Gateway Fault Detection Using Partial Frequency Spectrum Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machinery and equipment diagnosis systems face high processing and storage costs due to the need to collect and process large amounts of sensor data across entire frequency ranges, making it difficult to maintain low data processing costs.
Innovation Solution
A data collection system that collects time-series data from sensors, stores fault models for comparison, and determines a specific frequency range to examine within the data, allowing for abnormality detection using only the extracted partial frequency spectrum, thereby reducing processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data in an entire range of frequencies is collected and processed, then abnormality detection accuracy is improved, but data processing cost increases
Solution Approach 1:
The frequency spectrum is segmented into multiple frequency bands, and only the relevant frequency band containing abnormality information is extracted and processed. This segmentation approach maintains detection accuracy for critical frequencies while reducing overall processing complexity by excluding irrelevant frequency ranges.
Solution Approach 2:
The patent extracts only the necessary partial frequency spectrum containing abnormality information from the complete frequency spectrum. By taking out and processing only the relevant frequency components rather than the entire spectrum, the system achieves effective abnormality detection with reduced processing cost.
2Reliability
If a large amount of sensor data is collected, then detection reliability is improved, but storage cost increases
Solution Approach 1:
The patent extracts only the essential frequency components containing abnormality information from the complete sensor data spectrum. This extraction approach maintains detection reliability by preserving critical diagnostic information while significantly reducing the quantity of data requiring storage.
Solution Approach 2:
Instead of processing and storing the complete frequency spectrum, the patent applies partial action by focusing only on the specific frequency band where abnormality information exists. This partial processing approach maintains sufficient detection reliability while reducing storage requirements.
Data Source
AI summary
A data collection system pertaining to one embodiment of the present invention collects time-series data which is output from sensors installed on equipment which is a monitored object and carries out detecting an abnormality of the equipment. The data collection system stores plural fault models as data for comparison with time-series data and, in a learning process, determines a range to examine within time-series data by comparing the time-series data with each one of the fault models. An abnormality detection process includes extracting a partial frequency spectrum to examine from the frequency spectrum of time-series data, using information on the range to examine within the time-series data determined through the learning process, and carrying out detecting an abnormality of the equipment using the extracted frequency spectrum.