of message control, making the overall complex system easier to understand, configure, and maintain through modular organization.
Data Source
AI summary
A method of messaging control is implemented in a plant wide monitoring apparatus. A computer based framework infrastructure communicates with monitoring applications throughout the plant that are implemented on computers, monitors and computer-based applications. Sensors are placed on machinery and monitors receive sensor signals and generate event signals in response to defined physical occurrences, such as when vibration in a machine is exceeding a defined limit or a sensor is failing. A messaging application in the framework infrastructure generates messages corresponding to the event signals. The messages are transmitted to users based on send rules and suppression rules, which are user configurable. The send rules identify messages to be sent or not sent to a particular user based on characteristics of event signals. The suppression rules prevent the transmission of a message based on both the content of a particular event signal plus an external factor, such as a prior event signal. Suppression rules may suppress messages based on a repetitive characteristic in a series of event signals occurring within a specified time interval.