Tag: threshold

Anomaly Detection,Pre-Maintenance,Planning

Posted on by lechuck park

From Claude with some prompting
This image illustrates the concepts of Anomaly Detection, Pre-Maintenance, and Planning in system or equipment management.

Top section:

  1. “Normal Works”: Shows a graph representing normal operational state.
  2. “Threshold Detection”: Depicts the stage where anomalies exceeding a threshold are detected.
  3. “Anomaly Pre-Detection”: Illustrates the stage of detecting anomalies before they reach the threshold.

Bottom section:

  1. “Threshold Detection Anomaly Pre-Detection”: A graph showing both threshold detection and pre-detection of anomalies. It captures anomalies before a real error occurs.
  2. “Pre-Maintenance”: Represents the pre-maintenance stage, where maintenance work is performed after anomalies are detected.
  3. “Maintenance Planning”: Shows the maintenance planning stage, indicating continuous monitoring and scheduled maintenance activities.

The image demonstrates the process of:

  • Detecting anomalies early in normal system operations
  • Implementing pre-maintenance to prevent actual errors
  • Developing systematic maintenance plans

This visual explanation emphasizes the importance of proactive monitoring and maintenance to prevent failures and optimize system performance.

Event & Alarm

Posted on by lechuck park

From DALL-E with some prompting

The image illustrates the progressive stages of detecting alarm events through data analysis. Here’s a summary:

  1. Internal State: It shows a machine with an ‘ON/OFF’ state, indicating whether the equipment is currently operating.
  2. Numeric & Threshold: A numeric value is monitored against a set threshold, which can trigger an alert if exceeded.
  3. Delta (Changes) & Threshold: A representation of an alert triggered by significant changes or deviations in the equipment’s performance, as compared to a predefined threshold.
  4. Time Series & Analysis: This suggests that analyzing time-series data can identify trends and forecast potential issues.
  5. Machine Learning: Depicts the use of machine learning to interpret data and build predictive models.
  6. More Predictive: The final stage shows the use of machine learning insights to anticipate future events, leading to a more sophisticated alarm system.

Overall, the image conveys the evolution of alarm systems from basic monitoring to advanced prediction using machine learning.