Machine Alarm Level Setting Across Changing Operating Conditions
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Solution Overview
Problem
Existing machine diagnostics face challenges in setting accurate alarm levels due to changing operating conditions, leading to unreliable alarms with risks of missing defects or false triggers, particularly because manual methods require skilled personnel and extensive manual work.
Innovation Solution
A method and system for setting alarm levels that define condition indicators from machine kinematic data, record and calculate values during normal operation, divide the data into operating classes, and set alarm levels at class midpoints, using linear interpolation to enhance accuracy and avoid false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to set alarm levels by machine condition monitoring specialists, then alarm levels can be set for different operating conditions, but the process requires highly skilled people and a lot of manual work
Solution Approach 1:
The system automatically determines alarm levels by processing machine condition data and process parameters itself, without requiring external specialists. The automated algorithm divides operating conditions into classes, calculates statistical parameters (mean, standard deviation), and sets alarm levels based on these calculations, making the system self-sufficient in setting appropriate alarm thresholds for varying operating conditions.
2Reliability
If alarm levels are set for changing operating conditions, then defect detection reliability can be improved, but the risk of false alarms increases due to difficulty in setting appropriate levels
Solution Approach 1:
The method segments the continuous range of operating conditions into discrete operating classes based on process parameters. Each class represents a specific operating condition range, and separate alarm levels are determined for each class. This segmentation allows the system to adapt to changing operating conditions while maintaining accurate alarm thresholds specific to each condition, thereby reducing false alarms caused by inappropriate alarm levels during condition transitions.
Solution Approach 2:
The alarm level is dynamically adjusted based on changing operating parameters. For each operating class, the alarm level is calculated using statistical parameters (mean and standard deviation) specific to that class. When the machine transitions between operating classes, the alarm level automatically changes to reflect the new operating conditions, maintaining detection reliability while adapting to parameter variations.
3Ease of operation
If a single alarm level is used for all operating conditions, then the system is simple to operate, but it cannot accurately detect defects across varying operating conditions
Solution Approach 1:
The system provides a universal alarm level determination method that works across all operating conditions through a single automated process. The same algorithm that divides operating classes, calculates statistical parameters, and determines alarm levels applies universally regardless of the specific operating condition. This multi-functional approach handles both simple and complex operating scenarios within one system, maintaining ease of operation while ensuring reliable defect detection across the full range of conditions.
Data Source
AI summary
A method for setting alarm levels for a machine provides defining at least one condition indicator reflecting the condition of the machine with respect to a defect to be monitored of the machine, the at least one condition indicator defined from machine kinematic data, recording measurements of process related parameters during a predetermined period during which the machine is operating normally, calculating at least one condition indicator value for the at least one condition indicator) using machine condition data, determining a graph of the at least one condition indicator value as a function of a first process related parameter chosen from the measured process related parameters, dividing the graph into operating classes, each operating class being representative of different operating conditions of the machine, calculating an alarm level value for each operating class, setting the determined alarm level value at the midpoint of each operating class.