Driving Unit Predictive Maintenance Using Peak Interval Analysis
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Solution Overview
Problem
Current predictive maintenance methods for driving units are inadequate in detecting abnormal conditions in a timely and reliable manner, leading to significant losses due to unexpected downtime and accidents in industrial facilities.
Innovation Solution
A precise predictive maintenance method that collects and analyzes integrated area values and peak intervals of driving periods in both normal and malfunction states, setting alarm limits and gradient values to issue alerts when abnormal conditions are detected, thereby facilitating timely repair or replacement of driving units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive maintenance methods are used, then the system structure remains simple, but the detection precision and reliability of abnormal conditions deteriorate
Solution Approach 1:
The patent segments the driving period into multiple intervals and calculates integrated area values for each interval separately. This segmentation allows for more precise detection of abnormal conditions by analyzing specific portions of the driving cycle rather than treating it as a single unit, thereby improving measurement precision without requiring complex additional hardware
Solution Approach 2:
The patent introduces a new dimension of analysis by calculating integrated area values across different time intervals and comparing them. This transforms the detection approach from single-point measurements to area-based multi-dimensional analysis, enhancing detection precision through cumulative effect analysis while maintaining relatively simple system structure
2Reliability
If traditional maintenance monitoring is used, then the system remains simple to operate, but unexpected downtime and losses increase
Solution Approach 1:
The patent performs preliminary analysis by calculating integrated area values during normal operation and establishing baseline data before malfunctions occur. This preliminary action enables the system to detect early signs of abnormality and trigger maintenance before actual failures happen, thereby improving operational reliability and preventing unexpected downtime
Solution Approach 2:
The patent implements a feedback mechanism where the calculated integrated area values are continuously compared against reference values or thresholds. When deviations exceed predetermined limits, the system provides feedback signals to trigger maintenance actions, ensuring timely response to abnormal conditions and maintaining high operational reliability
3Measurement precision
If detailed analysis of driving information is performed, then detection reliability improves, but the complexity of data processing increases
Solution Approach 1:
The patent extracts specific meaningful features from the driving information by calculating integrated area values over defined intervals. This extraction process isolates the most relevant characteristics for detecting abnormal conditions, improving detection precision while avoiding the need to analyze all raw data, thus reducing data analysis complexity
Solution Approach 2:
The patent transforms raw driving information into a different parameter form (integrated area values) that is more suitable for detecting abnormal conditions. This parameter transformation simplifies the analysis by converting complex time-varying signals into cumulative metrics that are easier to compare and evaluate, thereby improving detection capability while reducing analytical complexity
Data Source
AI summary
The present invention relates to a precise predictive maintenance method for a driving unit and a configuration thereof includes a first base information collecting step S10 of collecting change information of an energy size; a second base information collecting step S20 of connecting a peak interval between a starting point and an ending point; a setting step S30 of setting an alarm gradient value, and a detecting step S40 of detecting the driving unit as an abnormal state.