View source for 18285386. ABNORMALITY DIAGNOSIS DEVICE AND ABNORMALITY DIAGNOSIS METHOD simplified abstract (Mitsubishi Electric Corporation)

=ABNORMALITY DIAGNOSIS DEVICE AND ABNORMALITY DIAGNOSIS METHOD=


==Organization Name==

[[:Category:Mitsubishi Electric Corporation|Mitsubishi Electric Corporation]]


[[Category:Mitsubishi Electric Corporation]]

==Inventor(s)==

[[:Category:Hiroshi Inoue of Tokyo (JP)|Hiroshi Inoue of Tokyo (JP)]][[Category:Hiroshi Inoue of Tokyo (JP)]]

[[:Category:Ken Hirakida of Tokyo (JP)|Ken Hirakida of Tokyo (JP)]][[Category:Ken Hirakida of Tokyo (JP)]]

[[:Category:Makoto Kanemaru of Tokyo (JP)|Makoto Kanemaru of Tokyo (JP)]][[Category:Makoto Kanemaru of Tokyo (JP)]]

[[:Category:Takaharu Nakamura of Tokyo (JP)|Takaharu Nakamura of Tokyo (JP)]][[Category:Takaharu Nakamura of Tokyo (JP)]]

==ABNORMALITY DIAGNOSIS DEVICE AND ABNORMALITY DIAGNOSIS METHOD - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18285386 titled 'ABNORMALITY DIAGNOSIS DEVICE AND ABNORMALITY DIAGNOSIS METHOD

==Simplified Explanation==

The patent application describes an abnormality diagnosis device that analyzes time-series data to detect abnormalities in a compressor and electric motor system. Here are the key points of the innovation:

* Feature quantity calculation unit calculates multiple feature quantities from current values.
* Operation mode determination unit decides the compressor's operation mode based on load torque and drive frequency.
* Feature quantity distribution generation unit creates a distribution from the feature quantities.
* Reference region generation unit establishes a reference region from the feature quantity distribution in normal conditions.
* Determination unit compares the feature quantity distribution during abnormality diagnosis with the reference region to identify abnormalities in the compressor and electric motor.

== Potential Applications ==
The technology can be applied in various industries where compressors and electric motors are used, such as manufacturing, HVAC systems, and automotive.

== Problems Solved ==
1. Early detection of abnormalities in compressor and motor systems.
2. Preventive maintenance to avoid costly breakdowns and downtime.

== Benefits ==
1. Increased operational efficiency.
2. Reduced maintenance costs.
3. Enhanced equipment reliability.

== Potential Commercial Applications ==
"Predictive Maintenance System for Compressors and Electric Motors"

== Possible Prior Art ==
Prior art in this field may include similar systems for diagnosing abnormalities in industrial equipment, such as vibration analysis tools and condition monitoring devices.

=== Unanswered Questions ===

=== How does this technology impact energy consumption in industrial settings? ===
The technology could potentially optimize energy usage by detecting abnormalities early and preventing inefficient operation.

=== What are the scalability considerations for implementing this system in large industrial facilities? ===
The scalability of the system in terms of data processing and integration with existing equipment would need to be addressed for widespread adoption in large facilities.


==Original Abstract Submitted==

An abnormality diagnosis device includes: a feature quantity calculation unit which calculates a plurality of feature quantities from the time-series data of the current values; an operation mode determination unit which determines an operation mode of a compressor on the basis of the load torque and the drive frequency; a feature quantity distribution generation unit which generates a feature quantity distribution from values of the plurality of feature quantities; a reference region generation unit which generates a reference region on the basis of the feature quantity distribution that is obtained in a normal case; and a determination unit which compares the feature quantity distribution that is obtained during an abnormality diagnosis and the reference region corresponding to the operation mode that is applied during the abnormality diagnosis, to determine whether an abnormality is present or absent in either of the compressor and the electric motor.



[[Category:H02P29/024]]
[[Category:F25B49/00]]